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Agents Md

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sickn33
agents-md

Create, revise, or audit AGENTS.md files from repository evidence, verified commands, and correctly scoped instructions without overwriting maintainer intent.

Overview

Publishersickn33
Repositoryagentic-awesome-skills
Skill nameagents-md
Stars
46.5K
Forks
6.8K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by sickn33 on GitHub. Read the source before you install it.

Installation

Install the Agents Md AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p .claude/skills
cp -r /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/agents-md .claude/skills/agents-md
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agents Md in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agents Md on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agents Md is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Maintain AGENTS.md from repository evidence

Overview

Create or improve agent instructions that help a coding agent change the repository correctly without rediscovering its workflow. Base every repository-specific command, path, and rule on evidence in the current checkout.

Prefer a focused diff over a wholesale rewrite. There is no universal line limit, required section list, symlink layout, or commit-attribution policy; follow the repository's own needs and maintainer intent.

When to Use

  • The user asks to create, update, shorten, or audit AGENTS.md.
  • A monorepo needs root instructions plus narrower package-level overrides.
  • Existing agent instructions contain stale commands, duplicated policy, or unsupported claims.
  • The user wants to reconcile AGENTS.md with CLAUDE.md, .github/copilot-instructions.md, or other repository instruction files.

Use @agents-generator instead when the task specifically calls for its packaged generation modes, assets, or backup workflow. Use this skill when a maintainer-readable, evidence-first edit is the primary goal.

How It Works

1. Preserve existing intent

Before writing, read every instruction file that applies to the target path, including existing AGENTS.md files and relevant tool-specific files such as CLAUDE.md, GEMINI.md, .github/copilot-instructions.md, and .github/instructions/*.instructions.md.

  • Improve an existing AGENTS.md in place when possible.
  • Preserve accurate maintainer-authored rules and repository-specific policy.
  • Do not replace another tool's instruction file with a symlink unless the user requests it and repository evidence shows identical content is desired.
  • Do not silently choose between conflicting instructions. Follow the higher-priority applicable rule, or ask when the intended policy cannot be established from the repository.

2. Build a bounded evidence map

Inspect only enough of the repository to establish how work is actually done:

  1. Read the project overview and contribution guidance, such as README*, CONTRIBUTING*, and relevant docs.
  2. Read manifests, lockfiles, workspace files, task runners, and build config to identify supported tools and exact commands.
  3. Read CI workflows to learn required checks. Do not assume every CI or deployment job is safe or appropriate to run locally.
  4. Inspect representative source and test files for naming, layout, and test conventions.
  5. Identify generated files, migrations, vendored code, large fixtures, secrets boundaries, and production-only operations.

Prefer rg --files and rg for discovery when available. Track the source of each non-obvious command or rule so unsupported claims do not enter the final file.

3. Choose the instruction scope

Use the root AGENTS.md for repository-wide guidance. Add or revise a nested AGENTS.md only when a subtree has materially different commands, architecture, conventions, or safety boundaries.

Keep shared rules at the root and only differences in nested files. For tools that implement the public AGENTS.md convention, the nearest file in the directory tree controls the working subtree. Do not copy the full root file into every package.

4. Write high-signal guidance

Choose headings that fit the repository instead of forcing a fixed template. Include the following only when supported by evidence:

  • Repository map: the few directories and boundaries an agent must know.
  • Setup and commands: exact install, development, build, lint, type-check, and test commands, with the working directory when it is not obvious.
  • Focused validation: targeted checks for a small change and broader checks required before handoff.
  • Change rules: generated-file ownership, migrations, schemas, APIs, dependencies, and cross-package coordination.
  • Safety boundaries: secrets, production data, destructive commands, deployments, and operations that require explicit authorization.
  • Contribution rules: repository-specific naming, formatting, commit, or pull-request requirements that affect implementation or handoff.

Write direct, testable statements. Prefer:

markdown
- From the repository root, run `npm test -- path/to/file.test.ts` for a focused test.

over:

markdown
- Make sure tests pass and follow best practices.

