無料GitHub で公開

wiki-llms-txt

Generates llms.txt and llms-full.txt files for LLM-friendly project documentation following the llms.txt specification. Use when the user wants to create LLM-readable summaries, llms.txt files, or make their wiki accessible to language models.

microsoft/skills

インストール方法を見る

含まれるファイル（1）

SKILL.md5.0 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

llms.txt Generator

Generate llms.txt and llms-full.txt files that provide LLM-friendly access to wiki documentation, following the llms.txt specification.

When This Skill Activates

User asks to generate llms.txt or mentions the llms.txt standard
User wants to make documentation "LLM-friendly" or "LLM-readable"
User asks for a project summary file for language models
User mentions llms-full.txt or context-expanded documentation

Source Repository Resolution (MUST DO FIRST)

Before generating, resolve the source repository context:

Check for git remote: Run git remote get-url origin
Ask the user: "Is this a local-only repository, or do you have a source repository URL?"
- Remote URL → store as REPO_URL
- Local → use relative paths only
Determine default branch: Run git rev-parse --abbrev-ref HEAD
Do NOT proceed until resolved

llms.txt Format (Spec-Compliant)

The file follows the llms.txt specification:

# {Project Name}

> {Dense one-paragraph summary — what it does, who it's for, key technologies}

{Important context paragraphs — constraints, architectural philosophy, non-obvious things}

## {Section Name}

- [{Page Title}]({relative-path-to-md}): {One-sentence description of what the reader will learn}

## Optional

- [{Page Title}]({relative-path-to-md}): {Description — these can be skipped for shorter context}

Key Rules

H1 — Project name (exactly one, required)
Blockquote — Dense, specific summary (required). Must be unique to THIS project.
Context paragraphs — Non-obvious constraints, things LLMs would get wrong without being told
H2 sections — Organized by topic, each with a list of [Title](url): Description entries
"Optional" H2 — Special meaning: links here can be skipped for shorter context
Relative links — All paths relative to wiki directory
Dynamic — ALL content derived from actual wiki pages, not templates
Section order — Most important first: Onboarding → Architecture → Getting Started → Deep Dive → Optional

Description Quality

❌ Bad	✅ Good
"Architecture overview"	"System architecture showing how Orleans grains communicate via message passing with at-least-once delivery"
"Getting started guide"	"Prerequisites, local dev setup with Docker Compose, and first API call walkthrough"
"The API reference"	"REST endpoints with auth requirements, rate limits, and request/response schemas"

llms-full.txt Format

Same structure as llms.txt but with full content inlined:

# {Project Name}

> {Same summary}

{Same context}

## {Section Name}

<doc title="{Page Title}" path="{relative-path}">
{Full markdown content — frontmatter stripped, citations and diagrams preserved}
</doc>

Inlining Rules

Strip YAML frontmatter (--- blocks) from each page
Preserve Mermaid diagrams — keep ```mermaid fences intact
Preserve citations — all [file:line](URL) links stay as-is
Preserve tables — all markdown tables stay intact
Preserve  comments — these provide diagram provenance

Prerequisites

This skill works best when wiki pages already exist (via /deep-wiki:generate or /deep-wiki:page). If no wiki exists yet:

Suggest running /deep-wiki:generate first
OR generate a minimal llms.txt from README + source code scan (without wiki page links)

Output Files

Generate three files:

File	Purpose	Discoverability
`./llms.txt`	Root discovery file	Standard path per llms.txt spec. GitHub MCP `get_file_contents` and `search_code` find this first.
`wiki/llms.txt`	Wiki-relative links	For VitePress deployment and wiki-internal navigation.
`wiki/llms-full.txt`	Full inlined content	Comprehensive reference for agents needing all docs in one file.

The root ./llms.txt links into wiki/ (e.g., [Guide](./wiki/onboarding/contributor-guide.md)). The wiki/llms.txt uses wiki-relative paths (e.g., [Guide](./onboarding/contributor-guide.md)).

If a root llms.txt already exists and was NOT generated by deep-wiki, do NOT overwrite it.

Validation Checklist

Before finalizing:

All linked files in llms.txt actually exist
All <doc> blocks in llms-full.txt have real content (not empty)
Blockquote is specific to this project (not generic boilerplate)
Sections ordered by importance
No duplicate page entries across sections
"Optional" section only contains truly optional content
llms.txt is concise (1-5 KB)
llms-full.txt contains all wiki pages

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

ログインしてレビューを書く

同じリポジトリのスキル

概要と使いどころ

agent-framework-azure-ai-py

無料

Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.

日本語の概要は準備中です。原文の説明を表示しています。