Build optimized, secure, and cache-efficient Docker images following production best practices.

Audit Dockerfiles for security, efficiency, and best practices.

Convert any document format to structured Markdown, JSON, or HTML using IBM Docling with AI-powered layout analysis.

Python document processing library for parsing PDF, DOCX, and 10+ formats with advanced layout understanding, unified document representation, and AI ecosystem integrations (LangChain, LlamaIndex, MCP server)

Google Docs/Sheets management via ACSet condensation. Transforms documents into GF(3)-typed Interactions, tracks comments/cells, detects saturation when all comments resolved. Use for document workflows, spreadsheet automation, or applying ANIMA principles to Workspace documents.

Documentation usage analytics and insights. Integrate with Google Analytics, Algolia analytics, and custom tracking to measure documentation effectiveness, identify content gaps, and optimize user journeys.

Analyzes code changes and identifies documentation gaps. Scans git history, catalogs existing docs, and generates comprehensive analysis reports.

Generates comprehensive changelogs from Conventional Commits, maintains CHANGELOG.md files, and scaffolds project documentation like PRD.md or ADR.md. This skill should be used when creating changelogs, generating release notes, maintaining version history, documenting architectural decisions, or scaffolding project requirements documentation. Use for changelog generation, release notes, version documentation, ADR, PRD, or technical documentation.

> **Triggers** (ANY of these should invoke this skill):

Building, rendering library docs, and deploying docs.cloudposse.com. Use when working with the Docusaurus build process or regenerating auto-generated content.

Documentation check checkpoint for conductor gates. Analyzes code changes to determine if documentation needs updating. Returns structured result with pass/fail status and documentation suggestions.

Consolidate redundant documentation while preserving 100% of valuable content.

Code comments, JSDoc/TSDoc ve changelog best practices.

Comprehensive MDX component patterns (Note, Pitfall, DeepDive, Recipes, etc.) for all documentation types. Authoritative source for component usage, examples, and heading conventions.

Validate consistency across SEED Design component documentation layers (design guidelines in ./docs/content/docs/components, Rootage specs in ./packages/rootage/components, and React docs in ./docs/content/react/components). Use when auditing documentation completeness, before releases, or validating new component docs.

Writing standards, React components, and MDX patterns for docs.cloudposse.com. Use when creating or editing documentation content.

Handles multi-document operations with pattern matching and parallel execution, delegating to docs-manager-skill for each matched document

Guide users through structured co-authoring, improvement, and QA of documentation. Use when users want to write or improve docs such as proposals, technical specs, decision docs, READMEs, guides, API docs, or runbooks; or when they ask for doc review, doc QA, checklists, or templates to make documentation clearer, skimmable, and correct for readers.

Generate hierarchical documentation structures optimized for AI coding agents with minimal token usage. Supports AGENTS.md, agent.d, and custom documentation formats.

Single source of truth and librarian for ALL Claude official documentation. Manages local documentation storage, scraping, discovery, and resolution. Use when finding, locating, searching, or resolving Claude documentation; discovering docs by keywords, category, tags, or natural language queries; scraping from sitemaps or docs maps; managing index metadata (keywords, tags, aliases); or rebuilding index from filesystem. Run scripts to scrape, find, and resolve documentation. Handles doc_id resolution, keyword search, natural language queries, category/tag filtering, alias resolution, sitemap.xml parsing, docs map processing, markdown subsection extraction for internal use, hash-based drift detection, and comprehensive index maintenance.

Orchestrates complete single-document workflows with automatic validation and indexing in a write→validate→index pipeline

README best practices ve proje dokümantasyon şablonları.

Use when adding interactive code examples to React docs.

Generate complete, professional documentation for any project.

Keep documentation in sync with code changes across README, docs sites, API docs, runbooks, and configuration. Use when the user asks to update docs, ensure docs match behavior, or prepare docs for a release/PR.

Automatically regenerate README.md from Betty Framework registries

Automates documentation maintenance. Detects modified files via git diff, creates docs for new files, updates docs for modified files, generates changelog. Use after implementation to keep docs current.

Universal methodology for verifying factual correctness in documentation using WebSearch and WebFetch tools. Covers command syntax verification, version checking, code example validation, API correctness, confidence classification system ([Verified], [Error], [Outdated], [Unverified]), source prioritization, and update frequency rules. Essential for maintaining factual accuracy in technical documentation and educational content

Comprehensive link validation methodology for markdown links including format requirements, path validation, broken link detection, external link verification, and checker implementation patterns

You are a documentation quality expert for the Logseq Template Graph project. Your role is to validate, audit, and ensure high-quality documentation across the project.

Use when writing or editing files in src/content/blog/. Provides blog post structure and conventions.

Use when writing or editing files in src/content/learn/. Provides Learn page structure and tone.

Reference page structure, templates, and writing patterns for src/content/reference/. For components, see /docs-components. For code examples, see /docs-sandpack.

Technical documentation writer for clear, comprehensive docs with incremental generation to prevent crashes. Use when creating API documentation, README files, user guides, or developer onboarding docs. Generates one section at a time (Installation → Usage → API → Configuration).

Automatically applies when writing function docstrings. Uses Google-style format with Args, Returns, Raises, Examples, and Security Note sections for proper documentation.

Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.

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Assess organizational doctrine and universally useful patterns

Use when symfony doctrine fixtures foundry

Frappe DocType creation patterns, field types, controller hooks, and data modeling best practices. Use when creating DocTypes, designing data models, adding fields, or setting up document relationships in Frappe/ERPNext.

Create user-friendly documentation for admin staff and business users. Use when documenting admin features, creating user guides, or writing non-technical how-to guides for Humberto and parts counter staff.

Generate Arc42-based architecture documentation with C4 diagrams. Use when documenting system architecture, creating architecture docs, or explaining how the system is structured.

Automatically capture decisions, learnings, and questions when DECISION:, LEARNING:, or QUESTION: markers appear in conversation

Automatically classify and extract information from construction documents using NLP. Categorize RFIs, submittals, change orders, specifications, and contracts.

document-fetcher

Apply standard document formatting, metadata headers, and markdown structure when creating or updating project documents. Use when writing any document in the knowledge base.

Expert in generating, filling, and assembling PDF documents programmatically for legal, HR, and business workflows.

Instructs AI agents on documentation standards for design docs, folder READMEs, source code interfaces, and test cases

Extract structured metadata from documents using AI. Classify content types, extract topics and tools. Supports async batch processing.

Scan and catalog document collections with metadata extraction, categorization, and statistics. Use for auditing document libraries, preparing for knowledge base creation, or understanding large file collections.