agent-context-md 0.1.4

Generate short, useful AGENTS.md files without repeating what the repo already says.

agents-md

Generate AGENTS.md context files that actually help AI coding agents.

Demo runs on a JS/TS repo. Simpler repos generate shorter files.

---

ETH Zurich tested AI coding agents across 8 benchmarks with auto-generated context files, hand-written files, and nothing. Auto-generated files made performance worse in 5 of 8 cases. Inference cost jumped 20-23%. Tasks took more steps.

The cause: generators write what agents can already read. Your README, your folder structure, your lint rules — the agent figures all of this out on its own. Putting it in AGENTS.md means reading it again on every task and paying for it each time.

agents-md reads your existing docs before writing anything. Everything the agent can discover on its own gets filtered out. What's left — exact commands with real flags, conventions the agent would get wrong, hard limits — is what ends up in the file.

This repo's own AGENTS.md was generated by agents-md.

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Contents

• Install
• Usage
• Sample output
• How deduplication works
• Quality scoring
• GitHub Action
• What gets detected
• Alternatives
• Development

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Install

pip install agent-context-md

The PyPI package is agent-context-md; the installed command is agents-md.

Zero runtime dependencies. No Node.js, no Docker.

LLM provider SDKs are optional extras — the tool works fully offline without them:

pip install agent-context-md[anthropic]
pip install agent-context-md[openai]
pip install agent-context-md[gemini]

Or run it with uv:

uvx --from agent-context-md agents-md init

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Usage

Generate:

# Static analysis only, no API key needed
agents-md init --no-llm

# With LLM synthesis (picks up key from env)
ANTHROPIC_API_KEY=sk-...  agents-md init
OPENAI_API_KEY=sk-...     agents-md init
GEMINI_API_KEY=...        agents-md init

# Explicit provider + model
agents-md init --provider anthropic --model <model-id>

# Preview without writing anything
agents-md init --no-llm --dry-run --verbose

# Append managed sections to a hand-written file
agents-md init --merge

Lint — works on any AGENTS.md, including hand-written ones:

agents-md lint                         # score ./AGENTS.md
agents-md lint path/to/AGENTS.md       # specific file
agents-md lint --check --threshold 70  # exit 1 if score < 70
agents-md lint --fix                   # remove duplicate and style-rule lines
agents-md lint --json                  # machine-readable output

Keep it current:

agents-md update   # refresh managed sections, leave your notes alone
agents-md diff     # show what changed in the repo since last generation

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Sample output

agents-md init --no-llm on a Next.js + TypeScript repo:

## Stack
- CI: GitHub Actions
- Framework: Next.js 15.3
- Language: TypeScript (strict mode)
- Package Manager: pnpm 10.0.0
- Test Runner: vitest 3.0.0

## Commands
- install: `pnpm install`
- run: `pnpm dev`
- build: `pnpm build`
- test: `pnpm test`
- lint: `pnpm lint`

## Testing
- Full suite: `pnpm test`
- Single test: `pnpm vitest run src/auth/login.test.ts -t "should reject expired tokens"`
- Prefer the narrowest test while iterating. Run the full suite before handing off.

## Boundaries
### Always Do
- Run the narrowest relevant test before handing off a code change.
### Ask First
- Ask before running `pnpm db:migrate` — it touches migrations.
- Ask before deleting files, rewriting history, or changing release metadata.
### Never Do
- Never commit secrets, tokens, or `.env` files.
- Never overwrite a hand-written AGENTS.md without `--force` or explicit confirmation.

Sections are wrapped in <!-- agents-md:start/end:section-name --> markers. agents-md update uses them to refresh individual sections without touching anything you added by hand.

---

How deduplication works

Before writing anything, the tool reads every Markdown file in your project root and docs/. It extracts facts from them — commands, framework mentions, structural notes. Then for each candidate line from the extractors, it asks: does this add anything the agent can't get from those docs?

README says:  "We use pnpm. Run `pnpm install` to get started."
Extractor:    install: pnpm install

→ Removed. The agent can read the README.

README says:  "Run the tests before pushing."
Extractor:    single test: pytest tests/test_auth.py::test_login -xvs

→ Kept. The README is vague. The exact command with flags is new.

