claude-md-creator

CLAUDE / AGENTS Context File Creator

Creates repository-level context files (CLAUDE.md, AGENTS.md) that give coding agents the minimum guidance needed to work correctly in a repo.

Core principle: Only include what the agent cannot easily discover itself.

Empirical research (Gloaguen et al., 2026 — "Evaluating AGENTS.md") shows that LLM-generated context files decrease agent performance by 3% and increase cost by 20-23% when they contain redundant or overly broad content. Human-written, minimal context files improve performance by ~4%. The difference comes down to signal density — every unnecessary line adds cognitive load without helping the agent solve tasks.

Trigger Conditions

Invoke this skill when any of the following occur:

• The /init command is run
• The user asks to create, write, or update a CLAUDE.md or AGENTS.md
• The user mentions "agent context", "initialize project", "set up Claude Code"
• A repo is missing a CLAUDE.md and the user begins a new project setup
• During brainstorming or writing-plans when the repo lacks a context file

What to Include (highest to lowest priority)

1. Build, test, and lint commands (highest impact)

Explicit tool and command mentions are the single most effective instruction type — agents use mentioned tools 1.6x-2.5x more often. Spell out exact commands:

npm run test -- --watch
uv run pytest tests/ -x
make lint && make typecheck

2. Non-obvious environment setup

Env vars, required services, secrets handling, database setup — things the agent would get wrong without being told.

3. Critical constraints (things that cause wrong behavior if violated)

Focus narrowly on constraints where violating them breaks something:

• "Never edit files in generated/ — they're overwritten by codegen"
• "Always run migrations through the ORM, never raw SQL"
• "The legacy/ module uses CommonJS — no ES imports"

4. Repo-specific patterns and anti-patterns

Only patterns unique to this project that differ from standard practice. If it's what any experienced developer would do by default, leave it out.

What to Exclude

These categories have been empirically shown to provide zero benefit or actively hurt agent performance:

Repository overviews and project descriptions

100% of LLM-generated context files included these, yet agents took identical steps to discover files whether the overview existed or not. The agent explores the repo anyway — an overview just adds tokens without saving any work.

Directory trees and file structure listings

Same finding: detailed directory structures don't help agents locate relevant files. They navigate codebases by searching, not by reading maps.

Architecture summaries and design explanations

Broad architecture descriptions don't help agents solve tasks. If there's an architectural constraint that would cause incorrect behavior (e.g., "this is a monorepo — changes to packages/core require rebuilding all dependents"), include the constraint. Skip the explanation of how the architecture works.

Content that duplicates existing documentation

Don't restate what's already in README, docs/, wiki, or inline comments. Redundancy with existing docs is actively harmful — when researchers removed documentation from repos, LLM-generated context files improved performance by 2.7%, proving the duplication was the problem.

Generic best practices

"Write tests", "follow SOLID principles", "use meaningful variable names" — agents already know these. Only include project-specific deviations from standard practice.

Over-constraining requirements

Unnecessary requirements make tasks harder. Every rule you add has a cost — the agent spends reasoning tokens processing it and may over-apply it. Include a constraint only if violating it would cause a real problem in this specific repo.

Process

1. Scan the repo — read key config files (package.json, tsconfig.json, Makefile, CI configs, etc.) and source structure.
2. Identify gaps — what would an agent get wrong without explicit guidance? Focus on commands, env setup, and constraints that cause breakage.
3. Ask minimal questions — only ask about things that can't be inferred from the repo.
4. Draft a short, high-signal context file — aim for under ~50 lines. Every line should pass the test: "would the agent produce incorrect output without this?"
5. Self-assess before presenting — before showing the draft, apply the filter to every line yourself: "Is this discoverable by reading the code, types, or inline comments?" If yes, cut it. If unsure, cut it. Only surface lines that would cause incorrect agent behavior if absent. Do not ask the user to identify redundant items — that is your job.
6. Present the draft for human review — LLM-generated context files without human review consistently underperform. Walk the user through what you kept and briefly state why each section survives the filter (one phrase per section is enough). Ask only questions the codebase cannot answer — e.g., undocumented team conventions, production-only gotchas, or decisions made outside the repo.