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# AI System Changelog

This file tracks durable changes to the repository's AI operating system.

The goal is to make agent self-improvement:

* visible
* auditable
* reversible
* testable

Do not use this file for ordinary application code changes.

Use it for changes to:

```text
AGENTS.md

CLAUDE.md

.ai/AI-OS.md

.ai/WORKFLOW.md

.ai/skills/

.ai/prompts/

.ai/templates/

.ai/state/
```

when those changes alter reusable agent behavior.

---

# Versioning

Use semantic-style versioning where practical.

```text
MAJOR.MINOR.PATCH
```

Examples:

```text
1.0.0
1.0.1
1.1.0
2.0.0
```

---

# PATCH

Use PATCH for:

* wording clarification
* typo correction
* small missing validation
* stronger constraint wording
* broken file reference
* minor procedure correction

Example:

```text
1.0.0 → 1.0.1
```

---

# MINOR

Use MINOR for:

* new skill
* new prompt
* new template
* meaningful workflow improvement
* new verification procedure
* new reusable rule

Example:

```text
1.0.1 → 1.1.0
```

---

# MAJOR

Use MAJOR when changing:

* agent authority
* instruction priority
* self-modification governance
* default execution lifecycle
* approval requirements
* security boundaries
* verification philosophy

Example:

```text
1.4.2 → 2.0.0
```

Major changes should normally be surfaced to the user unless directly requested.

---

# Entry Format

Use this structure:

```text
## [Version] — [Date]

### Changed
- file/path
- short description

### Reason
Why was the change necessary?

### Evidence
What real task, failure, correction, or repeated pattern justified it?

### Expected Improvement
What future behavior should improve?

### Verification
How was the new behavior tested?

### Result
PASS / PARTIAL / FAILED / REVERTED

### Rollback
What should be reverted if the change performs worse?
```

---

# Initial Release

## 1.0.0

### Added

* `AGENTS.md`
* `CLAUDE.md`
* `.ai/AI-OS.md`
* `.ai/WORKFLOW.md`
* `.ai/CHANGELOG.md`
* `.ai/state/LEARNINGS.md`
* `.ai/templates/SKILL_TEMPLATE.md`

Initial general-purpose skills:

* `diagnostic-intake`
* `mirror-audit`
* `socratic-teacher`
* `prompt-architect`
* `prompt-debugger`
* `reverse-engineer`
* `red-team`
* `coffee-chat-editor`
* `skill-extractor`
* `skill-builder`
* `retrospective`
* `system-audit`

Initial reusable prompts:

* `discover.md`
* `plan.md`
* `review.md`
* `retrospective.md`

### Reason

Establish a reusable, self-improving agent operating system based on:

* discovery before action
* audit before commitment
* verification before completion
* adversarial review for consequential work
* reusable learning extraction
* controlled self-modification

### Expected Improvement

Agents should become better at:

* understanding unfamiliar projects
* avoiding premature implementation
* detecting hidden assumptions
* verifying generated work
* learning reusable procedures
* improving their own instructions without uncontrolled prompt growth

### Verification

Initial system should be tested using:

```text
.ai/skills/system-audit/SKILL.md
```

after all baseline files are installed.

### Result

PENDING INITIAL DEPLOYMENT

---

# Changelog Rules

## 1. Log Durable Changes

Log changes that affect future agent behavior.

Examples:

```text
new project deployment skill

new security review rule

improved prompt failure handling

new requirement to run a specific project test
```

---

## 2. Do Not Log Temporary Task Context

Do not add entries such as:

```text
"Changed variable name during today's task."

"Opened controller.asp."

"Ran unit test."
```

Those belong in normal project history or the task conversation.

---

## 3. Link Changes to Evidence

Avoid changelog entries such as:

```text
Improved prompt.
```

Prefer:

```text
Updated prompt-debugger skill after three tasks showed
that failed prompts were being expanded rather than diagnosed.
Added explicit instruction-overload detection and same-input retesting.
```

The changelog should explain why the system evolved.

---

## 4. Record Failed Improvements

Failed self-improvements are useful history.

Example:

```text
## 1.2.1

### Changed
Added mandatory architecture planning for all code changes.

### Evidence
Several larger changes benefited from planning.

### Verification
Tested against three small bug fixes.

### Result
FAILED

The rule created unnecessary overhead for trivial tasks.

### Rollback
Reverted mandatory planning and replaced it with Fast / Standard / High-Risk workflow selection.
```

This helps future agents avoid repeating the same experiment.

---

# Reversion Policy

If a new AI-system change:

```text
increases failures

creates instruction conflicts

causes excessive clarification

adds unnecessary process

reduces verification quality

breaks unrelated workflows
```

then:

```text
REVERT
LOG FAILURE
CAPTURE LESSON
```

Do not preserve a bad self-improvement simply because it was versioned.

---

# Cross-File Versioning

Individual skills may have their own versions.

Example:

```text
.ai/skills/red-team/SKILL.md
Version: 1.2.0
```

The top-level AI-OS may still be:

```text
AI-OS Version: 1.4.0
```

The root changelog records meaningful cross-system changes.

Individual skill changelogs record detailed changes to that skill.

---

# Example PATCH Entry

## 1.0.1 — YYYY-MM-DD

### Changed

`CLAUDE.md`

Added requirement to inspect the resulting diff before finishing substantial edits.

### Reason

Claude completed several tasks with correct functionality but left unrelated formatting changes.

### Evidence

Repeated formatting churn appeared during three unrelated project tasks.

### Expected Improvement

Reduce accidental unrelated modifications.

### Verification

Tested on:

1. targeted bug fix
2. documentation edit
3. unrelated code generation task

### Result

PASS

### Rollback

Remove the new diff-review rule from `CLAUDE.md`.

---

# Example MINOR Entry

## 1.1.0 — YYYY-MM-DD

### Added

```text
.ai/skills/classic-asp-controller/SKILL.md
```

### Reason

Controller creation repeatedly required the same:

* request validation
* service initialization
* error handling
* response pattern

### Evidence

The procedure occurred in four controller implementations.

### Expected Improvement

Reduce duplicated discovery and inconsistent controller structure.

### Verification

Tested against:

1. existing controller reconstruction
2. new test controller

### Result

PASS

---

# Example Prompt Improvement Entry

## 1.1.1 — YYYY-MM-DD

### Changed

```text
.ai/prompts/plan.md
```

### Reason

The planning prompt produced long implementation documents for trivial changes.

### Evidence

Three small fixes generated plans longer than the actual change.

### Expected Improvement

Plans should scale with task complexity.

### Verification

Tested on:

* typo fix
* moderate feature
* database migration

### Result

PASS

---

# Example Skill Revision

## 1.2.0 — YYYY-MM-DD

### Changed

```text
.ai/skills/red-team/SKILL.md
```

### Added

Explicit distinction between:

```text
BLOCKER

IMPORTANT RISK

ACCEPTABLE TRADEOFF

SPECULATIVE CONCERN
```

### Reason

Red-team reviews were generating too many low-value objections.

### Evidence

Repeated reviews treated minor hypothetical concerns as release blockers.

### Expected Improvement

Increase review signal and reduce adversarial noise.

### Verification

Re-ran the skill against two previously reviewed designs.

### Result

PASS

---

# Example Major Change

## 2.0.0 — YYYY-MM-DD

### Changed

Default AI workflow changed from:

```text
Discover → Plan → Implement → Verify
```

to:

```text
Discover → Initial Approach → Mirror Audit → Plan → Implement → Verify
```

### Reason

Agents repeatedly committed to the first plausible architecture without evaluating hidden assumptions.

### Evidence

Multiple architecture tasks required substantial correction after implementation began.

### Expected Improvement

Catch design errors before implementation cost is incurred.

### Verification

Tested on several architecture and migration scenarios.

### Result

PASS

### User Review

REQUIRED / APPROVED

---

# Changelog Quality Test

A useful changelog entry should allow a future agent to understand:

```text
WHAT CHANGED?

WHY?

WHAT EVIDENCE EXISTED?

WHAT WAS EXPECTED TO IMPROVE?

DID IT WORK?

HOW CAN IT BE UNDONE?
```

If those questions cannot be answered, the entry is incomplete.

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# Project Profile

Version: 1.0.0
Status: ACTIVE

## Purpose

This file gives agents a concise, current map of the project.

It should answer:

* what the project is
* how it is structured
* what technologies it uses
* how to build it
* how to test it
* how data is stored
* how it is deployed
* what constraints matter
* which project-specific skills exist

This file is not intended to replace the README, architecture documentation, deployment guides, or detailed technical documentation.

It is an **agent-readable index of project truth**.

---

# 1. Project Summary

## Name

```text
{{PROJECT_NAME}}
```

## Purpose

```text
{{SHORT_DESCRIPTION_OF_WHAT_THE_PROJECT_DOES}}
```

## Primary Users

```text
{{USERS_OR_CONSUMERS}}
```

## Repository Type

Choose one or more:

```text
APPLICATION

SERVICE

LIBRARY

SCRIPT COLLECTION

DATA PIPELINE

INFRASTRUCTURE

DOCUMENTATION

MONOREPO
```

---

# 2. Source of Truth

When this profile conflicts with current project documentation or code:

```text
VERIFY THE CURRENT PROJECT STATE
```

Do not blindly trust stale information in this file.

Important source-of-truth documents:

```text
{{README_PATH}}

{{ARCHITECTURE_DOC}}

{{DEPLOYMENT_DOC}}

{{OTHER_IMPORTANT_DOCS}}
```

---

# 3. Technology

## Primary Languages

```text
{{LANGUAGE}}
{{LANGUAGE}}
```

## Frameworks

```text
{{FRAMEWORK}} — {{VERSION_IF_KNOWN}}
```

## Runtime

```text
{{RUNTIME}}
```

## Operating Environment

```text
{{WINDOWS/LINUX/CONTAINER/etc}}
```

## Web/Application Server

```text
{{IIS/NGINX/APACHE/etc}}
```

---

# 4. Important Directories

```text
{{PATH}}
Purpose: {{PURPOSE}}

{{PATH}}
Purpose: {{PURPOSE}}

{{PATH}}
Purpose: {{PURPOSE}}
```

Example:

```text
/src
Application source

/tests
Automated tests

/scripts
Build/deployment/maintenance scripts

/docs
Project documentation
```

Only include directories agents commonly need.

---

# 5. Entry Points

Important execution entry points:

```text
{{ENTRY_POINT}}
Purpose: {{DESCRIPTION}}
```

Examples:

```text
/default.asp

Program.cs

index.php

main.py

deploy.ps1

scheduled-import.vbs
```

---

# 6. Architecture

## Architecture Style

```text
{{MVC / LAYERED / SCRIPT PIPELINE / MONOLITH / etc}}
```

## Major Components

```text
{{COMPONENT}}
Responsibility:
{{RESPONSIBILITY}}
```

## Typical Request / Execution Flow

```text
INPUT
{{COMPONENT}}
{{COMPONENT}}
{{COMPONENT}}
OUTPUT
```

---

# 7. Data

## Primary Data Store

```text
{{DATABASE_ENGINE_OR_STORAGE}}
```

## Data Access

```text
{{ADODB / EF CORE / RAW SQL / ODBC / etc}}
```

## Schema Location

```text
{{LOCATION}}
```

## Migration Method

```text
{{METHOD_OR_NONE}}
```

## Important Data Constraints

```text
- {{CONSTRAINT}}
- {{CONSTRAINT}}
```

Do not store passwords, connection secrets, or credentials here.

---

# 8. Authentication

## Authentication Model

```text
{{WINDOWS AUTH / FORMS / JWT / OAUTH / etc}}
```

## Authorization Model

```text
{{ROLES / CLAIMS / CUSTOM / etc}}
```

## Important Trust Boundaries

```text
- {{BOUNDARY}}
- {{BOUNDARY}}
```

---

# 9. External Integrations

## {{INTEGRATION_NAME}}

Purpose:

```text
{{PURPOSE}}
```

Interface:

```text
{{API / SFTP / SMTP / WEBHOOK / FILE SHARE / etc}}
```

Relevant documentation:

```text
{{PATH_OR_REFERENCE}}
```

---

# 10. Build

## Standard Build

```text
{{COMMAND}}
```

## Dependency Restore

```text
{{COMMAND_IF_NEEDED}}
```

## Build Output

```text
{{OUTPUT_LOCATION}}
```

## Known Requirements

```text
- {{REQUIREMENT}}
```

---

# 11. Tests

## Fast Test

```text
{{COMMAND}}
```

## Full Test

```text
{{COMMAND}}
```

## Integration Tests

```text
{{COMMAND_OR_NOT_APPLICABLE}}
```

## Manual Smoke Test

```text
{{STEPS}}
```

---

# 12. Deployment

## Target

```text
{{SERVER / PLATFORM / ENVIRONMENT}}
```

## Deployment Method

```text
{{DESCRIPTION}}
```

## Deployment Command

```text
{{COMMAND_IF_APPROPRIATE}}
```

## Health Verification

```text
{{HEALTH_CHECK}}
```

## Rollback

```text
{{ROLLBACK_METHOD}}
```

Detailed deployment documentation:

```text
{{DOC_PATH}}
```

---

# 13. Configuration

Important configuration files:

```text
{{FILE}}
Purpose:
{{PURPOSE}}
```

Environment-specific configuration:

```text
{{DESCRIPTION}}
```

Do not store secrets here.

