Agents Md Generator logo

Agents Md Generator

Community
thienanblog
agents-md-generator

Create, audit, or compact repository instructions in AGENTS.md, scoped overrides, and requested tool compatibility files. Use to preserve non-obvious project rules while removing stale, duplicated, or generic guidance.

Overview

Publisherthienanblog
Repositoryawesome-ai-agent-skills
Skill nameagents-md-generator
Stars
66
Forks
21
Bundled files
9
LicenseApache-2.0
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.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Agents Md Generator 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/thienanblog/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/plugins/project-development-skills/skills/agents-md-generator .claude/skills/agents-md-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agents Md Generator 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 Generator 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 Generator 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.

AGENTS.md Generator

Produce a small instruction layer containing verified project facts that materially change agent behavior. General engineering knowledge belongs outside always-loaded instructions.

Working agreement

Follow the user's request and applicable repository instructions over these defaults. Use existing authorization; ask only about missing decisions that materially affect scope, cost, safety, or the result. Continue independent authorized work while awaiting an answer.

Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.

Discover the instruction chain

Inspect existing instructions, their ownership, scopes, and symlinks before editing. Preserve a working shared source such as AGENTS.md pointing to CLAUDE.md; do not replace it solely to impose a preferred filename. Add compatibility files only when the requested tool support needs them. Keep global user configuration outside a repository-instruction task.

For an unfamiliar or multi-tool repository, the bundled detector can locate instruction paths and declared framework/tool signals:

bash
"<skill-directory>/scripts/detect-agent-context" --root "<repository-root>" --format json

On Windows use scripts/detect-agent-context.cmd with the same arguments. Python provides JSON; native Bash/PowerShell fallbacks emit a smaller text report. Resolve scripts relative to this skill, not the target project. Direct file inspection is sufficient for a known, narrow edit.

The detector indexes selected public manifests and instruction/config paths without reading credentials, environment files, or instruction contents. Treat it as an index: read the applicable instructions and verify commands in their source before persisting them. Include global paths only when that investigation is authorized and relevant.

Read discovery.md for detector details or tool-compatibility.md when loading order and supported tool behavior affect the output.

Select durable rules

Keep a rule when it is non-obvious, supported by project evidence, useful across tasks, and concrete enough to follow. Good candidates include exact command runners, generated-file ownership, unusual business boundaries, required checks, and repository-specific deployment or approval constraints.

Remove or rewrite generic advice, personas, duplicated rules, obsolete commands, exhaustive inventories, mandatory questionnaires, unsupported scores, and procedures that conflict with current project intent. Describe actual authorization boundaries; do not invent new approval gates or weaken explicit ones.

Keep detailed design, API, testing, and deployment guidance in their existing owners, linked with a clear read condition. Exclude secrets, home-directory paths, prompt transcripts, task logs, and global tool inventories.

Write or reconcile

Default to one compact shared instruction source. Use nested files for meaningful scope differences supported by the target tools, without repeating parent rules. For Codex, check same-directory AGENTS.override.md precedence before proposing a layout. For requested Claude compatibility, an import or existing symlink can avoid duplicate rules.

Use output-template.md as a menu. Classify existing content as keep, update, move, remove, or unresolved; resolve facts from source, and ask only about conflicts that require the user's decision. Read merge-and-verify.md for an existing file consolidation.

Prefer roughly 80–150 lines for a root file and shorter scoped files, without padding to a target. Review a root above 200 lines or 16 KiB for duplication and misplaced detail; preserve justified requirements while respecting the target tool's actual loading limit. Do not raise a global context limit just to avoid editing.

Use tracked diffs for recovery. Preserve untracked content that would otherwise be lost. Do not create routine backup copies, alter history, or ignore entire tool-configuration directories merely as part of generating instructions.

Verify

Check cited paths and commands, scope/precedence, symlinks, duplicate rules, and unresolved placeholders. Measure line/byte size and ensure the relevant instruction chain fits the target tool. Run repository validators when applicable. Static inspection of a command does not mean it was executed; do not run a destructive command to verify its spelling.

Report material rules kept, changed, moved, or removed, the evidence used, and unresolved conflicts. Avoid a separate discovery report unless the user requested it.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agents Md Generator AI skill do?

Create, audit, or compact repository instructions in AGENTS.md, scoped overrides, and requested tool compatibility files. Use to preserve non-obvious project rules while removing stale, duplicated, or generic guidance.

Why use Agents Md Generator on TypingMind?

Because you install it once and use it with any model. Agents Md Generator 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 Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thienanblog/awesome-ai-agent-skills/tree/main/plugins/project-development-skills/skills/agents-md-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agents Md Generator?

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 Generator?

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

Is the Agents Md Generator AI skill free?

Yes. It is published on GitHub by thienanblog under the Apache-2.0 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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