개발 스킬

Write a comprehensive product or engineering specification before implementation. Use when the user asks for `/spec`, PRDs, feature specs, API specs, refactor specs, acceptance criteria, user stories, edge cases, or open decision tracking before code is written.

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Transform git commit history into polished user-facing changelogs and release notes. Use when the user wants a CHANGELOG.md from a version tag or date range, weekly product updates, app store release notes, or Keep a Changelog formatted output with internal noise filtered out.

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Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Behavior-preserving refactoring specialist - plans and executes safe incremental restructuring with code smell / SATD / hotspot targeting, characterization-test safety nets, metric and coverage gates, and refactor-only commits. Use for refactor, refactoring, code smell, technical debt, legacy code modernization, extract method, hotspot, and characterization test work.

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Backend specialist for APIs, databases, authentication with clean architecture (Repository/Service/Router pattern). Use for API, endpoint, REST, database, server, migration, and auth work.

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Mobile specialist for Flutter, React Native, and Swift native iOS development. Use for mobile app, Flutter, Dart, React Native, Swift, SwiftUI, iOS, Android, Riverpod, swift-openapi-generator, and widget work.

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Bug diagnosis and fixing specialist - analyzes errors, identifies root causes, provides fixes, and writes regression tests. Use for bug, debug, error, crash, traceback, exception, and regression work.

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Frontend specialist for React, Next.js, TypeScript with FSD-lite architecture, shadcn/ui, and design system alignment. Use for UI, component, page, layout, CSS, Tailwind, and shadcn work.

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Analyze conversation histories from multiple AI tools (Grok, Claude, Codex, Qwen, Cursor, Antigravity) and generate themed daily/period work summaries. Filter by date or time window.

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Figure out the best new thing to work on. Pulls open items from the GitHub project board, weighs assignment and labels, then prioritizes cleanly closeable scoped work and issues that would wrap up a milestone. Use when the user wants to know what to work on next, or run /next.

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Start work on a GitHub issue - assign it, move it to In Progress on the project board, branch from clean main, scan for related issues/PRs and sibling codebase patterns to replicate, then begin implementation directly with no confirmation gate. Use when the user says "work on

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Ableton Live Object Model (LOM) API reference for Python Remote Scripts and control surface development.

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Use when creating, naming, or placing a file or folder in libs/*, or when modifying or adding a file to an existing component (even when the barrel isn't touched) — component vs utility naming, the one-responsibility-per-file layout, when a folder needs an `index.ts` barrel (public API only), and the required set of files a component needs per lib. Load this before scaffolding or restructuring a component so the layout matches the codebase.

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Creates and maintains Figma Code Connect files (`*.figma.tsx`) that map Figma components to code via the parser-based `figma.connect()` API. Use when the user mentions Code Connect, Figma component mapping, design-to-code translation, or asks to create/update .figma.tsx files.

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Configuring or troubleshooting the Nonprofit Success Pack Program Management Module (PMM) - program delivery, cohort setup, service scheduling, service delivery recording, and attendance tracking for beneficiaries. Trigger keywords: program management module, PMM, Program__c, ProgramEngagement__c, ServiceDelivery__c, cohort setup, bulk service delivery, service schedule, service participant. NOT for designing which outcomes and indicators to measure - use admin/program-outcome-tracking-design. NOT for NPSP donor, household and giving-rollup data - use data/nonprofit-data-architecture.

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Configure or troubleshoot Health Cloud Care Programs: CareProgram hierarchy, patient enrollment (CareProgramEnrollee), consent prerequisites, program products (CareProgramEnrolleeProduct), provider associations (CareProgramProvider). Trigger keywords: care program, program enrollment, enrollee, program product, consent for enrollment, patient program outcome. NOT for care plan templates, problems and goals - use admin/care-plan-configuration. NOT for patient records, person accounts and care team roles - use admin/health-cloud-patient-setup.

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Dry-run Trust Magnitude for proposed named skills or suites before curation. Use when deciding whether a proposed suite deserves A/S treatment, when checking Fusion Score structural inputs, when comparing repo stars vs suite component counts, or when asked to appraise a candidate before adding it to the registry.

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Consult this skill BEFORE proposing a fix to any CLI gap or schema gap that touches evidence, trust magnitude, evidence types, evidence grades, the TM formula, or star-level promotion gates. Also read it whenever an issue mentions citations, stars, commits, contributors, views, reviewers, percentile, arxiv, github-stars-own, repo-own, social-signal, benchmark-result, peer-review, self-attestation, fusion-recipe, verifier-attestation, or proxy-containment — those are the 10 canonical evidence types and the raw inputs that feed their formulas. Also read it when asked whether a skill qualifies for a given star level (0★–6★): the Star Bar and promotion gates live in `META.md`, not in the TM methodology page. Reading both prevents proposing a calibration that satisfies the TM grade threshold but violates a Star Bar installability or link requirement.

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Use this skill when the user wants the full existing-registry Trust Magnitude inspector: understand why a named Gaia skill has a particular star rank, what its Trust Magnitude (TM) score is, which evidence rows are contributing (or dead), how far it is from the next grade, or what evidence to add to move the needle. Also use when asked for a full leaderboard of all named skills ranked by TM. Triggers: "why does X have 3 stars", "what's the TM for X", "inspect TM", "explain the score for X", "what evidence would rank up X", "show leaderboard", "rank all skills by TM", "what's dragging down X's score", "dead evidence rows", "how close is X to grade A/S".

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Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/PRD asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

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Explore a feature or CLI, reproduce defects and record bounded findings. Use for "explore X", "find issues with X", or the exploration stage of feature- pipeline; standalone runs return findings without automatically expanding scope.

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Use this skill when the user wants to understand why a named Gaia skill has a particular star rank, what its Trust Magnitude (TM) score is, which evidence rows are contributing (or dead), how far it is from the next grade, or what evidence to add to move the needle. Also use when asked for a full leaderboard of all named skills ranked by TM. Triggers: "why does X have 3 stars", "what's the TM for X", "inspect TM", "explain the score for X", "what evidence would rank up X", "show leaderboard", "rank all skills by TM", "what's dragging down X's score", "dead evidence rows", "how close is X to grade A/S".

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Batch wrapper for /gaia-ingest. Ingests a bounded set of already-verified evidence rows through CLI-only writes, uses --no-build on every row, appraises Trust Magnitude for every affected named skill, then runs exactly one build and validation pass. Use for an L4-approved intake after /ev-pipeline; benchmark-result rows also require a Phase 2B report and explicit human gate approval.

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