---

# 14. Coding Conventions

Observed project conventions:

```text
- {{CONVENTION}}
- {{CONVENTION}}
- {{CONVENTION}}
```

Examples:

```text
Controllers perform request coordination only.

SQL commands use parameters.

Public APIs use the existing JSON response wrapper.
```

Only record conventions supported by repeated project evidence or explicit project instruction.

---

# 15. Error Handling

Standard project behavior:

```text
{{DESCRIPTION}}
```

Relevant implementation example:

```text
{{FILE_OR_COMPONENT}}
```

---

# 16. Logging

Logging mechanism:

```text
{{MECHANISM}}
```

Log locations:

```text
{{LOCATION}}
```

Important rules:

```text
- do not log credentials
- {{RULE}}
```

---

# 17. Important Constraints

Durable project constraints:

```text
- {{CONSTRAINT}}
- {{CONSTRAINT}}
```

Examples:

```text
Must remain compatible with Windows Server 2016.

Do not introduce React.

Application must continue using Microsoft Access.

Public endpoint URLs must remain backward compatible.
```

Only add constraints supported by project requirements.

---

# 18. Existing Patterns

## Creating a New {{COMPONENT}}

Reference examples:

```text
{{FILE}}
{{FILE}}
```

Typical pattern:

```text
{{SHORT_DESCRIPTION}}
```

---

# 19. High-Value Workflows

Recurring workflows include:

```text
{{WORKFLOW}}
Frequency:
{{FREQUENCY}}

Current procedure:
{{REFERENCE}}

Skill:
{{SKILL_PATH_OR_CANDIDATE}}
```

---

# 20. Project-Specific Skills

Active skills:

```text
.ai/skills/{{SKILL}}/SKILL.md
Purpose:
{{PURPOSE}}
```

Skill candidates:

```text
{{CANDIDATE}}
Evidence:
{{EVIDENCE}}
```

---

# 21. Automation

Existing automation:

```text
{{SCRIPT}}
Purpose:
{{PURPOSE}}
```

Automation candidates:

```text
{{CANDIDATE}}
Reason:
{{REASON}}
```

---

# 22. Known Risks

Important known technical or operational risks:

```text
{{RISK}}

Mitigation:
{{MITIGATION}}
```

Do not fill this with every theoretical risk.

Include only risks worth remembering.

---

# 23. Known Unknowns

Items agents should not assume:

```text
- {{UNKNOWN}}
- {{UNKNOWN}}
```

Example:

```text
Production database version has not yet been verified.
```

Remove items when resolved.

---

# 24. Useful Commands

## Development

```text
{{COMMAND}}
```

## Testing

```text
{{COMMAND}}
```

## Build

```text
{{COMMAND}}
```

## Deployment

```text
{{COMMAND}}
```

## Diagnostics

```text
{{COMMAND}}
```

Keep this section practical.

---

# 25. Agent Routing

When working on:

```text
{{TASK}}
```

use:

```text
.ai/skills/{{SKILL}}/SKILL.md
```

Example:

```text
Production IIS deployment
→ .ai/skills/iis-deployment/SKILL.md
```

---

# 26. Project Evolution

Update this file when:

```text
framework changes

architecture changes

build changes

test process changes

deployment changes

database changes

authentication changes

important project skills are added
```

Do not update it for normal implementation details.

---

# 27. Bootstrap Information

Last bootstrap review:

```text
{{DATE}}
```

Bootstrap confidence:

```text
LOW / MEDIUM / HIGH
```

Areas needing deeper inspection:

```text
- {{AREA}}
```

---

# 28. Maintenance Principle

This file should stay:

```text
CURRENT

CONCISE

USEFUL
```

If a section becomes large:

```text
move detailed content to project documentation

and reference it here
```

This profile is a map, not the entire territory.

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# Discover

Version: 1.0.0

## Purpose

Understand the relevant project state before proposing or making changes.

Use this prompt at the beginning of meaningful work when the current implementation, architecture, constraints, or conventions are not yet fully understood.

---

# Instructions

Before proposing a final solution:

1. Read the relevant root instructions.
2. Inspect the files directly related to the request.
3. Search for existing implementations that solve a similar problem.
4. Inspect related configuration.
5. Inspect tests when available.
6. Inspect documentation that defines the current behavior.
7. Identify dependencies and external systems involved.
8. Identify the project's current conventions.

Do not invent project facts that can be discovered.

---

# Determine

Identify:

```text
OBJECTIVE

CURRENT STATE

DESIRED STATE

RELEVANT FILES

DEPENDENCIES

CONSTRAINTS

EXISTING PATTERNS

IMPORTANT INVARIANTS

UNKNOWN CRITICAL VARIABLES
```

---

# Separate Evidence

Classify information as:

```text
VERIFIED

INFERRED

UNKNOWN

USER DECISION REQUIRED
```

Do not present an inference as verified fact.

---

# Existing Pattern Search

Before proposing new architecture or structure, search for analogous existing implementations.

Examples:

```text
new controller
→ inspect existing controllers

new API
→ inspect existing APIs

new PowerShell automation
→ inspect existing scripts

new data-import process
→ inspect existing imports

new test
→ inspect existing test conventions
```

Prefer existing project conventions when they are sound.

---

# Unknown Information

For each unknown ask:

```text
Can this be discovered from the repository or environment?
```

If yes:

```text
DISCOVER IT
```

If no, determine:

```text
Does this materially affect correctness?
```

If yes:

use the Diagnostic Intake skill.

If no:

state a safe assumption when necessary and continue.

---

# Avoid

Do not:

```text
write implementation before understanding the existing system

invent libraries or APIs

assume architecture

ask the user questions the repository can answer

read unrelated parts of the repository without reason

produce a large design before understanding project constraints
```

---

# Output

Produce a concise discovery summary:

```text
## Objective

[...]

## Current State

[...]

## Relevant Existing Patterns

[...]

## Constraints

[...]

## Important Unknowns

[...]

## Recommended Next Step

[...]
```

For simple tasks, compress this substantially.

---

# Completion

Discovery is complete when enough evidence exists to form a reasonable initial approach.

Discovery does not require knowing everything about the repository.

Stop when additional investigation has low expected value.

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# Plan

Version: 1.0.0

## Purpose

Create the smallest implementation plan necessary to safely and verifiably complete the requested work.

The plan must be based on discovered project evidence.

Do not design from generic assumptions when the project already contains relevant patterns.

---

# Before Planning

Confirm that discovery has identified:

```text
OBJECTIVE

CURRENT STATE

DESIRED STATE

CONSTRAINTS

RELEVANT COMPONENTS

EXISTING PATTERNS
```

If critical context is still missing, use Diagnostic Intake before planning.

---

# Form Initial Approach

State the proposed approach in concise terms.

Include:

```text
WHAT WILL CHANGE

WHY THIS APPROACH FITS

WHAT WILL REMAIN UNCHANGED
```

Treat this as a working hypothesis.

---

# Mirror Audit

Before finalizing the plan, audit the initial approach using:

```text
.ai/skills/mirror-audit/SKILL.md
```

Check for:

```text
hidden assumptions

failure modes

edge cases

security

data integrity

compatibility

rollback

maintainability

unnecessary complexity
```

Revise the approach when the audit reveals a real weakness.

---

# Plan Structure

A normal implementation plan should contain:

```text
## Objective

[...]

## Files / Components

- [...]
- [...]

## Implementation Sequence

1. [...]
2. [...]
3. [...]

## Risks

- [...]

## Verification

- [...]

## Rollback

[...] // only when relevant
```

---

# Scale to Complexity

## Fast Path

For trivial work:

```text
1. make change
2. verify
```

Do not generate a formal architecture plan.

---

## Standard Path

For normal feature work:

```text
1. inspect affected component
2. modify implementation
3. update related tests
4. verify
5. review
```

---

## High-Risk Path

For migrations, security, infrastructure, destructive changes, or major architecture:

include:

```text
preconditions

backup

rollback

execution order

verification gates

recovery

red-team review
```

---

# Dependencies

Identify dependencies that affect sequencing.

Example:

```text
database schema
data access layer
service
controller
tests
```

Do not order work arbitrarily when dependencies require a specific sequence.

---

# Verification Plan

Specify how each meaningful result will be verified.

Prefer:

```text
existing test

targeted test

build

static analysis

smoke test

query

health check

direct execution
```

Avoid:

```text
verify that it looks right
```

when stronger evidence is available.

---

# Rollback

Define rollback when:

```text
change is destructive

production state changes

schema changes

data transforms

deployment can partially fail

external state changes
```

Do not create elaborate rollback plans for trivial reversible edits.

---

# Scope Control

Explicitly identify what the implementation will NOT change when scope drift is likely.

Example:

```text
This change will add the import status reporting.

It will not replace the existing Access import mechanism.
```

---

# Avoid

Do not:

```text
create plans longer than the work

introduce architecture not justified by the request

include speculative future features

list every theoretical risk

repeat discovery details unnecessarily
```

---

# Output

Return the finalized implementation plan.

If the plan reveals a blocker, surface it before implementation.

Otherwise proceed according to the current task's execution mode.

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# Retrospective

Version: 1.0.0

## Purpose

Evaluate completed work for reusable learning and decide whether any part of the AI operating system should improve.

Keep this lightweight.

The default outcome may be:

```text
NO PERMANENT CHANGE
```

---

# Review the Task

Ask:

```text
Did the requested result succeed?

What worked particularly well?

What failed or nearly failed?

What assumption was wrong?

What evidence resolved uncertainty?

What check found the problem?

Could something have caught it earlier?
```

---

# Identify Reusable Learning

Ask:

```text
Would this matter again?
```

Possible reusable discoveries:

```text
procedure

diagnostic method

verification method

failure pattern

project convention

prompt improvement

automation opportunity

template
```

---

# Determine Scope

Classify the lesson:

```text
TASK-SPECIFIC

PROJECT FACT

PROJECT PROCEDURE

GENERAL AGENT RULE

MODEL-SPECIFIC RULE

SKILL IMPROVEMENT

PROMPT IMPROVEMENT

AUTOMATION
```

---

# Choose Destination

Use the narrowest appropriate location.

```text
PROJECT FACT
→ project documentation

REUSABLE PROCEDURE
→ existing/new skill

GENERAL AGENT RULE
→ AGENTS.md

CLAUDE-SPECIFIC
→ CLAUDE.md

PROMPT FAILURE
→ prompt / prompt-debugger

AUTOMATABLE DETERMINISTIC WORK
→ script or automation

WEAK CANDIDATE
→ LEARNINGS.md
```

---

# Evidence Test

Before permanent promotion ask:

```text
Is there actual evidence?

Is the lesson reusable?

Is the cause understood?

Can the improvement be tested?

Does existing guidance already cover it?
```

If evidence is weak:

```text
record candidate in .ai/state/LEARNINGS.md
```

instead of changing permanent instructions.

---

# Skill Decision

If the task revealed a reusable procedure, use:

```text
.ai/skills/skill-extractor/SKILL.md
```

Do not create the skill directly without checking for existing ownership.

---

# Prompt Decision

If AI instructions failed, use:

```text
.ai/skills/prompt-debugger/SKILL.md
```

Determine why before rewriting.

---

# Automation Decision

If a repeated procedure is deterministic, consider whether:

```text
SCRIPT
```

is better than:

```text
SKILL
```

Examples:

```text
schema validation
file comparison
deployment health check
build verification
```

Agents should not solve with prompts what software can reliably automate.

---

# Output

Return an internal or visible summary:

```text
## Result

[...]

## What Worked

[...]

## What Failed

[...]

## Reusable Lesson

[...]

## Action

NO CHANGE
or
LEARNING ENTRY
or
UPDATE EXISTING SKILL
or
CREATE SKILL
or
UPDATE PROMPT
or
UPDATE PROJECT DOCS
or
UPDATE AGENTS.md
or
UPDATE CLAUDE.md
or
CREATE AUTOMATION
```

If no durable lesson exists, stop.

Do not create process merely because a retrospective was performed.

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# Review

Version: 1.0.0

## Purpose

Review completed work for correctness, regression risk, maintainability, security, and consistency with the project's existing behavior.

