Documentation

Critical thinking partner for technical concepts and strategy. Use when the user wants to explore technical ideas, validate assumptions, or develop strategy. Claude researches the user's Obsidian notes to understand their thinking patterns and context, identifies gaps and flawed assumptions, and provides constructive challenge. For AWS topics, Claude also consults AWS documentation to ensure technical accuracy.

Automatically review documents against checklists and best practices. Use when creating or reviewing ADRs, Design Docs, meeting notes, technical proposals, or RFCs. Detects anti-patterns, vague expressions, and missing elements.

Write academic research documents following academic guidelines with peer-reviewed sources from Google Scholar and other academic databases. Always verify source credibility and generate IEEE standard references. Use for research papers, literature reviews, technical reports, theses, dissertations, conference papers, and academic proposals requiring proper citations and scholarly rigor.

Generate initial project planning documents (PVS, ADR, Tech Spec, Roadmap) from a project concept description. Use when starting a new project, when docs/planning/ contains placeholder files, or when user requests project planning document generation.

Generate comprehensive unit tests for source files with 100% coverage target. Use when writing tests for React components, utility functions, hooks, or API routes. Supports Jest and Vitest frameworks following Pandora coding standards.

Conduct comprehensive, systematic literature reviews using multiple databases (PubMed, bioRxiv, Semantic Scholar, OpenAlex). Creates documented searches, synthesizes findings thematically, verifies citations, and generates professional markdown reports with multiple citation styles (APA, Nature, Vancouver). Use when the user needs thorough literature research or types /deep_research.

Generate comprehensive codebase documentation using the Diátaxis framework. Creates tutorials (learning-oriented), how-to guides (problem-solving), reference docs (technical descriptions), and explanations (conceptual). Supports monorepos, multiple languages, and session persistence (resume anytime). Use when: (1) documenting a new codebase, (2) updating existing docs after code changes, (3) user mentions "generate docs", "document this code", "write documentation", (4) creating README, API docs, or user guides.

Design test strategy with concrete test cases. Use before writing tests.

Discovery-driven codebase indexer for Claude Code. Analyzes what EXISTS in a project, then documents what developers need to know. Creates CLAUDE.md + claude-docs/ with architecture-aware documentation. Supports any language (JS/TS, Python, Java, Rust, Go, etc.). Use when: user asks to "index project", "document codebase", "set up claude docs", "analyze project structure", "help me understand this codebase", or when starting work on unfamiliar project. Triggers on phrases like "index this", "create docs for claude", "document the codebase", "set up project documentation".

Write secure, performant, and optimized database queries using parameterized queries, eager loading, proper indexing, and transaction management. Use this skill when writing database queries in controllers, repositories, services, or model methods, when using query builders or ORM methods, when implementing filtering/sorting/pagination logic, when optimizing N+1 query problems with eager loading, when working with joins and complex queries, when implementing query caching, or when wrapping related operations in database transactions.

Generate comprehensive async pytest tests for Quart endpoints, database operations, and WebSocket connections. Activates when writing tests or ensuring code coverage.

Documentation standards and patterns

Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

Python development best practices, patterns, and conventions. Use when writing Python code, reviewing .py files, discussing pytest, asyncio, type hints, pydantic, dataclasses, or Python project structure. Triggers on mentions of Python, pytest, mypy, ruff, black, FastAPI, Django, Flask.

Core architecture principles (SSOT, DRY, Anti-Spaghetti) for maintainable code design. Use when planning features, implementing code, or reviewing architecture to prevent duplication and technical debt.

Guide for spec-based agent-driven development using structured requirements (EARS notation), technical design documentation, and implementation planning. Use when users want to build features using a specification-first approach, need to document requirements formally, want to generate implementation plans from specs, or when working on projects that benefit from clear requirement-to-code traceability.

Documentation for Kubernetes Agent Sandbox - a CRD-based system for managing isolated AI agent execution environments. Use for queries about Sandbox CRDs (Sandbox, SandboxTemplate, SandboxClaim, SandboxWarmPool), Python SDK (SandboxClient, SandboxRouter, ComputerUseExtension), network policies, security configurations, and implementation examples. Keywords kubernetes sandbox, agent sandbox, CRD, python sdk, agentic-sandbox-client, isolated environment, gvisor, network policy.

General documentation standards for README, AsciiDoc, and technical documentation

Collaborative idea refinement - turns rough ideas into fully-formed designs through questioning, alternative exploration, and incremental validation. Use before writing code or implementation plans.

Customer-facing documentation