Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Use when implementing a feature or multi-file code change - provides structured implementation flow with persona selection, validation, and testing guidance.

Use this skill when verifying factual claims, checking accuracy of statements, or assessing the credibility of information. Trigger phrases: 'fact-check this', 'is this true', 'verify this claim', 'check if this is accurate'. Do NOT use for subjective opinions, legal or medical advice, or claims requiring real-time data or breaking news.

Event-driven architecture patterns with event sourcing, CQRS, and message-driven communication. Use when designing distributed systems, microservices communication, or systems requiring eventual consistency and scalability.

Iterative refinement workflow for polishing code, documentation, or designs through systematic evaluation and improvement cycles. Use when refining drafts into production-grade quality.

Use this skill when building evaluation frameworks to measure LLM quality, safety, accuracy, or alignment including test suites, human eval rubrics, automated evals, and metrics design. Not for training or fine-tuning models. Not for dataset curation or benchmark comparison across publicly available models.

Use this skill when drafting professional or personal emails—cold outreach, follow-ups, internal memos, client communication, or any message that needs to be clear and effective. Trigger phrases: 'write an email to', 'draft a follow-up', 'help me email'. Do NOT use for mass marketing email campaigns (use copywriter skill) or legal/formal notices requiring specific language.

Use when generating, updating, or organizing documentation (component/API docs, project indexes, diagrams, tutorials, learning paths) - provides structured workflows and references for docs generation, indexing, diagrams, and teaching.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks

Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.

Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks

Orchestrate a full documentation health audit across five dimensions: structural health, semantic accuracy, topic completeness, prose quality, and information architecture. Runs each phase in dependency order with phase gating. This skill should be used for pre-release audits, periodic health checks, or comprehensive documentation assessments.

Audit documentation completeness by mapping what a doc set should cover against what it actually covers. Produces a prioritized gap report by topic, not just by file. This skill should be used after shipping features, before releases, or when users report missing documentation.

Validate that claims in documentation match codebase reality. Extracts verifiable assertions (file paths, commands, function references, behavioral claims, dependencies) from markdown docs and checks them against the actual project. Use after code changes, before releases, or when docs feel untrustworthy.

Evaluate documentation information architecture: navigation paths, discoverability, progressive disclosure, cross-linking, and mental model alignment. This skill should be used when restructuring docs, adding new sections, or when users report difficulty finding information.

Use when facing 3+ independent failures that can be investigated without shared state or dependencies - dispatches multiple Claude agents to investigate and fix independent problems concurrently

Use when estimating time, effort, or complexity for features or projects - provides structured estimation workflows with breakdowns, risks, and confidence intervals.

Use when running builds, executing tests, or improving developer experience workflows - provides structured guidance for build/test execution and DX improvement.

Build scalable design systems with design tokens, component APIs, and documentation. Use when creating or evolving component libraries.

Use this skill when reviewing, evaluating, or giving structured feedback on UI designs, wireframes, mockups, or design systems. Trigger phrases: 'critique this design', 'give feedback on my UI', 'review this wireframe', 'what's wrong with this design'. Do NOT use for writing code, implementing designs, or marketing material critique.

Use when invalid data causes failures deep in execution, requiring validation at multiple system layers - validates at every layer data passes through to make bugs structurally impossible

Use this skill when you face a complex or high-stakes decision and need a structured framework to evaluate options objectively. Ideal for career choices, product prioritization, vendor selection, or any multi-criteria trade-off. Not for trivial daily decisions or situations that require licensed professional advice.

Use this skill when designing, cleaning, deduplicating, or documenting datasets for model training and evaluation including schema design, class imbalance handling, and train/val/test splits. Not for running model training or hyperparameter tuning. Not for real-time data pipeline engineering.