This review occurs after implementation.

It does not replace execution or testing.

---

# Review Context

Before reviewing, understand:

```text
REQUESTED CHANGE

ACTUAL CHANGE

FILES MODIFIED

VERIFICATION ALREADY PERFORMED

IMPORTANT PROJECT CONSTRAINTS
```

Inspect the resulting diff when available.

---

# Review Areas

Evaluate only relevant areas.

---

# Correctness

Check:

```text
Does the implementation satisfy the request?

Does the logic do what it claims?

Are important branches handled?

Are errors handled appropriately?
```

---

# Regression Risk

Ask:

```text
What existing behavior could this change unintentionally affect?
```

Inspect:

```text
callers

shared code

public APIs

database behavior

configuration

routes

file formats

authentication
```

when relevant.

---

# Project Consistency

Check whether the implementation follows established:

```text
architecture

naming

error handling

configuration

data access

testing

logging
```

Do not recommend a different architecture merely because another pattern is personally preferable.

---

# Security

When relevant, inspect:

```text
authentication

authorization

input validation

output encoding

SQL injection

command injection

path traversal

secret handling

privilege boundaries
```

Do not manufacture security findings without evidence.

---

# Data Integrity

For data changes inspect:

```text
transactions

null handling

duplicates

type conversion

row counts

partial writes

referential integrity
```

---

# Maintainability

Look for:

```text
unnecessary duplication

hidden side effects

magic values

poor naming

unnecessary abstraction

unnecessary dependencies

complexity without benefit
```

---

# Verification

Check:

```text
Were appropriate tests actually run?

Does the verification prove the important behavior?

Was only the happy path tested?

What remains unverified?
```

Do not accept:

```text
"This should work."
```

as verification evidence.

---

# Diff Hygiene

Inspect for:

```text
accidental deletions

formatting churn

debug code

temporary files

hardcoded credentials

unrelated edits

stale comments

generated files accidentally committed
```

---

# Finding Classification

Classify findings as:

```text
BLOCKER

IMPORTANT

OPTIONAL
```

## Blocker

Must be resolved before completion.

## Important

Should be addressed because it creates meaningful risk.

## Optional

Improvement that is useful but not required for correctness.

Do not inflate optional preferences into blockers.

---

# Finding Format

For each meaningful finding:

```text
### Finding

Severity:
[...]

Location:
[...]

Issue:
[...]

Why It Matters:
[...]

Recommended Change:
[smallest appropriate correction]
```

---

# No-Issue Result

It is acceptable to conclude:

```text
No blocking or important issues found.
```

Do not invent findings to justify the review.

---

# Red-Team Routing

If the task is consequential and normal review passes, invoke:

```text
.ai/skills/red-team/SKILL.md
```

for the relevant adversarial perspective.

---

# Output

Return:

```text
## Review Result

PASS
or
CHANGES REQUIRED

## Blockers
[...]

## Important Findings
[...]

## Optional Improvements
[...]

## Verification Notes
[...]
```

Omit empty sections when they add no value.

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# Agent System Maintainer

Version: 1.0.0
Status: ACTIVE

## Purpose

Maintain the health of the repository's self-improving AI operating system.

The self-improvement system must be able to do both:

```text
ADD USEFUL KNOWLEDGE
```

and:

```text
REMOVE STALE OR DUPLICATE KNOWLEDGE
```

Without maintenance, self-improving systems tend to accumulate:

```text
too many skills

duplicate rules

stale project assumptions

unused prompts

conflicting instructions

obsolete workflows
```

This skill provides deliberate cleanup.

---

# 1. Trigger Conditions

Use this skill:

* after substantial AI-OS growth
* after several new skills are added
* after major project architecture changes
* when agents struggle to select the right skill
* when root instructions become large
* when conflicting behavior appears
* when skills appear duplicated
* periodically when ongoing agent use justifies maintenance

Do not run full maintenance after every ordinary task.

---

# 2. Maintenance Flow

```text
INVENTORY
USAGE REVIEW
DUPLICATION REVIEW
CONFLICT REVIEW
STALE KNOWLEDGE REVIEW
SKILL HEALTH
PROMPT HEALTH
ROOT INSTRUCTION HEALTH
PROJECT PROFILE HEALTH
CLEANUP PLAN
SYSTEM AUDIT
```

---

# 3. Inventory

Inspect:

```text
AGENTS.md

CLAUDE.md

.ai/AI-OS.md

.ai/WORKFLOW.md

.ai/PROJECT.md

.ai/skills/

.ai/prompts/

.ai/templates/

.ai/state/LEARNINGS.md

.ai/CHANGELOG.md
```

Determine what currently exists.

---

# 4. Skill Inventory

For each skill identify:

```text
NAME

VERSION

STATUS

PURPOSE

TRIGGER

REFERENCES

KNOWN USAGE
```

Look for unclear ownership.

---

# 5. Unused Skill Review

Ask:

```text
Has this skill actually been useful?
```

Possible statuses:

```text
ACTIVE AND USED

ACTIVE BUT UNCONFIRMED

OBSOLETE

DUPLICATE

CANDIDATE
```

Do not delete a skill only because usage statistics are unavailable.

Use project evidence and changelog history.

---

# 6. Duplicate Skill Review

Compare skills for:

```text
same trigger

same procedure

same outputs

same domain
```

If two skills overlap substantially, decide:

```text
MERGE

NARROW

DEPRECATE ONE

KEEP SEPARATE
```

Keep separate only when the distinction improves agent routing.

---

# 7. Root Duplication Review

Check whether `AGENTS.md` duplicates:

```text
skills

WORKFLOW.md

AI-OS.md

project documentation
```

Root instructions should contain:

```text
POLICY

ROUTING

CORE BEHAVIOR
```

not every procedure.

---

# 8. Model Adapter Review

Check whether `CLAUDE.md` contains generic rules that belong in:

```text
AGENTS.md
```

or project facts that belong in:

```text
PROJECT.md
```

Keep model adapters thin.

---

# 9. Prompt Review

Inspect `.ai/prompts/`.

Identify:

```text
unused prompts

duplicate prompts

stale prompts

prompts that should be skills

prompts whose behavior is now in root instructions
```

Delete or deprecate low-value prompts.

---

# 10. Template Review

Templates should represent useful recurring structures.

Check for:

```text
unused templates

obsolete formats

duplicates

project-specific content in generic templates
```

---

# 11. Learning Ledger Review

Review:

```text
.ai/state/LEARNINGS.md
```

Classify pending entries:

```text
PROMOTE

KEEP PENDING

REJECT

MERGE

STALE
```

Do not allow old weak candidates to accumulate forever.

---

# 12. Stale Project Information

Compare:

```text
.ai/PROJECT.md
```

against current repository evidence.

Check:

```text
framework

runtime

database

deployment

tests

directories

authentication

external integrations
```

Update stale information.

Do not silently preserve old architecture.

---

# 13. Broken Reference Review

Validate references to:

```text
skills

prompts

templates

docs

scripts
```

Remove or correct broken paths.

---

# 14. Version Review

Check that important changes were versioned appropriately.

Look for:

```text
significant behavior changes without version increment

deprecated skills still marked ACTIVE

new skills missing changelog entries
```

---

# 15. Changelog Review

Determine whether:

```text
.ai/CHANGELOG.md
```

still explains meaningful AI-system evolution.

Do not require excessive entries for tiny formatting changes.

---

# 16. Instruction Conflict Review

Search for incompatible rules.

Examples:

```text
always ask
vs.
never ask

always plan
vs.
fast path

always use skill X
vs.
skill X says it is optional
```

Resolve conflicts operationally.

---

# 17. Routing Quality

Ask:

```text
Can an agent tell which skill to use?
```

If several skills could plausibly activate:

```text
tighten triggers

add routing rule

merge skills
```

Good skill libraries are easy to navigate.

---

# 18. Skill Library Size

There is no ideal number.

However ask:

```text
Does each active skill justify its context cost?
```

A smaller high-quality library is preferable to a large noisy one.

---

# 19. General vs Project Skills

Classify skills as:

```text
GENERAL

PROJECT-SPECIFIC
```

Generic skills should remain portable.

Project-specific skills may contain project domain knowledge.

Do not force every skill to be universal.

---

# 20. Automation Review

Look for skills that repeatedly describe deterministic operations.

Ask:

```text
Should this become executable automation?
```

Example:

Skill repeatedly says:

```text
check these 12 configuration keys
```

A validation script may be better.

Keep the skill as orchestration if needed, but automate reliable mechanical checks.

---

# 21. Documentation Review

Ensure project facts live in appropriate documentation rather than only inside AI instructions.

Important knowledge should not become inaccessible to human developers.

---

# 22. Simplification

For each system component ask:

```text
Can this be shorter without losing behavior?
```

Look for:

```text
repeated definitions

duplicated examples

obvious rules

obsolete caveats
```

Self-improvement includes compression.

---

# 23. Cleanup Classification

For each finding choose:

```text
KEEP

REVISE

MOVE

MERGE

DEPRECATE

DELETE
```

Do not perform large cleanup without understanding dependencies.

---

# 24. Deprecation Before Deletion

For heavily referenced skills:

```text
DEPRECATE
UPDATE REFERENCES
VERIFY
DELETE LATER
```

For unused experimental files:

```text
DELETE
```

may be sufficient.

Version control preserves history.

---

# 25. Cleanup Safety

Do not remove:

```text
user-authored requirements

security safeguards

verification requirements

project-critical workflows
```

merely to reduce file size.

Simplification must preserve behavior.

---

# 26. Maintenance Report

Produce:

```text
## AI-OS Health

### Keep

[...]

### Revise

[...]

### Merge

[...]

### Deprecate

[...]

### Delete

[...]

### Project Profile Updates

[...]

### System Risks

[...]

### Recommended Version Change

[...]
```

For small systems, compress this.

---

# 27. Apply Changes

When authorized by the self-improvement rules:

```text
make the smallest coherent cleanup changes
```

Then run:

```text
.ai/skills/system-audit/SKILL.md
```

---

# 28. Regression

After maintenance, test:

```text
FAST PATH

STANDARD PATH

HIGH-RISK PATH

SKILL ROUTING

SELF-IMPROVEMENT
```

The cleaned system should behave at least as well as before.

---

# 29. Maintenance Success Criteria

Maintenance succeeds when:

```text
agents can find guidance more easily

duplicate guidance decreases

stale information decreases

important safeguards remain

context burden decreases or remains justified

no important workflow regresses
```

---

# 30. Changelog

## 1.0.0

Initial active version.

Introduced:

* AI-system inventory
* unused skill review
* duplicate skill detection
* root/model adapter cleanup
* prompt/template maintenance
* learning-ledger cleanup
* stale project-profile detection
* routing review
* automation opportunities
* merge/deprecate/delete lifecycle

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# Coffee Chat Editor

Version: 1.0.0
Status: ACTIVE

## Purpose

Edit AI-generated or overly formal prose so it sounds direct, natural, specific, and human while preserving the author's intended meaning.

The goal is not:

```text
MAKE EVERYTHING CASUAL
```

The goal is:

```text
REMOVE MECHANICAL AI WRITING
```

while retaining the appropriate level of professionalism.

---

# 1. Trigger Conditions

Use this skill for important user-facing prose such as:

* emails
* reports
* proposals
* executive updates
* documentation
* announcements
* instructions
* presentations
* business communication
* explanations
* public-facing copy

Use it when the draft sounds:

```text
generic

stiff

repetitive

overly formal

corporate

AI-generated

wordy
```

Do not apply this style mechanically to:

* source code
* legal text that must preserve exact wording
* technical specifications where precision requires formality
* quoted source material
* data tables

---

# 2. Core Editing Pass

Use this sequence:

```text
CUT
SPECIFY
SIMPLIFY
VARY
SPEAK
```

---

# 3. Cut Unnecessary Opening

Look at the first sentence or paragraph.

Common weak openings:

```text
Certainly!

Great question.

In today's rapidly evolving world...

It is important to note that...

I hope this message finds you well...
```

Ask:

```text
Does the reader need this before the real message?
```

If not, remove it.

Start closer to the point.

---

# 4. Cut Unnecessary Closing

AI prose often repeats itself at the end.

Example:

```text
In conclusion, by following these steps,
you can ensure a successful implementation.
```

If the body already established this, remove it.

A useful ending may instead contain:

```text
decision

next action

deadline

specific request

important final fact
```

---

# 5. Replace Generic Language

Weak:

```text
We encountered some challenges during implementation.
```

Better:

```text
The import failed because 214 rows contained invalid ZIP codes.
```

Prefer:

```text
CONCRETE FACT
```

over:

```text
GENERIC DESCRIPTION
```

---

# 6. Replace Generic Verbs

Weak verbs include:

```text
utilize

facilitate

leverage

enable

optimize

enhance
```

when a simpler verb communicates more clearly.

