wiki-llms-txt

Gera arquivos llms.txt e llms-full.txt para documentação de projetos compatível com LLMs, seguindo a especificação llms.txt. Use quando o usuário quiser criar…

npx skills add https://github.com/microsoft/skills --skill wiki-llms-txt

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:

  1. Check for git remote: Run git remote get-url origin
  2. 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
  3. Determine default branch: Run git rev-parse --abbrev-ref HEAD
  4. 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

  1. H1 — Project name (exactly one, required)
  2. Blockquote — Dense, specific summary (required). Must be unique to THIS project.
  3. Context paragraphs — Non-obvious constraints, things LLMs would get wrong without being told
  4. H2 sections — Organized by topic, each with a list of [Title](url): Description entries
  5. "Optional" H2 — Special meaning: links here can be skipped for shorter context
  6. Relative links — All paths relative to wiki directory
  7. Dynamic — ALL content derived from actual wiki pages, not templates
  8. 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 <!-- Sources: --> 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:

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

Output Files

Generate three files:

FilePurposeDiscoverability
./llms.txtRoot discovery fileStandard path per llms.txt spec. GitHub MCP get_file_contents and search_code find this first.
wiki/llms.txtWiki-relative linksFor VitePress deployment and wiki-internal navigation.
wiki/llms-full.txtFull inlined contentComprehensive 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

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development