dotclaudeskills

Use when PRD/TRD contains architectural decisions to document - extracts decisions from refined requirements (architecture choices, data model decisions, API patterns, technology selections, integration strategies, reuse patterns) and generates MADR-format ADRs. One ADR per decision point. Also use when asked to document decisions, create decision records, or retrospectively capture architectural choices.

Use when a plan, PRD, or spec has vague requirements, undefined terms, or missing details - conducts structured interview using AskUserQuestion to surface hidden assumptions, challenge ambiguities, and produce implementation-ready specs. Also use proactively when encountering plans that say things like "make it faster" or "improve UX" without concrete definitions.

Use when converting PRD/requirements into JSON format for Ralph Wiggum autonomous loops. Extracts user stories, orders by dependency (schema first, then backend, then UI, then dashboard), auto-adds standard criteria (typecheck, build, lint, tests), outputs structured JSON ready for autonomous iteration.

Use when auditing code for AI authorship, reviewing acquisitions/contractors, verifying academic integrity, or during code review - provides systematic tiered framework for detecting fully AI-generated AND AI-assisted code patterns with confidence scoring

Use when PRD is prose-heavy, ambiguous, has scattered file paths, missing API contracts, or says "similar to X" without explanation. Transforms rough requirements into implementation-ready specs. Auto-detects tech stack, validates file paths (EXISTS/CREATE/VERIFY markers), handles greenfield and multi-stack projects. Do NOT use for simple bug fixes, typos, or already-structured Jira tickets with clear file paths and acceptance criteria.

Diagnoses and fixes Kubernetes issues with interactive remediation. Use when pods crash (CrashLoopBackOff, OOMKilled), services unreachable (502/503, empty endpoints), deployments stuck (ImagePullBackOff, pending). Also use when tempted to run kubectl fix commands directly without presenting options, or when user says "just fix it" for K8s issues.

Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization

Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

Converts ORM calls to raw SQL and analyzes query performance. Detects N+1 queries, missing indexes, and other anti-patterns. Use when debugging slow queries, tracing ORM-generated SQL, or optimizing database performance for Sequelize, Prisma, TypeORM (Node.js), GORM, sqlc, sqlx, ent (Go), or SQLAlchemy, Django ORM, Peewee (Python).

Analyzes and optimizes SQL/NoSQL queries for performance. Use when reviewing query performance, optimizing slow queries, analyzing EXPLAIN output, suggesting indexes, identifying N+1 problems, recommending query rewrites, or improving database access patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, and Elasticsearch.