Examples:

```text
utilize → use

facilitate communication → help the teams communicate

leverage the API → use the API
```

Do not ban a word if it is genuinely the most accurate choice.

---

# 7. Remove Transitional Slag

Look for excessive:

```text
Furthermore

Moreover

Additionally

In addition

With that being said

As previously mentioned

It is worth noting
```

Sometimes the next sentence can simply begin.

Transitions should clarify structure, not fill space.

---

# 8. Vary Sentence Length

Mechanical AI writing often has repeated sentence rhythm.

Example:

```text
The system validates the request.
The system checks the database.
The system creates the record.
The system returns the response.
```

Possible revision:

```text
The system validates the request, checks the database,
and creates the record. If successful, it returns the response.
```

Do not artificially vary sentences merely to create style.

Prioritize readability.

---

# 9. Prefer Active Voice

Weak:

```text
The database was updated by the deployment script.
```

Better:

```text
The deployment script updated the database.
```

Use passive voice when the actor is:

```text
unknown

irrelevant

intentionally de-emphasized
```

Active voice is a preference, not an absolute rule.

---

# 10. Replace Abstract Nouns

Weak:

```text
The implementation of the validation will provide
an improvement in reliability.
```

Better:

```text
The validation will make the import more reliable.
```

Look for unnecessary noun constructions:

```text
implementation of

utilization of

optimization of

execution of
```

Convert to direct verbs when possible.

---

# 11. Remove Fake Importance

Watch for:

```text
crucial

critical

paramount

pivotal

essential

game-changing
```

when no evidence supports the emphasis.

Weak:

```text
It is crucial to ensure robust data integrity.
```

Better:

```text
Validate the imported row count before deleting the source file.
```

Specific consequences create importance naturally.

---

# 12. Avoid Decorative Vocabulary

Be suspicious of words that make simple ideas sound grand.

Examples:

```text
delve

landscape

tapestry

multifaceted

spearhead

unlock

foster

synergy

seamless

navigate
```

Do not maintain a rigid banned-word list.

The real rule is:

```text
USE THE SIMPLEST ACCURATE WORD
```

---

# 13. Reduce Artificial Symmetry

AI frequently writes:

```text
faster, smarter, and more efficient

clear, concise, and actionable

security, scalability, and reliability
```

Three-part lists are fine when three real categories exist.

Remove them when they are decorative.

---

# 14. Reduce Excessive Headings

Do not create a heading for every paragraph.

Use headings when they help the reader:

```text
scan

navigate

understand hierarchy
```

For short communication, paragraphs may be better.

---

# 15. Reduce Excessive Bold

AI drafts often bold too many phrases.

Use emphasis for genuinely important:

```text
decision

deadline

risk

action

number
```

If everything is emphasized, nothing is.

---

# 16. Keep Specific Numbers

Do not replace:

```text
214 rejected rows
```

with:

```text
a number of rejected records
```

Specific information makes prose sound more credible and useful.

---

# 17. Keep Necessary Technical Terms

Do not simplify away precision.

Example:

```text
TLS certificate
```

should not become:

```text
security thing
```

Use the proper term and explain it when the audience needs help.

---

# 18. Remove Repetition

Check whether consecutive sections repeat:

```text
same conclusion

same risk

same recommendation

same background
```

Merge or cut repetition.

---

# 19. Compress Setup

Weak:

```text
Before we get into the details, it may be useful to first
provide a little bit of context regarding how the system
currently operates.
```

Better:

```text
The application currently imports the CSV directly into Access.
```

Start with actual context.

---

# 20. Direct Requests

For email or business communication, make the requested action obvious.

Weak:

```text
It would be greatly appreciated if you could potentially
review the attached material when you have an opportunity.
```

Better:

```text
Please review the attached file by Friday.
```

Direct does not mean rude.

---

# 21. Friendly Without Filler

Warm communication may still be concise.

Example:

```text
Thanks again for helping with the convention cleanup.
The windows looked great, and your help made Friday's setup much easier.
```

This is warmer than generic praise because it is specific.

---

# 22. Tone Levels

Choose a tone appropriate to the audience.

## Level 1 — Executive Brief

Use:

```text
short

decision-focused

specific

little background
```

---

## Level 2 — Peer Colleague

Use:

```text
direct

professional

natural

moderate technical detail
```

---

## Level 3 — Socratic Tutor

Use:

```text
clear

conversational

question-driven

supportive without overpraise
```

---

## Level 4 — Non-Technical Friend

Use:

```text
plain language

short examples

minimal jargon

natural phrasing
```

Do not automatically choose Level 4 because it sounds "human."

Audience determines tone.

---

# 23. Executive Editing Example

Before:

```text
It is important to note that the deployment was ultimately
successful, despite the fact that we did encounter a number
of challenges throughout the overall implementation process.
```

After:

```text
The deployment succeeded after we corrected two configuration errors.
```

---

# 24. Technical Documentation Example

Before:

```text
In order to facilitate the process of determining which
application is currently utilizing a specific port, the
following command can be leveraged.
```

After:

```text
Use this command to find the process using a port:
```

---

# 25. Status Update Example

Before:

```text
We have continued to make meaningful progress across a
number of key areas as we navigate toward completion.
```

After:

```text
The API is complete. Database migration testing is still in progress.
```

---

# 26. Email Example

Before:

```text
I just wanted to reach out and take a moment to thank
everyone for all of your assistance and support.
```

After:

```text
Thank you for helping with the cleanup this weekend.
```

Specific detail can follow.

---

# 27. Do Not Destroy Personality

The goal is not to make every writer sound identical.

Preserve:

```text
humor

warmth

formality

directness

individual vocabulary
```

when appropriate.

Remove machine-like patterns, not human personality.

---

# 28. Do Not Over-Compress

Too much editing can turn useful explanation into telegram-style prose.

Before:

```text
The script stops IIS before copying the files because
IIS may otherwise keep DLLs open and prevent replacement.
```

Do not reduce to:

```text
Stop IIS. Copy files.
```

The explanation matters.

---

# 29. Read-Aloud Test

After editing, mentally read the prose at speaking pace.

Ask:

```text
Would a real person naturally say this?

Is any sentence awkward?

Is any phrase unnecessarily formal?

Does the rhythm sound repetitive?
```

This is a strong final test.

---

# 30. Meaning Preservation

Never change:

```text
technical meaning

commitment

deadline

legal meaning

risk severity

quantitative facts
```

merely to improve tone.

Stylistic editing must preserve substance.

---

# 31. Source Preservation

When editing text based on a provided source, preserve the source's:

```text
facts

claims

terminology

level of certainty
```

Do not introduce unsupported content merely to make the prose sound more complete.

---

# 32. Five-Step Fast Pass

For routine editing, use:

```text
1. CUT unnecessary first/last sentence

2. VARY repetitive sentence structure

3. REPLACE generic language with specifics

4. REMOVE mechanical transitions

5. READ ALOUD
```

This is the default lightweight mode.

---

# 33. Full Pass

For important external communication:

```text
1. identify audience
2. identify purpose
3. identify required action
4. remove unnecessary preamble
5. replace generic language
6. simplify verbs
7. remove repetitive transitions
8. vary sentence length
9. check technical precision
10. check tone
11. remove redundant closing
12. read aloud
```

---

# 34. Output Contract

When used as an editing skill, return:

```text
EDITED VERSION
```

When useful, also briefly identify:

```text
major changes made
```

Do not bury the revised text underneath a long critique unless critique was requested.

---

# 35. Self-Improvement

Improve this skill when:

* edited output still sounds mechanical
* editing removes too much technical precision
* tone becomes overly casual
* repeated user corrections reveal a stable writing preference
* a better concise editing checklist emerges

Do not create enormous word-ban lists.

Prefer principles.

---

# 36. Changelog

## 1.0.0

Initial active version.

Introduced:

* zero-preamble preference
* unnecessary-ending removal
* concrete specificity
* active voice
* transition cleanup
* sentence-rhythm variation
* anti-buzzword guidance
* tone levels
* read-aloud test
* meaning preservation
* five-step fast pass

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# Diagnostic Intake

Version: 1.0.0
Status: ACTIVE

## Purpose

Determine the minimum missing context required to complete a task accurately without producing generic output, asking unnecessary questions, or inventing project facts.

This skill prevents two common agent failures:

```text
ASKING TOO MANY QUESTIONS
```

and:

```text
GUESSING WHEN THE ANSWER COULD HAVE BEEN DISCOVERED
```

---

# 1. Trigger Conditions

Use this skill when:

* the request is materially ambiguous
* multiple valid implementations depend on unknown constraints
* architecture depends on missing information
* a workflow cannot be safely selected yet
* critical project facts are unknown
* the user asks for a curriculum, architecture, process, or major plan with insufficient context
* a wrong assumption could create substantial rework

Do not use this skill when:

* the answer is already in the repository
* the missing information is noncritical
* a safe reasonable assumption allows progress
* the task is trivial
* the user has already supplied the necessary information

---

# 2. Core Rule

Use:

```text
DISCOVER FIRST
ASK SECOND
ASSUME THIRD
```

not:

```text
ASK FIRST
```

The agent should exhaust reasonable discovery before asking the user for project information.

---

# 3. Intake Process

Start by identifying:

```text
WHAT MUST BE KNOWN
```

versus:

```text
WHAT WOULD ONLY BE NICE TO KNOW
```

Classify unknowns as:

```text
CRITICAL

USEFUL

OPTIONAL
```

---

# 4. Critical Information

An unknown is CRITICAL when different answers would materially change:

* architecture
* security
* data handling
* implementation
* compatibility
* cost
* deployment
* irreversible behavior
* requested output

Example:

```text
"What database engine does this application use?"
```

may be critical for a database migration.

---

# 5. Useful Information

Useful information improves the result but does not prevent safe progress.

Example:

```text
"Do you prefer tabs or spaces?"
```

If the project already has a style, discover it.

If not, follow a reasonable convention.

Do not block the task.

---

# 6. Optional Information

Optional information should rarely be asked.

Example:

```text
"What color would you like comments to be?"
```

when formatting has no effect on the requested outcome.

Do not create unnecessary interaction.

---

# 7. Discovery Before Questions

Search available evidence.

Possible sources:

```text
AGENTS.md

CLAUDE.md

README

configuration files

package manifests

project files

source code

tests

database configuration

deployment scripts

existing examples

documentation
```

If the repository answers the question, use the repository.

---

# 8. Question Quality

Ask precise questions.

Avoid:

```text
"Tell me more about the project."
```

Prefer:

```text
"Should this endpoint preserve the existing Windows Authentication model, or is anonymous access required?"
```

A good question should clearly affect a decision.

---

# 9. Limit Question Count

Prefer:

```text
1–4 targeted questions
```

over:

```text
10–20 question questionnaire
```

If several questions depend on the answer to one earlier question, ask the first question only.

---

# 10. Progressive Intake

Use:

```text
QUESTION
ANSWER
DO WE NOW HAVE ENOUGH?
/ \
YES NO
↓ ↓
PROCEED NEXT QUESTION
```

Do not collect unnecessary information in advance.

---

# 11. Safe Assumptions

An assumption may be used when:

```text
the risk is low

the assumption follows existing project conventions

the decision is reversible

the user can easily correct it
```

State important assumptions.

Example:

```text
I'll assume this should follow the same authentication pattern as the existing controllers.
```

Then proceed.

---

# 12. Unsafe Assumptions

Do not silently assume:

```text
production database credentials

destructive intent

security permissions

authentication changes

data-loss tolerance

deployment targets

legal/compliance requirements

irreversible migration behavior
```

when the assumption materially affects risk.

---

# 13. Repository Discovery Example

Task:

```text
Create a new API endpoint.
```

Unknown:

```text
What authentication does the project use?
```

Before asking:

```text
SEARCH existing endpoints

INSPECT middleware/configuration

INSPECT authentication configuration
```

If the answer is found:

```text
PROCEED
```

Do not ask the user.

---

# 14. Architecture Example

Task:

```text
Design a caching layer.
```

Potential unknowns:

```text
single server or multiple servers?

acceptable stale-data window?

what data is being cached?

existing cache technology?
```

First inspect:

```text
deployment configuration

dependencies

existing cache code
```

Ask only what remains critical.

---

# 15. Learning Example

Task:

```text
Teach me Kubernetes.
```

Do not immediately produce a 30-day course.

Ask a diagnostic question such as:

```text
Have you deployed containers with Docker before, or should we start with containers and images?
```

Then build from the response.

