claude-md-authoring_skill

This skill helps you create and maintain CLAUDE.md project memory files with non-obvious conventions, patterns, and gotchas to guide AI coding agents.
  • Python

78

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill sammcj/agentic-coding --skill claude-md-authoring

  • SKILL.md6.7 KB

Overview

This skill creates and maintains CLAUDE.md project memory files that give AI coding agents concise, non-obvious context about a codebase. It focuses on actionable conventions, recurring gotchas, and architectural decisions that agents cannot reliably infer from source alone. Use it to reduce repeated investigation and speed up agent-driven development tasks.

How this skill works

The skill inspects a repository and identifies gaps where agent conversations repeatedly surface the same questions or mistakes. It structures output into 2–4 XML-style sections (for example <ARCHITECTURE>, <CONVENTIONS>, <GOTCHAS>, <TESTING>) and writes only non-obvious, actionable guidance with references to existing docs. It also enforces the core principles: signal over noise, solve real friction, and avoid duplicating standard documentation.

When to use it

  • Creating a new CLAUDE.md to capture project-specific memory before large-scale agent work
  • Adding an architectural decision or design pattern that agents repeatedly need clarified
  • Documenting recurring bugs, workarounds or non-obvious setup steps encountered during debugging
  • Capturing conventions that linters/README don’t cover (naming quirks, private API markers)
  • When agent conversations show repeated confusion about a particular code area

Best practices

  • Write only non-obvious, actionable items — omit anything inferable from code or README
  • Limit to 2–4 focused sections and use XML-style tags for easy parsing by agents
  • Prefer file path references (e.g. src/auth/refresh.ts) over line numbers
  • Link to existing documentation rather than duplicating schemas or deployment steps
  • Update only for recurring, cross-cutting issues; keep one-off fixes in code comments

Example use cases

  • Add a GOTCHAS entry describing a cache race: 'Acquire lock before checking cache status in src/cache/index.py'
  • Document auth token behaviour: 'Tokens expire after 1 hour; background jobs must call refreshToken() in src/auth/refresh.py'
  • Record a project convention: 'Use _internal suffix for private APIs not caught by linter'
  • Note testing requirements: 'Integration tests require Docker; run make test before merging'
  • Summarise architecture: 'Event-driven system via EventBridge; services communicate through events, not direct calls'

FAQ

Keep entries concise and task-focused: describe what to do, why it matters, and where to look for implementation; avoid full tutorials.

When should I not add something to CLAUDE.md?

Skip obvious facts, one-off fixes, temporary workarounds, and content already covered in README or dedicated docs.

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