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Agentsmd Scaffold

Community
majiayu000
agentsmd-scaffold

Generate or update repository-specific AGENTS.md instruction files from real repo evidence. Use when asked to create, design, scaffold, split, or improve root or scoped AGENTS.md files for Codex/Claude/agent workflows, especially when a repo needs directory-specific rules, validation commands, generated-file boundaries, or a short agent onboarding router.

Overview

Publishermajiayu000
Repositoryspellbook
Skill nameagentsmd-scaffold
Stars
280
Forks
26
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by majiayu000 on GitHub. Read the source before you install it.

Installation

Install the Agentsmd Scaffold 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/majiayu000/spellbook.git /tmp/spellbook
mkdir -p .claude/skills
cp -r /tmp/spellbook/skills/agentsmd-scaffold .claude/skills/agentsmd-scaffold
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agentsmd Scaffold 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 Agentsmd Scaffold 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 Agentsmd Scaffold 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 Scaffold

Use this skill to generate a small, evidence-backed AGENTS.md stack for a repository. The output may be a plan, exact proposed file contents, or applied files when the user explicitly asks to write them.

This skill is for instruction scaffolding. Use repo-agent-context-audit first when the user only asks whether the repo's agent context is healthy.

Operating Contract

Default to a scoped plan before editing. Only write or modify AGENTS.md, CLAUDE.md, WARP.md, hooks, settings, or generated docs when the user has explicitly asked to apply the scaffold.

Direct actions:

  • Run read-only discovery, scanner commands, and repo command inspection.
  • Produce a scoped AGENTS.md plan with evidence and validation commands.
  • Draft exact file contents when the user asks for proposed text.

Escalate before:

  • Creating or editing high-context files when the user only asked for an audit.
  • Rewriting existing AGENTS.md, CLAUDE.md, or WARP.md instead of adding a short pointer or scoped complement.
  • Batch-normalizing multiple repositories.

Evidence-backed pushback:

  • Challenge new scoped files when the directory has no distinct local rules.
  • Challenge guessed commands, ownership, or generated-file rules unless repo evidence supports them.

Feedback loop:

  • Promote repeated false starts into references/scaffold-agents.md, scanner signals, or eval prompts.

Workflow

1. Discover Existing Context

Run the scanner from this skill directory when possible:

bash
python3 scripts/scan_repo_context.py <repo-root>
python3 scripts/scan_repo_context.py <repo-root> --json

Then inspect the files that matter:

  • existing AGENTS.md, CLAUDE.md, WARP.md, .claude/instructions.md, and .github/copilot-instructions.md
  • README.md, CONTRIBUTING.md, package manifests, Makefiles, CI workflows, and documented test commands
  • generated files and their generators
  • high-risk directories such as migrations, deploy scripts, auth, secrets, payments, registry metadata, generated clients, and production operations

Do not infer commands or ownership from names alone. Use scanner output as a lead, then verify with actual files.

2. Choose The Instruction Stack

Read references/scaffold-agents.md before proposing files. Choose the smallest stack that changes agent behavior:

  • root AGENTS.md for repo-wide routing and validation
  • nested AGENTS.md only where directory rules differ from root
  • no nested file for directories that only need ordinary README context
  • no bulk normalization across multiple repos until a few examples have been manually validated

3. Produce A Candidate Plan

Before editing, report:

markdown
## Scoped AGENTS Plan

| Path | Why here | Rules to include | Validation |
|---|---|---|---|
| `AGENTS.md` | <repo evidence> | <root topics> | `<command>` |
| `<dir>/AGENTS.md` | <repo evidence> | <scoped topics> | `<command>` |

## Files To Preserve

- `<existing high-context file>` - <how it will be referenced or left alone>

## Open Facts

- <missing command or ownership fact that cannot be inferred>

4. Scaffold On Request

When applying the scaffold:

  • keep root files short, normally 80-150 lines
  • keep nested files focused on that directory's ownership, source-of-truth rules, and validation commands
  • include real commands and paths, not placeholders, unless the fact is truly missing
  • preserve existing high-context files unless the user requested a rewrite
  • pair every prohibition with a concrete alternative, helper, generator, or command

Decision Gates

SituationAction
Existing CLAUDE.md or WARP.md is already a good routerAdd a short AGENTS.md pointer only if cross-runtime routing helps.
Root instruction file exceeds roughly 200 linesPropose root router plus scoped files or references.
Directory has generated outputsAdd scoped rules naming source of truth and regenerate/check commands.
Directory has distinct safety rulesAdd scoped rules with escalation boundaries.
Directory has ordinary implementation files onlyKeep guidance in root unless conventions differ.
Commands cannot be verified from repo evidenceLeave an open fact instead of guessing.

Gotchas

  • Do not add nested AGENTS.md files for every directory. Add them only where local rules differ from root.
  • Do not guess build, test, lint, or generator commands from framework names. Cite the manifest, CI workflow, script, or docs that prove the command.
  • Do not overwrite an existing CLAUDE.md, WARP.md, or AGENTS.md just to normalize naming. Preserve it, point to it, or propose a split first.
  • Do not put long architecture explanations in root AGENTS.md; route to references or existing docs instead.

Verification

After applying changes:

  • run the repo's narrow validation command for the affected scope
  • run any repo-wide registry, docs, typecheck, lint, or test command named in the new instructions when practical
  • rerun python3 scripts/scan_repo_context.py <repo-root> if using the bundled scanner to confirm scoped files are discoverable

If verification cannot run, report the exact missing precondition and the command that should be run later.

Resources

  • scripts/scan_repo_context.py: read-only scanner for high-context files, command hints, specs, local skills, and scoped AGENTS.md candidates.
  • references/scaffold-agents.md: scaffold selection rules and templates for root, generated metadata, scripts/tools, skill libraries, and tests.
  • evals/evals.json: lightweight prompts for future behavior checks.

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 Agentsmd Scaffold AI skill do?

Generate or update repository-specific AGENTS.md instruction files from real repo evidence. Use when asked to create, design, scaffold, split, or improve root or scoped AGENTS.md files for Codex/Claude/agent workflows, especially when a repo needs directory-specific rules, validation commands, generated-file boundaries, or a short agent onboarding router.

Why use Agentsmd Scaffold on TypingMind?

Because you install it once and use it with any model. Agentsmd Scaffold 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 Agentsmd Scaffold in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/majiayu000/spellbook/tree/main/skills/agentsmd-scaffold. 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 Agentsmd Scaffold?

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 Agentsmd Scaffold?

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

Is the Agentsmd Scaffold AI skill free?

Yes. It is published on GitHub by majiayu000 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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