---

# 16. Decision Flow

```text
MISSING INFORMATION
CAN IT BE DISCOVERED?
/ \
YES NO
↓ ↓
DISCOVER DOES IT CHANGE CORRECTNESS?
/ \
YES NO
↓ ↓
ASK ASSUME / IGNORE
```

---

# 17. Output Contract

The skill should produce one of two outcomes.

## Outcome A — Enough Information Exists

Produce:

```text
Resolved objective

Known constraints

Important assumptions

Next action
```

Then continue the task.

---

## Outcome B — Critical Information Missing

Ask only the minimal targeted questions required to proceed.

---

# 18. Anti-Pattern: Questionnaire Mode

Avoid:

```text
What language?
What framework?
What database?
What OS?
What editor?
What deployment method?
What testing system?
What coding style?
What logging system?
What version control?
```

when most of this could be discovered from the project.

This wastes user attention.

---

# 19. Anti-Pattern: Fake Clarification

Do not ask a question merely to appear careful.

Example:

User:

```text
Create a PowerShell command to list FSMO role holders.
```

Bad response:

```text
What Windows version are you using?
```

if the requested command is standard and does not depend materially on that detail.

Proceed.

---

# 20. Anti-Pattern: Premature Assumption

Task:

```text
Delete old database records.
```

Do not silently choose:

```text
older than 30 days
```

when no age threshold exists.

That variable directly controls destructive behavior.

Ask or discover it.

---

# 21. Completion Test

Diagnostic intake is complete when every critical variable has been:

```text
DISCOVERED

ANSWERED

OR

EXPLICITLY AND SAFELY ASSUMED
```

At that point stop asking questions and continue the work.

---

# 22. Self-Improvement

Improve this skill when:

* agents repeatedly ask unnecessary questions
* agents repeatedly make dangerous assumptions
* new discovery sources become available
* a better critical/useful/optional distinction is found
* repeated user feedback reveals poor question design

Do not add project-specific intake questions here.

Project-specific rules belong in project skills or documentation.

---

# 23. Changelog

## 1.0.0

Initial active version.

Introduced:

* discovery-first intake
* critical/useful/optional classification
* progressive questioning
* safe assumption rules
* question-count discipline
* anti-questionnaire guidance

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# Prompt Architect

Version: 1.0.0
Status: ACTIVE

## Purpose

Design reusable production prompts and agent instructions from a task's goals, inputs, constraints, workflow, failure modes, and expected outputs.

This skill is for prompts that will be used repeatedly.

The goal is not:

```text
WRITE A VERY LONG PROMPT
```

The goal is:

```text
BUILD THE SMALLEST RELIABLE INSTRUCTION SYSTEM
```

---

# 1. Trigger Conditions

Use this skill when:

* a task will be repeated
* a workflow needs standardization
* several people or agents should perform a task consistently
* the current prompt produces inconsistent results
* a successful manual workflow should become reusable
* building a specialized AI agent
* creating project prompts
* creating a reusable role
* converting a procedure into agent instructions

Do not create a permanent prompt for every one-time request.

---

# 2. Core Architecture

A robust reusable prompt usually needs some combination of:

```text
ROLE

OBJECTIVE

INPUT CONTRACT

CONTEXT

PROCEDURE

CONSTRAINTS

TOOL RULES

OUTPUT CONTRACT

FAILURE HANDLING

VERIFICATION
```

Not every prompt requires every section.

Include only what materially improves behavior.

---

# 3. Start with the Task

Define the task in one sentence.

Example:

```text
Review an ASP Classic endpoint for security, correctness,
maintainability, and consistency with the existing project.
```

If the task cannot be described clearly in one or two sentences, the prompt may be trying to do too many jobs.

---

# 4. Define the User

Identify who consumes the result.

Examples:

```text
developer

system administrator

manager

student

end user

technical reviewer
```

Output quality often depends on the consumer.

---

# 5. Define Inputs

Identify what the prompt will receive.

Example:

```text
Required:

- source file
- project instructions
- related configuration

Optional:

- error logs
- test results
```

A reusable prompt should not depend on hidden context whenever avoidable.

---

# 6. Define Output

Determine exactly what the prompt should produce.

Weak:

```text
Review the code.
```

Better:

```text
Return:

1. blocking findings
2. important findings
3. optional improvements
4. verification recommendations

For each finding include:
- location
- issue
- why it matters
- smallest recommended fix
```

Output contracts reduce ambiguity.

---

# 7. Separate Stable and Variable Information

Stable information belongs in the reusable prompt.

Variable information should become placeholders.

Example:

Stable:

```text
Review authentication, input validation, error handling,
data access, and consistency with repository conventions.
```

Variable:

```text
{{TARGET_FILE}}
{{PROJECT_CONTEXT}}
{{USER_GOAL}}
```

Do not hard-code temporary details into reusable prompts.

---

# 8. Role

Use a role when expertise or perspective materially changes the work.

Good:

```text
Act as a senior application security reviewer
for a legacy ASP Classic application.
```

Weak:

```text
You are the world's greatest genius programmer.
```

Roles should affect behavior, not inflate tone.

---

# 9. Objective

State the actual outcome.

Example:

```text
Identify realistic security defects and provide the
smallest maintainable corrections without redesigning
unrelated parts of the application.
```

This combines:

```text
GOAL
+
SCOPE
+
QUALITY BAR
```

---

# 10. Context Contract

Tell the agent which evidence matters.

Example:

```text
Before reviewing the target file:

1. read AGENTS.md
2. inspect relevant authentication code
3. inspect at least one analogous endpoint
4. inspect the application's error-handling convention
```

This is stronger than giving generic advice.

---

# 11. Procedure

Define observable steps.

Example:

```text
1. inspect the target
2. identify trust boundaries
3. trace user-controlled inputs
4. trace data access
5. inspect output encoding
6. identify findings
7. verify findings against project conventions
8. rank by severity
```

Do not require hidden chain-of-thought.

Ask for operations that can be performed and verified.

---

# 12. Do Not Request Private Chain-of-Thought

Avoid instructions such as:

```text
Show every reasoning step.

Reveal your complete chain of thought.

Think step by step and print everything.
```

Instead use:

```text
Inspect the evidence.

Identify assumptions.

Compare alternatives.

State the chosen approach and concise rationale.

Report verification evidence.
```

The workflow matters more than exposing internal reasoning.

---

# 13. Constraints

Define explicit boundaries.

Examples:

```text
Do not introduce a new framework.

Do not change public URL behavior.

Do not replace parameterized SQL with string concatenation.

Do not modify unrelated files.

Do not invent APIs not present in the repository.
```

Negative constraints are useful when they protect important boundaries.

---

# 14. Avoid Giant Ban Lists

Do not add dozens of arbitrary negative rules unless evidence shows they are needed.

Too many constraints can create:

```text
conflicts

instruction dilution

unexpected refusal

poor prioritization
```

Prefer high-value constraints.

---

# 15. Tool Rules

If tools are available, specify when they matter.

Example:

```text
Search the repository before claiming a class does not exist.

Run the build after code changes.

Inspect the diff before completion.
```

Do not describe every possible tool call.

Define behavioral rules.

---

# 16. Verification

A reusable prompt should define success.

Examples:

```text
Run existing tests.

Validate generated JSON.

Compare imported and expected row counts.

Check all referenced files exist.

Verify commands against the target environment.
```

Prompt outputs are stronger when verification is part of the task.

---

# 17. Failure Handling

Define what should happen when the task cannot proceed normally.

Example:

```text
If a critical project fact cannot be discovered,
ask only the minimum targeted question needed.

If testing cannot be performed,
state exactly what remains unverified.
```

This prevents silent guessing.

---

# 18. Interaction Mode

Specify whether the task should be:

```text
AUTONOMOUS

INTERACTIVE

SOCRATIC

REVIEW-ONLY
```

Example:

```text
For learning:
one concept → one drill → wait.

For code generation:
produce the complete implementation unless blocked.
```

Choosing the wrong interaction mode is a common prompt failure.

---

# 19. Persona Design

For reviewer or adversarial prompts, define incentives rather than simply saying:

```text
Be critical.
```

Better:

```text
You are the operations engineer responsible for recovering this deployment at 2 AM.

Your primary concern is recoverability.

Treat any irreversible step without rollback as high risk.
```

This creates realistic scrutiny.

---

# 20. Prompt Skeleton

A general reusable structure:

```text
# Role

[Relevant expertise/perspective]

# Objective

[Exact task outcome]

# Inputs

[Required and optional inputs]

# Context

[What must be inspected or understood]

# Procedure

1. [...]
2. [...]
3. [...]

# Constraints

- [...]
- [...]

# Verification

[How correctness is checked]

# Failure Handling

[What to do if blocked or uncertain]

# Output

[Exact expected structure]
```

---

# 21. Test the Prompt

Do not save an important reusable prompt without testing it.

Test at least:

```text
NORMAL CASE

INCOMPLETE CASE

EDGE CASE
```

For high-impact prompts also test:

```text
ADVERSARIAL OR AMBIGUOUS CASE
```

---

# 22. Normal Case Test

Use realistic valid input.

Ask:

```text
Did the prompt produce the intended structure?

Did it follow project constraints?

Was the output useful without manual repair?
```

---

# 23. Incomplete Input Test

Remove important but discoverable context.

Check whether the prompt:

```text
discovers it

asks a precise question

or safely identifies what is missing
```

It should not hallucinate.

---

# 24. Edge Case Test

Use unusual but valid input.

Examples:

```text
empty dataset

very large file

legacy version

missing optional field

unusual directory structure
```

The goal is to expose brittle assumptions.

---

# 25. Ambiguity Test

Give input that could be interpreted multiple ways.

Check whether the prompt:

```text
discovers context

asks a targeted question

or states a safe assumption
```

rather than arbitrarily choosing.

---

# 26. Constraint Test

Try to trigger behavior the prompt forbids.

Example:

If the prompt says:

```text
Do not introduce dependencies.
```

give it a task where adding a package is tempting.

Verify that it respects the boundary.

---

# 27. Output Test

Ensure the output contract is useful.

Ask:

```text
Can another person or agent act directly on this output?
```

If extensive manual restructuring is always needed, improve the contract.

---

# 28. Prompt Refinement

After failure:

```text
PRESERVE FAILED INPUT

PRESERVE FAILED OUTPUT

DIAGNOSE FAILURE

MAKE SMALLEST CHANGE

RERUN SAME TEST
```

Use:

```text
.ai/skills/prompt-debugger/SKILL.md
```

Do not rewrite everything immediately.

---

# 29. Prompt Versioning

Useful production prompts should be versioned.

Example:

```text
security-review_v1.0.md

security-review_v1.1.md
```

Or track version inside the file.

Version changes should reflect meaningful behavior changes.

---

# 30. Prompt Changelog

For important prompts, note:

```text
what changed

why

what failed

how the change was tested
```

Prompt evolution should be evidence-driven.

---

# 31. Extract Prompt from Successful Work

A powerful creation method is:

```text
SUCCESSFUL TASK
WHAT PROCEDURE PRODUCED IT?
WHAT INPUTS MATTERED?
WHAT BOUNDARIES MATTERED?
GENERALIZE
PROMPT
```

Use the reverse-engineer or skill-extractor skill when useful.

---

# 32. Prompt vs Skill Decision

Create a prompt when:

```text
the primary need is a repeatable task launch or output format
```

Create a skill when:

```text
the primary need is a reusable operational procedure
```

Example:

Prompt:

```text
Review this pull request.
```

Skill:

```text
How to conduct a security-focused code review.
```

A prompt may invoke a skill.

---

# 33. Prompt vs AGENTS.md

Do not put every reusable prompt rule into `AGENTS.md`.

Root instructions should define broad behavior.

Task-specific behavior belongs in prompts or skills.

---

# 34. Prompt Composition

Complex tasks may compose:

```text
ROOT INSTRUCTIONS
+
PROJECT CONTEXT
+
SKILL
+
TASK PROMPT
+
CURRENT INPUT
```

This is usually better than one giant permanent prompt.

---

# 35. Information Density

Prefer precise instructions with high information value.

Weak:

```text
Be thoughtful, careful, thorough, professional, excellent,
detailed, accurate, helpful, expert, comprehensive.
```

Better:

```text
Identify blocking defects first.
For each defect cite the affected component,
explain the failure mode, and give the smallest safe correction.
```

Specific behavior beats adjectives.

---

# 36. Tone Constraints

Tone rules should be operational.

Example:

```text
Use direct language.

Avoid unnecessary preamble.

Do not use generic praise.

Prefer concrete examples.
```

Avoid attempting to control every word.

---

# 37. Prompt Anti-Fluff

When writing user-facing output, useful defaults include:

```text
start with the answer

use active voice

prefer concrete nouns and verbs

remove redundant summary

avoid generic corporate filler
```

For a final editing pass use:

```text
.ai/skills/coffee-chat-editor/SKILL.md
```

---

# 38. Examples in Prompts

Examples can strongly constrain behavior.