Boundaries and security lines are always kept, even when they overlap with the README.

Pass --verbose to see the full list of what was removed and why.

---

Quality scoring

agents-md lint scores any AGENTS.md out of 100 and tells you exactly what to fix.

Criterion	Points
Commands section with 2+ exact commands	10
Commands include real flags (-x, --watch, etc.)	15
Single-test command targeting one file or function	10
Three-tier boundaries (Always / Ask First / Never)	20
Dedicated testing section	10
File under 150 lines	15
No README duplication	15
No linter-owned style rules	5

Score guide:

• 85-100 — Strong, focused agent context with low obvious waste.
• 65-84 — A few improvements would help.
• 45-64 — Key sections are missing or there's redundant content.
• 0-44 — This file is likely slowing agent sessions down.

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GitHub Action

Fail PRs when the score drops below a threshold:

- uses: actions/checkout@v6
- uses: actions/setup-python@v6
  with:
    python-version: "3.13"
- uses: osmaneb23/agents-md/.github/actions/agents-md-lint@v0.1.4
  with:
    path: AGENTS.md
    threshold: "70"
    version: "0.1.4"

The action installs the pinned agent-context-md package version from the version input. Pin to a release tag in normal workflows; security-sensitive workflows can pin the action to a full commit SHA.

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What gets detected

Commands

Scripts and tasks from package.json, Makefile, pyproject.toml (taskipy, poe), Justfile, Taskfile.yml. Single-test commands are inferred for pytest, unittest, Jest, Vitest, Mocha, Cargo, and Go.

Stack

Package manager detected from lock files, never guessed. Framework, language, TypeScript strict mode (from tsconfig), runtime, test runner, linter, type checker, CI system.

Conventions

src/ layout, test naming patterns, TypeScript path aliases, named-export-only modules, barrel files with their import alias, Result<T,E> error-as-value patterns, centralized HTTP wrappers, catch blocks that intentionally return fallback values, .env.example variables, test fixture and factory directories. Only things an agent would plausibly get wrong on the first attempt.

Fingerprint

SHA-256 hashes of key generation inputs stored in a comment at the bottom of the file. agents-md diff tracks manifests, workflows, README/docs, and env examples so it can tell you whether an update is worth running.

Supported: Python, JavaScript/TypeScript, Go, Rust.

---

Alternatives

Tool	What it does	Where it falls short
agents-init (npm)	Sets up AGENTS.md + MCP config + subagents	No dedup filter. Generates the kind of bloated file the ETH study warns against.
GenerateAgents.md (PyPI)	DSPy-based generation, any model	DSPy is a heavy dependency. No dedup.
AGENTS.md_generator (Python)	Safe-by-default skeleton	Intentionally minimal. You still fill it in manually.
Hand-written	Full control	Drifts with the codebase. No quality feedback.

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Design decisions

• Zero runtime dependencies. The core package installs in seconds. LLM SDKs are opt-in extras.
• Non-destructive updates. agents-md update only touches content inside managed markers. lint --fix creates a .bak before writing. init prompts before overwriting.
• No guessing. If the package manager can't be determined from lock files, it's left blank. A wrong command is worse than a missing one.
• Short over padded. The LLM synthesis prompt explicitly tells the model to refuse to pad. A 30-line file beats a 150-line one if the repo is simple.
• No Node.js required. Python only, works anywhere Python runs.

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Development

The project's own AGENTS.md has the authoritative commands, conventions, and boundaries. Check it before changing code or opening a PR.

Before changing scoring weights, deduplication rules, or managed marker formats, open an issue. These are product decisions, not implementation details.

Releases go through PyPI Trusted Publishing via .github/workflows/publish.yml. To publish: set up a pending publisher for project agent-context-md, repository osmaneb23/agents-md, workflow publish.yml, environment pypi, then run the publish workflow.

See CONTRIBUTING.md for PR expectations.

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Sources

• ETH Zurich study: arxiv.org/abs/2602.11988
• AGENTS.md open format: github.com/agentsmd/agents.md
• GitHub's analysis of 2,500+ AGENTS.md files: github.blog
• Augment Code guide: augmentcode.com/guides/how-to-build-agents-md