AI Development Team Agent Pack This repository is a boilerplate Git repo for running a Markdown-based AI software development team inside software projects.
Vous ne pouvez pas sélectionner plus de 25 sujets Les noms de sujets doivent commencer par une lettre ou un nombre, peuvent contenir des tirets ('-') et peuvent comporter jusqu'à 35 caractères.

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Skill — Clean Code

Purpose

Write simple, readable, testable, maintainable code.

Principles

  • Prefer clarity over cleverness.
  • Prefer explicit contracts over implied behavior.
  • Keep instructions short and routable.
  • Use examples where they prevent mistakes.
  • Avoid duplicating rules that belong in another file.
  • Treat the Product Owner as the final authority.
  • Make work testable.
  • Capture durable lessons.

Process

  1. Understand the task.
  2. Load the smallest useful set of context files.
  3. Identify assumptions.
  4. Produce the requested artifact.
  5. Validate against a checklist.
  6. Suggest improvements only when justified.

Best Practices

  • Use headings that agents can scan.
  • Use checklists for repeatable work.
  • Use tables for routing and decisions.
  • Keep examples minimal and practical.
  • Capture preconditions, postconditions, invariants, and failure modes when behavior matters.
  • Store project-specific facts in project files.
  • Store reusable rules in skill files.
  • Store role behavior in agent files.
  • Store process steps in workflow files.
  • Store reusable document formats in templates.

Anti-Patterns

  • Giant single instruction files.
  • Conflicting rules in multiple places.
  • Vague agent personas with no outputs.
  • Uncontrolled self-modifying instructions.
  • Rewriting standards without approval.
  • Creating documentation no one will use.

Output Standard

Outputs should be:

  • Complete enough to act on
  • Concise enough to reuse
  • Structured in Markdown
  • Easy to review
  • Easy to update

Self-Evolution Protocol

This file is allowed to improve over time, but only through a controlled change process.

When to propose an update

An agent may propose an update when it learns:

  • A recurring mistake should be prevented.
  • A better workflow has been proven useful.
  • A project-specific convention has become stable.
  • A prompt pattern produced better results.
  • A tool, framework, library, or deployment rule changed.
  • The Product Owner approved a new standard.

How to update this file

Agents must not silently rewrite this file. They must create an improvement proposal using:

./.ai/evolution/improvement-proposal-template.md

Every proposal must include:

  • File to update
  • Current problem
  • Proposed change
  • Reason
  • Risk
  • Rollback plan
  • Product Owner approval status

Learning Log

Add durable lessons here only after they are proven useful.

Date Lesson Learned Change Made Approved By
YYYY-MM-DD Initial baseline created. Created file. Product Owner

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