Use examples when:

```text
format is unusual

distinctions are subtle

output keeps drifting

failure cases are important
```

Avoid dozens of examples.

One or two strong examples are often enough.

---

# 39. Few-Shot Pattern

When examples are useful:

```text
INPUT EXAMPLE

EXPECTED OUTPUT

SECOND DIFFERENT INPUT

EXPECTED OUTPUT
```

Ensure examples demonstrate the rule rather than accidentally hard-coding one domain case.

---

# 40. Self-Improvement

Improve a reusable prompt when real use reveals:

* recurring failure
* repeated manual cleanup
* missing constraints
* poor interaction mode
* weak verification
* excessive output
* insufficient project discovery
* incorrect tool assumptions

Capture evidence before permanent change.

---

# 41. Prompt Deprecation

Retire a prompt when:

```text
its workflow is obsolete

a skill replaces it

a better prompt fully supersedes it

technology changes

it repeatedly causes worse outcomes
```

Do not maintain unused prompts indefinitely.

---

# 42. Output Contract for This Skill

When using Prompt Architect, produce:

```text
PROMPT PURPOSE

INPUT CONTRACT

REUSABLE PROMPT

TEST CASES

EXPECTED BEHAVIOR

KNOWN LIMITATIONS

VERSION
```

For simple prompts, compress this.

---

# 43. Changelog

## 1.0.0

Initial active version.

Introduced:

* role/objective/context design
* stable-vs-variable separation
* observable procedures instead of exposed chain-of-thought
* negative constraints
* output contracts
* failure handling
* interaction modes
* persona incentives
* prompt testing
* versioning
* modular prompt composition

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# Retrospective

Version: 1.0.0
Status: ACTIVE

## Purpose

Review completed work to identify durable lessons, process weaknesses, successful techniques, and possible AI-system improvements.

The retrospective answers:

```text id="rgm7wl"
What did this task teach us that might make future work better?
```

It is intentionally lightweight.

The goal is learning, not bureaucracy.

---

# 1. Trigger Conditions

Use this skill after:

* substantial debugging
* architecture work
* significant feature implementation
* deployment
* database migration
* unusual operational incident
* repeated failed attempts
* complex integration
* a task where the user corrected the process
* work that clearly revealed a reusable procedure

Do not perform a formal retrospective after every trivial change.

---

# 2. Core Loop

```text id="58l91x"
TASK
RESULT
WHAT HAPPENED?
WHAT DID WE LEARN?
IS IT REUSABLE?
CHANGE PROCESS IF JUSTIFIED
```

---

# 3. First Question — Did It Work?

Determine:

```text id="mp9n3l"
Was the requested outcome achieved?
```

Possible:

```text id="zdijj2"
YES

PARTIALLY

NO
```

Do not discuss process improvement while hiding an incomplete result.

---

# 4. What Worked?

Identify procedures that helped.

Examples:

```text id="w4q87q"
searching analogous code first

running a targeted test before full suite

using a staging table

creating rollback before deployment

testing connectivity before changing configuration
```

Successful behavior may be reusable.

---

# 5. What Failed?

Identify:

```text id="348o7o"
failed assumption

failed command

failed design

failed verification

failed prompt

failed interaction
```

Be specific.

Weak:

```text id="x6ro6g"
There were some issues.
```

Better:

```text id="gkrbq7"
The deployment failed because files remained locked
after the application pool stop completed.
```

---

# 6. Wrong Assumptions

Ask:

```text id="3q5v2c"
What did we believe that turned out to be false?
```

Examples:

```text id="2iv9a6"
service stop releases every file lock

CSV always has a header

API retry is harmless

database values are all numeric

test environment matches production
```

Wrong assumptions often become useful future checks.

---

# 7. Missing Information

Ask:

```text id="kmrdy2"
What information would have made the task easier or safer?
```

Then ask:

```text id="ff9e41"
Could the agent have discovered it earlier?
```

If yes, improve discovery.

If no, consider whether Diagnostic Intake should have asked for it.

---

# 8. Verification Effectiveness

Ask:

```text id="jaz043"
What verification caught the problem?
```

Then:

```text id="vnd37i"
Could an earlier or cheaper verification step have caught it sooner?
```

This is a strong source of process improvement.

---

# 9. Debugging Efficiency

For debugging tasks:

```text id="c4wz8x"
Which test reduced uncertainty the most?

Which step was wasted?

Did we make speculative changes?

Could we have isolated the layer earlier?
```

The goal is better diagnostic sequencing.

---

# 10. User Corrections

Review meaningful user corrections.

Ask:

```text id="dmkixc"
Was this correction:

task-specific?

project-specific?

a durable workflow preference?

a general agent rule?
```

Do not overgeneralize.

---

# 11. Repeated Friction

Look for:

```text id="mkyd7q"
manual repeated steps

repeated questions

repeated setup

repeated validation

repeated cleanup

repeated prompt repair
```

These may indicate:

```text id="66x57i"
SKILL

AUTOMATION

TEMPLATE

PROMPT

DOCUMENTATION
```

---

# 12. Reusable Success

Ask:

```text id="tsg0z4"
Did we discover a procedure worth deliberately repeating?
```

If yes:

```text id="h7ts5n"
capture it
```

even if nothing failed.

The system should learn from success.

---

# 13. Reusable Failure Prevention

Ask:

```text id="w5gbwk"
Could a simple future check prevent this failure?
```

Examples:

```text id="7ko948"
validate encoding first

check free disk space

test database connection

compare expected/imported counts

check duplicate key

verify service account permission
```

Prefer prevention over documenting complicated recovery when possible.

---

# 14. Classification

Classify each meaningful lesson.

```text id="7xszjn"
NO PERMANENT CHANGE

LEARNING ENTRY

PROJECT DOCUMENTATION

EXISTING SKILL UPDATE

NEW SKILL

PROMPT UPDATE

NEW PROMPT

TEMPLATE

AGENTS.md

CLAUDE.md

AUTOMATED TEST

SCRIPT / AUTOMATION
```

Use the narrowest correct destination.

---

# 15. No Permanent Change

This is a valid and common result.

Use when:

```text id="zbxtlo"
incident was one-off

existing process already addresses it

lesson is too weak

change would create unnecessary complexity
```

Do not manufacture self-improvement.

---

# 16. Learning Entry

If the observation is promising but not sufficiently proven, add:

```text id="w3vpja"
.ai/state/LEARNINGS.md
```

Do not promote it yet.

---

# 17. Project Documentation

Use when the lesson is project truth.

Example:

```text id="k36g9j"
This application must run under a specific IIS App Pool.
```

That belongs in deployment/project documentation, not a generic skill.

---

# 18. Existing Skill Update

Use when:

```text id="f3du83"
a skill already owns the workflow

AND

the task revealed a missing or weak step
```

Prefer updating the skill over creating a duplicate.

---

# 19. New Skill

Use:

```text id="76d1l5"
.ai/skills/skill-extractor/SKILL.md
```

when a reusable procedure lacks an owner.

Do not create the skill directly from a weak retrospective observation.

---

# 20. Prompt Update

Use when the failure came from how an AI task was framed.

Use:

```text id="vgwecw"
.ai/skills/prompt-debugger/SKILL.md
```

before changing important prompts.

---

# 21. Automated Test

A repeated defect may be best prevented with a test rather than an instruction.

Ask:

```text id="h1x96z"
Can the machine catch this automatically?
```

If yes, automation is often better than asking the agent to remember.

---

# 22. Automation

Repeated deterministic manual work may become a script.

Examples:

```text id="xiqa6n"
validation script

deployment preflight

schema checker

file comparison

health check
```

A process improvement does not have to be another prompt.

---

# 23. Impact Test

Before promoting a lesson, ask:

```text id="4ymuvw"
How often could this recur?

What is the cost of recurrence?

How much effort does prevention add?
```

Prioritize:

```text id="mdczl2"
HIGH FREQUENCY

HIGH CONSEQUENCE

LOW-COST PREVENTION
```

---

# 24. Simplicity Test

Ask:

```text id="09twf4"
Is the proposed improvement simpler than the repeated problem it prevents?
```

A complex control for a rare trivial error may not be worthwhile.

---

# 25. Retrospective Output

A lightweight retrospective may contain:

```text id="j1phbx"
RESULT

WHAT WORKED

WHAT FAILED

KEY LESSON

REUSABLE?

ACTION
```

For simple tasks, this may be only a few lines internally.

---

# 26. Example — Deployment

Result:

```text id="e3ucy5"
successful after retry
```

Worked:

```text id="xyv3o1"
backup and health check
```

Failed:

```text id="9s2slu"
application pool stop did not release all file locks
```

Lesson:

```text id="0y919r"
deployment preflight should test whether target files can actually be replaced
```

Action:

```text id="mj0bom"
update iis-deployment skill
```

---

# 27. Example — Database Import

Result:

```text id="3gorsd"
import completed
```

Problem:

```text id="5vl0k4"
command returned success even though rows were rejected
```

Lesson:

```text id="bwwfbo"
command status alone does not verify data-import completeness
```

Action:

```text id="pzogw1"
add expected/imported/rejected row-count verification
```

---

# 28. Example — Prompt Failure

Result:

```text id="a29v73"
user had to answer eight unnecessary questions
```

Cause:

```text id="jdbees"
prompt asked for broad intake before repository discovery
```

Lesson:

```text id="qjtv1u"
discover first, then ask only undiscoverable critical questions
```

Action:

```text id="kj46v9"
prompt-debugger + diagnostic-intake review
```

---

# 29. Example — No Change

Task:

```text id="367loc"
fix spelling error
```

Result:

```text id="eedqmj"
success
```

Lesson:

```text id="i0eu4b"
none
```

Action:

```text id="za6ao7"
NO PERMANENT CHANGE
```

This is correct.

---

# 30. Avoid Retrospective Inflation

Do not turn every task into:

```text id="2r4vg3"
meeting notes

postmortem

five new skills

ten changelog entries
```

Self-improvement should reduce future work, not create a maintenance tax.

---

# 31. Capture Evidence

When a lesson is promoted, preserve:

```text id="fzkbyv"
what happened

why it mattered

where the improvement went

how it was tested
```

This supports future rollback.

---

# 32. Reuse Feedback

When a previous learning is reused, update:

```text id="hlmd6v"
.ai/state/LEARNINGS.md
```

if appropriate.

Record:

```text id="fj3wkw"
Did it help?

Did it prevent the original problem?

Did it create new friction?
```

This closes the learning loop.

---

# 33. Repeated Retrospective Pattern

If several retrospectives produce related lessons:

```text id="hh9eqw"
MERGE THEM
```

Example:

```text id="mx0jcx"
file locks during deployment

partial copy during deployment

health check missing during deployment
```

may collectively justify:

```text id="og2ap0"
iis-deployment skill
```

rather than three tiny rules.

---

# 34. Self-Improvement

Improve this skill when:

* retrospectives create too much overhead
* meaningful lessons are missed
* too many weak lessons are promoted
* the same classifications are confusing
* learning feedback is not being reused

The retrospective should remain lightweight.

---

# 35. Changelog

## 1.0.0

Initial active version.

Introduced:

* success/failure review
* assumption review
* verification review
* user-correction classification
* repeated-friction analysis
* reusable success capture
* action classification
* impact and simplicity tests
* no-change outcome
* reuse feedback loop

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# Learning Ledger

Version: 1.0.0

## Purpose

This file stores **candidate reusable lessons** discovered during real project work.

It sits between:

```text
OBSERVATION
```

and:

```text
PERMANENT AI-SYSTEM CHANGE
```

Not every observation should become:

* a new rule
* a new skill
* a new prompt
* an `AGENTS.md` update

The learning ledger allows evidence to accumulate first.

---

# 1. Core Principle

Use:

```text
OBSERVE
CAPTURE
REUSE / REPEAT?
PROMOTE
```

instead of:

```text
OBSERVE
IMMEDIATELY ADD PERMANENT RULE
```

This prevents instruction bloat.

---

# 2. What Belongs Here

Capture a learning candidate when real work reveals:

* repeated friction
* repeated user correction
* repeated implementation failure
* repeated debugging procedure
* a strong reusable solution
* missing verification
* missing project guidance
* a useful architectural convention
* an effective prompt technique
* a recurring tool problem
* an obsolete instruction
* a reusable automation opportunity

---

# 3. What Does Not Belong Here

Do not record:

* ordinary typos
* one-off syntax mistakes
* transient task context
* temporary file names
* facts already documented elsewhere
* generic observations with no action
* every successful command
* every minor correction

The ledger should contain signal.

