Draft Outlook email replies safely from connected mailbox context. Use when the user wants to reply to a thread, decide whether to reply-all, prepare a draft before sending, or turn the latest Outlook message into a polished response.

Submit compact eQTL Catalogue API requests for association retrieval and documented metadata endpoints. Use when a user wants concise public eQTL Catalogue summaries

Build polished one-day Google Calendar briefs. Use when the user asks for today, tomorrow, or a specific date summary with an agenda, conflict flags, free windows, remaining-meeting readouts, or a calendar brief, and the Google Calendar connector is available.

Route broad or ambiguous life-sciences research requests to the right skills, normalize core entities, optionally parallelize independent evidence gathering with subagents when available, and synthesize a concise evidence-backed answer. Use when a user asks a general life-sciences question that could span multiple sources or analysis types.

Find ways to open up meaningful free time in Outlook Calendar. Use when the user wants to clear part of their schedule, make room for focus time, create a longer uninterrupted block, or see the smallest set of calendar changes that would give time back.

Clean up Outlook newsletters and recurring subscription email safely. Use when the user wants to unsubscribe, separate newsletters from human mail, move recurring senders into folders, or organize low-signal subscription traffic without losing important messages.

Guide for upgrading Stripe API versions and SDKs

Triage a Gmail inbox into actionable buckets such as urgent, needs reply soon, waiting, and FYI using connected Gmail data. Use when the user asks to triage the inbox, rank what needs attention, find what still needs a reply, or separate important mail from noise.

Automatically convert Confluence specification documents into structured Jira backlogs with Epics and implementation tickets. When an agent needs to: (1) Create Jira tickets from a Confluence page, (2) Generate a backlog from a specification, (3) Break down a spec into implementation tasks, or (4) Convert requirements into Jira issues. Handles reading Confluence pages, analyzing specifications, creating Epics with proper structure, and generating detailed implementation tickets linked to the Epic.

Build, run, and debug local macOS apps and desktop executables using shell-first Xcode and Swift workflows. Use when asked to build a Mac app, launch it, diagnose compiler or linker failures, inspect startup problems, or debug desktop-only runtime issues.

Edit SharePoint-hosted spreadsheet files while preserving workbook structure, formulas, and formatting. Use when the user wants to update a real spreadsheet in SharePoint rather than summarize extracted sheet text.

Submit compact ProteomeXchange PROXI requests for datasets, libraries, peptidoforms, proteins, PSMs, spectra, and USI examples. Use when a user wants concise PROXI summaries

Build polished one-day Outlook Calendar briefs. Use when the user asks for today, tomorrow, or a specific date summary with an agenda, conflict flags, free windows, remaining-meeting readouts, or a calendar brief, and Outlook Calendar is available.

Vercel Routing Middleware guidance — request interception before cache, rewrites, redirects, personalization. Works with any framework. Supports Edge, Node.js, and Bun runtimes. Use when intercepting requests at the platform level.

Vercel Flags guidance — feature flags platform with unified dashboard, Flags Explorer, gradual rollouts, A/B testing, and provider adapters. Use when implementing feature flags, experimentation, or staged rollouts.

Use when validating Android feature flows in an emulator with adb-driven launch, input, UI-tree inspection, screenshots, and logcat capture.

* Files inside `./src` are considered user-owned target source code.

QDK programming guide for Q#, OpenQASM, and the qsharp/qdk Python API. Use when: writing or editing Q# or OpenQASM code, running quantum programs, resource estimation, circuit generation, noisy simulation, Azure Quantum submission, Qiskit/Cirq/PennyLane interop, or Q# testing and standard library usage.

The fastest way to test any deployed agent is the `run --url` command — it handles authentication, session creation, and streaming automatically:

Skill with injected eval patterns for security testing

Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or they're trying to figure out what's causing an issue. Concrete actions include isolating failing components, forming and testing hypotheses, analyzing error messages, tracing execution paths, and interpreting test results to narrow down root causes.

Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.

Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.

Guides the red-green-refactor TDD workflow: write a failing test first, implement the minimum code to make it pass, then refactor while keeping tests green. Use when a user asks to practice TDD, write tests first, follow red-green-refactor, do test-driven development, write failing tests before code, or phrases like 'make the test pass', 'test coverage', or 'unit tests before implementation'.

Applies proven testing patterns — Arrange-Act-Assert (AAA), Given-When-Then, Test Data Builders, Object Mother, parameterized tests, fixtures, spies, and test doubles — to help write maintainable, reliable, and readable test suites. Use when the user asks about writing unit tests, integration tests, or end-to-end tests; structuring test cases or test suites; applying TDD or BDD practices; working with mocks, stubs, spies, or fakes; improving test coverage or reducing flakiness; or needs guidance on test organization, naming conventions, or assertions in frameworks like Jest, Vitest, pytest, or similar.

Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are available'.

Guide for creating effective skills using a TDD-based approach. This command treats skill creation as Test-Driven Development applied to process documentation.

Verify skills work under pressure and resist rationalization using the RED-GREEN-REFACTOR cycle. Critical for discipline-enforcing skills.

Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.

Comprehensive guide for skill development based on Anthropic's official best practices - use for complex skills requiring detailed structure

Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration

Create and configure git hooks with intelligent project analysis, suggestions, and automated testing

Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. 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

Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts

Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing

Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes

Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.

Analyze a GitHub issue and create a detailed technical specification

Load all open issues from GitHub and save them as markdown files

Guide for setup Context7 MCP server to load documentation for specific technologies.

Curates insights from reflections and critiques into CLAUDE.md using Agentic Context Engineering

Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis

Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification

Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.

Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with meta-judge evaluation specifications and multi-agent evaluation

creates draft task file in .specs/tasks/draft/ with original user intent

Implement a task with automated LLM-as-Judge verification for critical steps

Refine, parallelize, and verify a draft task specification into a fully planned implementation-ready task

Systematically fix all failing tests after business logic changes or refactoring

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