Link to maintained documentation instead of copying it. Distinguish required checks from optional, slow, privileged, or deployment-only checks.

5. Validate before handoff

  1. Re-read each changed AGENTS.md completely.
  2. Remove contradictions, duplicate rules, placeholders, and stale claims.
  3. Confirm every mentioned file and directory exists.
  4. Cross-check commands against manifests or CI, and run safe, proportionate checks when useful.
  5. If nested files changed, confirm each contains only subtree-specific rules and does not conflict accidentally with the root.
  6. Review the diff as a maintainer: every added line should change an agent's decision or prevent a realistic mistake.

Report the files changed, evidence used, checks actually run, and unresolved uncertainty. Never say a command was tested when it was only read from config.

Examples

Create a focused root file

Evidence found:

  • package-lock.json selects npm.
  • package.json defines lint, typecheck, and test scripts.
  • CI runs those three checks from the repository root.
  • src/generated/ is produced by npm run generate.

A useful result might include:

markdown
# Agent instructions

## Commands
- Run commands from the repository root.
- Install with `npm ci`.
- For handoff, run `npm run lint`, `npm run typecheck`, and `npm test`.

## Generated code
- Do not edit `src/generated/` directly; update its source and run `npm run generate`.

Do not add a package manager, command, or generated-file rule that the evidence did not establish.

Revise without erasing policy

If an existing file has accurate release restrictions but a stale test command, change only the stale command and any directly affected explanation. Preserve the release restrictions, even when a shorter replacement would look cleaner.

Best Practices

  • Keep instructions concise enough to scan, but let repository complexity determine length.
  • Prefer repository-specific decisions over generic engineering advice.
  • Include targeted commands when the project supports them; do not invent a file-scoped invocation for a tool that only supports suite-level runs.
  • State where commands run and whether they modify files.
  • Reference formatter and linter config instead of restating every rule.
  • Make tool-specific files additive when their semantics differ; do not assume all agents interpret formats or precedence identically.

Security & Safety Notes

  • Treat this as a state-changing skill because it may edit repository files. Review the diff before handoff.
  • Never include secrets, credentials, internal URLs, personal data, or local environment values in agent instructions.
  • Do not run deployment, publication, database mutation, history rewrite, or other consequential commands merely to validate documentation.
  • Preserve approval gates already present in repository policy.
  • Do not upload private repository instructions to third-party services. Ask for explicit consent before transmitting any repository content externally.

Common Pitfalls

  • Guessing commands: infer tools from manifests and CI, not popularity.
  • Forcing one layout: a symlink or a fixed section list may erase tool-specific or maintainer-authored guidance.
  • Overwriting an existing file: make the smallest evidence-backed change.
  • Copying the README: include only information that changes agent behavior.
  • Duplicating nested files: keep shared guidance at the root and local differences near the relevant code.
  • Claiming validation that did not run: distinguish inspection from execution in the handoff.

Limitations

  • Repository evidence can be incomplete or contradictory; ask when a material policy choice cannot be resolved safely.
  • A documented command may still require credentials, services, or operating system support that are unavailable locally.
  • AGENTS.md support and precedence vary across coding tools; verify the target tool when interoperability matters.
  • This skill improves instruction quality but cannot prove that every future agent will follow the file correctly.

Related Skills and Tools

  • @agents-generator - packaged generation, dry-run, update, and backup modes.
  • @folder-specific-claude-and-agents-md - deeper guidance for scoped Claude/AGENTS instruction layouts.
  • agents.md - public format and scope guidance.

Frequently asked questions

What does the Agents Md AI skill do?

Create, revise, or audit AGENTS.md files from repository evidence, verified commands, and correctly scoped instructions without overwriting maintainer intent.

Why use Agents Md on TypingMind?

Because you install it once and use it with any model. Agents Md is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agents Md in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sickn33/agentic-awesome-skills/tree/main/plugins/agentic-awesome-skills-claude/skills/agents-md. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agents Md?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agents Md?

As many as you like. As long as a model supports skills, you can use Agents Md with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agents Md AI skill free?

Yes. It is published on GitHub by sickn33 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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