---

# 4. Learning Entry Template

Copy this template for each candidate.

```text
---

## YYYY-MM-DD — Short Descriptive Title

### Observed During

Describe the task or project context.

### Evidence

What actually happened?

Include evidence such as:

- repeated error
- user correction
- failed test
- successful repeated method
- manual workaround
- project convention

### Candidate Lesson

State the possible reusable lesson in one or two clear sentences.

### Scope

Choose one:

- GLOBAL AGENT
- MODEL-SPECIFIC
- PROJECT-SPECIFIC
- SKILL-SPECIFIC
- PROMPT-SPECIFIC
- TEMPLATE
- AUTOMATION
- PROJECT DOCUMENTATION
- UNKNOWN

### Candidate Destination

Examples:

AGENTS.md

CLAUDE.md

.ai/skills/existing-skill/SKILL.md

.ai/skills/new-skill/SKILL.md

.ai/prompts/example.md

.ai/templates/example.md

project documentation

none

### Confidence

LOW

MEDIUM

HIGH

### Repetition Count

1

Increase when the same lesson appears again.

### Action

PENDING

PROMOTED

REJECTED

REVISIT

### Promotion Reason

Complete only if promoted.

### Result After Reuse

What happened when the promoted improvement was used again?

### Related Changelog

Version or changelog entry, if applicable.

---
```

---

# 5. Confidence Guidelines

## LOW

Use when:

```text
one incident

uncertain cause

weak evidence

possible coincidence
```

Do not usually promote a LOW-confidence lesson.

---

## MEDIUM

Use when:

```text
pattern appeared more than once

cause is reasonably understood

lesson appears reusable
```

A MEDIUM lesson may justify a small skill or prompt improvement.

---

## HIGH

Use when:

```text
repeated evidence

clear user correction

reproducible failure

verified project convention

successful repeated procedure
```

HIGH-confidence lessons are strong candidates for promotion.

---

# 6. Repetition Count

Repetition count tracks how often the pattern appears.

Example:

```text
Repetition Count: 1
```

Later:

```text
Repetition Count: 2
```

Then:

```text
Repetition Count: 4
```

Repeated evidence increases confidence.

Do not treat repetition count as the only criterion.

One serious verified failure may justify immediate action.

---

# 7. Promotion Decision

Before promoting a learning, check:

```text
Is it reusable?

Is the cause understood?

Is the lesson actionable?

Does it already exist elsewhere?

Is the proposed destination correct?

Can we test the improvement?

Could the change create broader problems?
```

If not, leave it pending.

---

# 8. Promotion Destinations

## AGENTS.md

Use when the lesson applies to:

```text
most agents

most tasks

repository-wide agent behavior
```

Example:

```text
Always inspect existing database migration scripts before designing a new migration.
```

---

## CLAUDE.md

Use when the lesson specifically concerns Claude behavior.

Example:

```text
Claude should inspect the diff after broad search-and-replace edits.
```

---

## Existing Skill

Prefer this when the lesson extends an existing procedure.

Example:

```text
mirror-audit should check service-account permissions during deployment reviews.
```

---

## New Skill

Create only when:

```text
procedure is reusable

trigger is recognizable

workflow is substantial enough

existing skills do not own it
```

---

## Prompt

Use when the lesson concerns launching or structuring a repeated interaction.

---

## Template

Use when the reusable asset is primarily structural.

---

## Project Documentation

Use when the lesson is a project fact.

Example:

```text
Production deployments must target IIS App Pool ScheduliciousPool.
```

That is not a general agent rule.

---

# 9. Rejection

Reject candidate learning when:

```text
it was a one-off incident

the original diagnosis was wrong

it duplicates existing guidance

it creates more process than value

the project changed

testing shows the proposed improvement is worse
```

Do not delete the entry immediately.

A rejected lesson can provide useful historical context.

Mark:

```text
Action: REJECTED
```

and explain why.

---

# 10. Example: Repeated Project Pattern

## YYYY-MM-DD — ASP controllers require consistent initialization

### Observed During

Several Classic ASP controller implementations.

### Evidence

Four controllers required the same initialization sequence:

1. validate request
2. initialize model/service
3. handle initialization error
4. execute action
5. return normalized response

### Candidate Lesson

Controller creation follows a stable reusable project pattern and should not be rediscovered each time.

### Scope

PROJECT-SPECIFIC

### Candidate Destination

```text
.ai/skills/classic-asp-controller/SKILL.md
```

### Confidence

HIGH

### Repetition Count

4

### Action

PROMOTED

### Promotion Reason

The procedure occurred repeatedly and has a clear input/process/output structure.

### Result After Reuse

Pending.

---

# 11. Example: Bad Candidate

## YYYY-MM-DD — Missing semicolon

### Observed During

JavaScript edit.

### Evidence

A semicolon was accidentally omitted.

### Candidate Lesson

Agents should remember semicolons.

### Confidence

LOW

### Repetition Count

1

### Action

REJECTED

### Reason

Ordinary isolated coding mistake. Existing syntax checking already handles this.

---

# 12. Example: Verification Improvement

## YYYY-MM-DD — Import status needs row-count verification

### Observed During

Automated CSV import into Access.

### Evidence

The import command returned successfully even when some records were rejected.

### Candidate Lesson

Successful command completion is insufficient verification for data imports.

Imports should compare:

```text
expected rows

imported rows

rejected rows
```

### Scope

SKILL-SPECIFIC

### Candidate Destination

```text
.ai/skills/access-csv-import/SKILL.md
```

### Confidence

HIGH

### Repetition Count

2

### Action

PENDING

---

# 13. Example: Prompt Failure

## YYYY-MM-DD — Planning prompt overproduces detail

### Observed During

Several small bug fixes.

### Evidence

The planning prompt generated long multi-section plans for changes involving one or two lines.

### Candidate Lesson

Planning depth should scale with task complexity.

### Scope

PROMPT-SPECIFIC

### Candidate Destination

```text
.ai/prompts/plan.md
```

### Confidence

HIGH

### Repetition Count

3

### Action

PROMOTED

---

# 14. Example: Model-Specific Behavior

## YYYY-MM-DD — Claude loads too many unrelated files

### Observed During

Large repository investigation.

### Evidence

Claude read broad directories despite the target subsystem being known.

### Candidate Lesson

Claude should search first and expand file context incrementally.

### Scope

MODEL-SPECIFIC

### Candidate Destination

```text
CLAUDE.md
```

### Confidence

MEDIUM

### Repetition Count

2

### Action

PROMOTED

---

# 15. Periodic Review

Periodically review PENDING entries.

For each entry decide:

```text
PROMOTE

KEEP PENDING

REJECT

MERGE WITH ANOTHER LEARNING
```

Look especially for several related entries that should become one generalized skill.

---

# 16. Merge Related Learning

Suppose the ledger contains:

```text
PowerShell deployment failed because service wasn't stopped.

PowerShell deployment failed because App Pool was locked.

PowerShell deployment failed because files were still in use.
```

Instead of three skills, generalize:

```text
Safe Windows Application Deployment
```

with:

```text
pre-deployment checks

service/app-pool handling

file lock checks

deployment

restart

health verification

rollback
```

The generalized procedure is the useful asset.

---

# 17. Learning from Success

Do not capture only failures.

Useful successful observations include:

```text
"This audit consistently catches migration problems."

"This three-step debugging process isolated the issue quickly."

"This template produces reliable project updates."

"This deployment validation catches configuration drift."
```

These can become:

* skills
* prompts
* templates
* tests

Success is training data for the AI-OS.

---

# 18. Learning from User Corrections

User corrections are high-value signals.

If the user repeatedly says:

```text
"Don't use React in this project."

"Always use parameterized ADODB commands."

"Each street needs its own worksheet."

"Do not rewrite the whole file."
```

determine whether the correction represents:

```text
task-specific instruction

project rule

persistent workflow preference

general agent improvement
```

Store it in the narrowest correct location.

---

# 19. Do Not Infer Too Much

A single correction does not automatically imply a universal rule.

Example:

```text
User:
"Don't use React for this application."
```

Do not infer:

```text
"User never wants React anywhere."
```

Instead record the project-specific constraint if relevant.

---

# 20. Learning Promotion Workflow

```text
REAL WORK
OBSERVATION
LEARNING ENTRY
MORE EVIDENCE?
GENERALIZE
CHOOSE DESTINATION
MODIFY AI-OS
SYSTEM AUDIT
TEST
CHANGELOG
MARK PROMOTED
```

---

# 21. Ledger Hygiene

Keep this file useful.

Periodically:

* merge duplicate entries
* reject weak stale candidates
* mark promoted lessons
* update reuse results
* remove truly obsolete noise

Do not turn the ledger into an endless event log.

---

# 22. Initial State

No project-specific learning has been recorded yet.

The first entries should come from real project work.

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# [Skill Name]

Version: 0.1.0
Status: CANDIDATE

## Purpose

Describe the reusable job this skill performs.

A good purpose answers:

```text
What class of problem does this skill solve?
```

Keep the purpose specific.

Avoid vague purposes such as:

```text
"Help with development."
```

Prefer:

```text
"Safely import delimited text files into Microsoft Access while validating schema, row counts, errors, and rollback conditions."
```

---

# 1. Trigger Conditions

Use this skill when:

* [trigger]
* [trigger]
* [trigger]

Do not use this skill when:

* [non-trigger]
* [non-trigger]

Trigger conditions should make it obvious when an agent should load the skill.

---

# 2. Inputs

Required inputs:

```text
INPUT 1:
Description

INPUT 2:
Description
```

Optional inputs:

```text
OPTIONAL INPUT 1:
Description
```

If an input can be discovered from the project, inspect the project before asking the user.

---

# 3. Preconditions

Before executing this skill, verify:

```text
[ ] required inputs exist

[ ] relevant project instructions have been read

[ ] required tools or dependencies are available

[ ] destructive actions have appropriate safeguards

[ ] assumptions are documented
```

Add domain-specific preconditions when necessary.

---

# 4. Output Contract

The skill should produce:

```text
OUTPUT 1

OUTPUT 2

OUTPUT 3
```

Define what success looks like.

Avoid vague output descriptions such as:

```text
"Provide a good solution."
```

Prefer:

```text
"Produce a verified PowerShell script, list required permissions, provide rollback steps, and report the exact verification commands used."
```

---

# 5. Procedure

Follow this process.

## Step 1 — Discover

Inspect:

* relevant files
* existing implementations
* configuration
* dependencies
* tests
* documentation

Do not assume project facts that can be discovered.

---

## Step 2 — Understand

Identify:

```text
OBJECTIVE

CURRENT STATE

DESIRED STATE

CONSTRAINTS

DEPENDENCIES

RISKS
```

---

## Step 3 — Execute

Describe the reusable procedure.

Example:

```text
1. validate input
2. inspect destination
3. prepare change
4. execute change
5. capture result
6. verify
```

Keep steps concrete and executable.

---

## Step 4 — Verify

Define verification methods.

Prefer:

```text
automated test

build

static analysis

query

command output

row-count comparison

smoke test

direct inspection
```

A skill without verification is incomplete.

---

## Step 5 — Handle Failure

If verification fails:

```text
STOP

CAPTURE ERROR

IDENTIFY FAILURE POINT

DIAGNOSE

CORRECT ROOT CAUSE

RETRY VERIFICATION
```

Do not continue as though the operation succeeded.

---

# 6. Decision Rules

Add decision rules when the procedure contains branches.

Example:

```text
IF destination exists
inspect compatibility

IF destination does not exist
create it

IF schema differs
stop and reconcile mapping

IF import errors occur
capture rejected rows
```

Prefer explicit decisions over vague instructions.

---

# 7. Safety Rules

Document any important safety boundaries.

Examples:

```text
do not delete source data before verification

do not overwrite production configuration without backup

do not log secrets

do not disable security controls to make a procedure work

do not perform destructive migration without rollback
```

Remove this section if the skill has no meaningful safety considerations.

---

# 8. Failure Handling

Known failure classes:

## Failure Type 1

Symptoms:

```text
...
```

Likely cause:

```text
...
```

Response:

```text
...
```

---

## Failure Type 2

Symptoms:

```text
...
```

Likely cause:

```text
...
```

Response:

```text
...
```

Do not attempt to list every theoretical failure.

Document recurring or high-impact failures.

---

# 9. Verification Checklist

Before considering this skill complete:

```text
[ ] required inputs validated

[ ] procedure completed

[ ] output exists

[ ] expected behavior verified

[ ] errors checked

[ ] regressions considered

[ ] destructive changes validated

[ ] result communicated clearly
```

Customize this checklist for the skill.

---

# 10. Examples

## Example 1 — Normal Case

### Input

```text
...
```

### Process

```text
...
```

### Expected Output

```text
...
```

---

## Example 2 — Edge Case

### Input

```text
...
```

### Expected Handling

```text
...
```

Examples are optional when the procedure is already obvious.

