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概要と使いどころ
Orchestrate any tdmcp test-coverage work: run the coverage harness, inspect coverage gaps, plan focused Vitest/msw or bridge tests, delegate coverage writers, verify coverage deltas, and re-run gates. Use whenever the user asks to raise coverage, add broad regression coverage, create a test harness, improve tests, re-run a coverage wave, or fix a coverage gate.
日本語の概要は準備中です。原文の説明を表示しています。
Experimental workflow for finding interrupted coding runs and reporting resume candidates. Use after an agent host restart or an interrupted automation; resuming anything needs the user's literal GO.
日本語の概要は準備中です。原文の説明を表示しています。
Master ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programming patterns for writing clean, efficient JavaScript code. Use when refactoring legacy code, implementing modern patterns, or optimizing JavaScript applications.
日本語の概要は準備中です。原文の説明を表示しています。
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.
日本語の概要は準備中です。原文の説明を表示しています。
Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.
日本語の概要は準備中です。原文の説明を表示しています。
Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest/ai-translation-text, @azure-rest/ai-translation-document). Use when implementing text translation, transliter...
日本語の概要は準備中です。原文の説明を表示しています。
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
日本語の概要は準備中です。原文の説明を表示しています。
Create clear, comprehensive technical documentation for any audience
日本語の概要は準備中です。原文の説明を表示しています。
Run iterative IA deepening, finalize shard, update indexes/tracker, and enforce ambiguity gates before handoff
日本語の概要は準備中です。原文の説明を表示しています。
Derive slices from specs, order by dependencies, write acceptance criteria with citations, and finalize phase/progress artifacts
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Apply the confirmed design direction to all visual surfaces. This skill now bridges the pipeline's placeholder system with the `impeccable` skill's PRODUCT.md and DESIGN.md files. If `{{DESIGN_DIRECTION}}` still appears as a literal placeholder, run `/impeccable teach` to establish design context.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize a single Jetpack Compose @Composable function. Use when the user wants to improve, polish, animate, or refactor ONE Composable — keywords like "optimize this composable", "make this smoother", "add animation to this", "split this composable", "fix recomposition", "this composable is laggy", "polish this UI", or when user pastes/points to a single @Composable function. Analyzes the target for recomposition issues, missing animations, size/concern splits, and stability problems, then iterates with the user via AskUserQuestion to pick which optimizations to apply, then delegates implementation to the kta-compose-developer agent and summarizes changes. Input MUST be exactly one Composable (file path + function name, or function name alone).
日本語の概要は準備中です。原文の説明を表示しています。
Apply Gang of Four design patterns to solve architectural problems in TypeScript. Use when refactoring code architecture, implementing extensible systems, decoupling components, or following SOLID principles. Covers all 22 GoF patterns: Creational (Factory Method, Abstract Factory, Builder, Prototype, Singleton), Structural (Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy), and Behavioral (Chain of Responsibility, Command, Iterator, Mediator, Memento, Observer, State, Strategy, Template Method, Visitor).
日本語の概要は準備中です。原文の説明を表示しています。
Bun runtime, package manager, bundler, and test runner. Use when running scripts with bun, managing packages, serving HTTP with Bun.serve, querying databases with Bun.sql/bun:sqlite/Bun.redis, shell scripting with $, using S3/file I/O, writing tests with bun:test, bundling or compiling to executable, or using any Bun-specific API (spawn, glob, semver, FFI, workers, plugins, HTMLRewriter).
日本語の概要は準備中です。原文の説明を表示しています。
Zod 4 — TypeScript-first schema validation with static type inference. Use when writing Zod schemas, validating data, defining types with Zod, parsing input, creating form validation schemas, defining API request/response schemas, working with z.object, z.string, z.number, z.enum, z.array, z.union, z.discriminatedUnion, z.file, z.jwt, z.email, z.uuid, z.url, z.codec, z.toJSONSchema, z.fromJSONSchema, z.int, z.stringbool, z.templateLiteral, z.record, z.partialRecord, or any other Zod API. Also use when migrating from Zod 3 to Zod 4, or when the user's package.json shows zod@^4. CRITICAL: Always use Zod 4 APIs. Never use deprecated Zod 3 patterns unless user explicitly requests Zod 3 compatibility.
日本語の概要は準備中です。原文の説明を表示しています。
Write a 5-part mini spec (goal, constraints, acceptance criteria, verification, done gate) before starting any multi-step, externally-delivered, or risky task. Triggers when: a request has 3+ steps, touches external delivery, or is easy to declare done too early. NOT for: single lookups, one-line edits, routine acks, or simple factual answers.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Algolia — algolia index management, search relevance tuning, analytics review, and configuration optimization. Covers index settings, ranking criteria, faceting configuration, synonyms, rules, API key management, and usage quotas. Read this skill before any Algolia operations.
日本語の概要は準備中です。原文の説明を表示しています。
Iterative Python via live Jupyter kernel (hamelnb).
日本語の概要は準備中です。原文の説明を表示しています。
Datasets : ProntoQA, FOLIO, ProofWriter, ConditionalQA, StrategyQA Model : GPT-5 (Azure deployment) Config : max_attempts=3 , verify_timeout=10000ms B
日本語の概要は準備中です。原文の説明を表示しています。
Use for Self-Rewriting Core: AST analyze, patch, commit.
日本語の概要は準備中です。原文の説明を表示しています。
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
日本語の概要は準備中です。原文の説明を表示しています。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
日本語の概要は準備中です。原文の説明を表示しています。
TDD for process documentation - test with subagents before writing, iterate until bulletproof
日本語の概要は準備中です。原文の説明を表示しています。