They are strongly recommended for complex skills.

---

# 11. Interaction Rules

Specify whether this skill should:

```text
run autonomously

ask questions

pause before destructive action

operate interactively

produce a complete result
```

Example:

```text
Ask only when a critical variable cannot be discovered.

Do not ask for confirmation for reversible low-risk steps.

Require explicit user approval before destructive production changes.
```

---

# 12. Tool Guidance

If the skill commonly uses specific tools, describe them here.

Example:

```text
Prefer repository search before broad file reads.

Use build/test tools when available.

Use database queries to validate imported row counts.

Use version-control diff inspection before completion.
```

Do not hard-code tools unless they are truly part of the skill.

---

# 13. Related Skills

Related skills:

```text
.ai/skills/[skill]/SKILL.md
```

Use related skills rather than duplicating their full procedures.

Example:

```text
Use mirror-audit before high-risk implementation.

Use red-team after verification.

Use retrospective after substantial work.
```

---

# 14. Self-Improvement Rules

This skill may be improved when:

```text
a recurring failure is discovered

a step is repeatedly unclear

a better verification method is found

a project pattern becomes reusable

the procedure produces unnecessary work
```

Before changing this skill:

```text
1. capture evidence
2. identify the failure or improvement
3. make the smallest useful edit
4. test the revised procedure
5. update the version
6. update this changelog
7. update .ai/CHANGELOG.md when appropriate
```

Do not expand the skill based only on speculative edge cases.

---

# 15. Versioning

Use:

```text
PATCH
```

for:

* clarification
* typo
* small missing check

Use:

```text
MINOR
```

for:

* new behavior
* new procedure branch
* new verification method

Use:

```text
MAJOR
```

when:

* purpose changes
* trigger changes incompatibly
* output contract changes incompatibly
* the skill is fundamentally redesigned

---

# 16. Changelog

## 0.1.0

Initial candidate skill.

### Evidence

[What real work justified creating this skill?]

### Verification

[How was the skill tested?]

### Result

PENDING

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# CLAUDE.md

Version: 1.0.0

## Purpose

This file contains **Claude-specific operating rules** for this repository.

The model-neutral operating system lives in:

```text
AGENTS.md
```

Claude must read and follow `AGENTS.md` first.

Do not duplicate the full contents of `AGENTS.md` here.

This file should contain only behavior that is specifically useful for Claude or Claude Code.

---

# 1. Primary Rule

Before substantial work:

```text
READ AGENTS.md
READ RELEVANT PROJECT DOCUMENTATION
CHECK .ai/skills/
INSPECT THE PROJECT
BEGIN WORK
```

Treat `AGENTS.md` as the primary project-wide AI operating policy.

---

# 2. Inspect Before Editing

Claude should use available repository tools to understand the project before changing it.

Prefer:

```text
SEARCH
READ
INSPECT
TRACE
VERIFY
```

before:

```text
ASSUME
GUESS
REWRITE
```

Before changing code, inspect:

* the target file
* related files
* callers
* dependencies
* configuration
* tests
* similar existing implementations

Do not infer that something does not exist merely because it has not yet appeared in context.

Search for it.

---

# 3. Keep Context Focused

Avoid loading large unrelated portions of the repository into context.

For large projects:

```text
DISCOVER
LOCATE RELEVANT FILES
READ SMALLEST USEFUL SET
FORM APPROACH
MAKE CHANGE
```

Expand context only when evidence shows that additional files are needed.

Do not read the entire repository merely because tools make it possible.

---

# 4. Prefer Repository Evidence

When project facts can be discovered, prefer repository evidence over general knowledge.

Examples:

Instead of assuming:

```text
the project uses SQL Server
```

inspect:

```text
connection strings
configuration
packages
existing database code
```

Instead of assuming:

```text
the application uses Bootstrap
```

inspect:

```text
layout files
package manifests
CSS imports
existing views
```

Repository truth has priority over generic expectations.

---

# 5. Use Existing Patterns

Before introducing a new pattern, search for existing examples.

For example:

```text
new controller
search existing controllers

new service
search existing services

new script
search existing scripts

new test
search existing tests
```

Match established conventions when they are reasonable.

Do not introduce a new architecture merely because it is theoretically cleaner.

---

# 6. Tool Use

Prefer actual tool verification over statements such as:

```text
"This should work."
```

When tools are available:

* run the command
* inspect the output
* inspect the diff
* run the tests
* check the compiler
* inspect the generated file

Do not claim that verification occurred unless it actually occurred.

---

# 7. Editing Discipline

Prefer small, explicit edits.

Use:

```text
SMALL CHANGE
VERIFY
NEXT CHANGE
```

rather than:

```text
LARGE REWRITE
HOPE
```

Avoid rewriting an entire file when a targeted edit is sufficient.

Preserve:

* comments
* formatting
* naming
* public behavior
* architecture
* user-authored documentation

unless the requested change requires modifying them.

---

# 8. Do Not Perform Unrelated Cleanup

When working on a specific task, avoid modifying unrelated code merely because improvements are visible.

Potential unrelated improvements may be:

```text
noted
```

or:

```text
captured in .ai/state/LEARNINGS.md
```

if they are genuinely reusable.

Do not turn every task into a repository-wide refactor.

---

# 9. Plan for Complex Work

For meaningful tasks, create a concise implementation plan before substantial edits.

The plan should normally cover:

```text
OBJECTIVE

FILES

SEQUENCE

RISKS

VERIFICATION
```

Do not produce a long formal plan when the work is simple.

The plan exists to reduce mistakes, not create paperwork.

---

# 10. Use Skills

Before substantial work, check:

```text
.ai/skills/
```

for a relevant skill.

Examples:

```text
unclear requirements
→ diagnostic-intake

architecture proposal
→ mirror-audit

learning request
→ socratic-teacher

failed AI prompt
→ prompt-debugger

high-risk change
→ red-team

successful recurring process
→ skill-extractor
```

Skills are operational procedures.

They do not override stronger project evidence or user requirements.

---

# 11. Create Missing Skills

If Claude encounters a recurring procedure that is not covered by an existing skill, it may create one.

Use:

```text
.ai/skills/skill-extractor/SKILL.md
```

and:

```text
.ai/skills/skill-builder/SKILL.md
```

Before creating a new skill:

```text
SEARCH EXISTING SKILLS
```

Determine whether an existing skill can be extended.

Avoid near-duplicate skills.

---

# 12. Claude Self-Improvement

Claude may improve this file when a durable Claude-specific issue is discovered.

Examples:

```text
Claude repeatedly loads too much context

Claude repeatedly rewrites whole files unnecessarily

Claude repeatedly fails to use a useful repository tool

Claude repeatedly misinterprets a project-specific tool workflow
```

A change to `CLAUDE.md` must:

```text
have evidence

be reusable

apply specifically to Claude

remain concise

not duplicate AGENTS.md

be added to .ai/CHANGELOG.md

pass the system audit
```

If the lesson applies to all agents, update:

```text
AGENTS.md
```

instead.

---

# 13. Self-Improvement Does Not Expand Authority

Claude must not modify this file to grant itself new authority.

Claude must never add rules that allow it to:

```text
ignore the user

skip required approval

disable tests

hide errors

suppress warnings

remove verification

bypass repository requirements

ignore safety requirements

grant itself external permissions

remove changelog requirements
```

Claude-specific optimization is about execution quality.

It is not about increasing autonomy.

---

# 14. Manage Long Tasks Incrementally

For large tasks, divide work into coherent phases.

Example:

```text
PHASE 1
Discover architecture

PHASE 2
Implement core change

PHASE 3
Add tests

PHASE 4
Run verification

PHASE 5
Review documentation

PHASE 6
Retrospective
```

Verify each useful phase before continuing when practical.

Do not accumulate a large stack of unverified edits.

---

# 15. Debugging

When debugging:

```text
REPRODUCE
COLLECT EXACT ERROR
TRACE RELEVANT CODE
FORM HYPOTHESIS
TEST HYPOTHESIS
FIX ROOT CAUSE
VERIFY
```

Prefer evidence-driven debugging over trial and error.

Do not make several speculative fixes simultaneously unless the changes are inseparable.

---

# 16. Search Before Creating

Before creating:

```text
file
class
function
service
configuration
script
skill
prompt
template
```

search for an existing equivalent.

The goal is to prevent:

```text
duplicate functionality
duplicate conventions
duplicate AI guidance
```

---

# 17. Verify Generated Commands

For:

* PowerShell
* shell scripts
* SQL
* deployment commands
* configuration
* build scripts

inspect syntax and environment assumptions carefully.

When execution is possible, test the command.

When execution is not possible, clearly identify what remains unverified.

---

# 18. Code Generation

Generated code should be:

```text
complete enough to use

consistent with project conventions

free of invented APIs

free of unexplained placeholders

minimal in dependencies

easy to maintain
```

Use pseudocode only when:

```text
the user requests it
```

or when the task is explicitly architectural rather than implementation-ready.

---

# 19. Preserve Existing Behavior

When implementing a feature or fix, identify what existing behavior must remain unchanged.

Watch for unintended changes to:

```text
public APIs

database behavior

authentication

authorization

configuration

URLs

file formats

deployment

logging

error handling
```

A successful implementation should not create unrelated regressions.

---

# 20. Diff Review

Before finishing a substantial code change, inspect the resulting diff when possible.

Check for:

```text
accidental deletions

formatting churn

unrelated edits

debug code

temporary files

hardcoded values

secrets

stale comments

missing tests
```

The final diff should tell a coherent story.

---

# 21. Communication During Long Work

For longer tasks, provide useful progress updates.

Good updates explain:

```text
what was discovered

what changed

what failed

what risk was found

what is being verified
```

Avoid narrating every low-level tool call.

The user should understand the direction of the work without being flooded with operational noise.

---

# 22. Surface Problems Early

If Claude discovers:

```text
a broken assumption

an incompatible dependency

a security problem

a data-loss risk

a missing requirement

an existing defect affecting the task
```

surface it as soon as it becomes relevant.

Do not quietly work around important issues without telling the user.

---

# 23. Reasoning Communication

Claude should not expose private internal chain-of-thought.

When useful, provide concise summaries of:

```text
assumptions

decision rationale

tradeoffs

evidence

risks

verification
```

For example:

```text
I chose approach A because the project already uses pattern X and it avoids adding a second dependency.
```

This communicates useful reasoning without exposing hidden internal reasoning traces.

---

# 24. Learning Requests

When the user's goal is mastery rather than immediate output, use:

```text
.ai/skills/socratic-teacher/SKILL.md
```

Do not automatically produce giant tutorials.

Prefer:

```text
ONE CONCEPT
ONE EXERCISE
WAIT
FEEDBACK
```

unless the user explicitly requests a complete explanation or reference document.

---

# 25. Agent Improvement During Normal Work

Claude should quietly watch for reusable improvement opportunities during work.

Examples:

```text
"This validation step keeps catching the same issue."

"This project always initializes controllers this way."

"This deployment command must always run before that one."

"This API repeatedly requires this header."

"This test procedure should be standardized."
```

Do not interrupt the main task merely to create AI-system artifacts.

Finish the relevant work, then evaluate whether the lesson deserves promotion.

---

# 26. Retrospective

Before finishing substantial work, perform a lightweight retrospective.

Consider:

```text
What made this task difficult?

What assumption failed?

What check found the issue?

What should have found it sooner?

Did we discover a reusable procedure?

Should an existing skill change?

Should a new skill exist?

Should AGENTS.md change?

Should CLAUDE.md change?
```

Use:

```text
.ai/skills/retrospective/SKILL.md
```

when appropriate.

---

# 27. Instruction Hygiene

Keep this file Claude-specific.

If this file begins accumulating:

```text
project architecture

business rules

generic coding standards

general AI workflow rules
```

move them to the correct location.

Prefer:

```text
CLAUDE.md
small adapter
```

not:

```text
CLAUDE.md
second giant AGENTS.md
```

---

# 28. Completion Checklist

Before completing substantial work, confirm:

```text
[ ] AGENTS.md was followed.

[ ] Relevant project context was inspected.

[ ] Existing patterns were considered.

[ ] The requested change was completed.

[ ] Verification was actually performed.

[ ] The resulting diff was reviewed when possible.

[ ] Important risks were considered.

[ ] Documentation was updated when required.

[ ] Reusable learning was considered.

[ ] Any AI-system modifications were tested.

[ ] Any AI-system modifications were logged.
```

---

# 29. Core Claude Principle

Claude should strive to leave behind two improvements:

```text
BETTER PROJECT STATE
```

and, when evidence justifies it:

```text
BETTER AGENT PROCESS
```

Do not improve the agent process merely to create more process.

Improve it when doing so makes future work measurably clearer, safer, faster, or more reliable.

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