1.3万 件 ・ 関連度順
概要と使いどころ
新規・変更・削除・改善などの要望やレビュー指摘を受けたら、設計や実装に入る前にまず必ず発火させる「要件定義フェーズ」のスキル。依頼者のインテントを読み取り、Why / Done / スコープ / 影響範囲を引き出す。見える挙動が変わる依頼全般が対象で、やることが具体的でも md にまとめられていても発火させ、複数の要望が混ざる依頼ほど積極的に発火させる。発火しないのは、再現条件と期待動作が完全に明確なバグ修正、依存更新・タイポ修正などの定型作業、要件定義と基本設計の文書が両方揃った実装フェーズの続き（メモや TODO があるだけでは除外しない）だけ。
Weekly read-only repository audit. Grades one rotating rubric from .claude/audit-rubrics/, files between zero and five GitHub issues for what it finds, and changes no files. Use when the user asks to audit the repo, or when the audit-weekly workflow invokes it. It never edits, commits, branches, or opens pull requests.
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。
Pick one open GitHub issue off this repository's queue, implement the fix, and open a pull request for it. One issue and one PR per run. Use when the user asks to burn down the issue backlog, or when the issue-burndown workflow invokes it. It never merges, never closes an issue, never edits .github/workflows/, and never acts on an issue from outside the organisation unless a maintainer has opted it in.
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。
Find the canonical Rosetta Stone attribute that best matches a fuzzy description, semantic phrase, or required schema shape. Searches the catalog with pagination, describes the shortlist in one batched call, ranks candidates by name + shape match, and returns the canonical attribute ID plus close alternatives. Use when: "find the X attribute", "what's the graph-edge attribute ID", "look up the email Rosetta Stone attribute", "search the attribute catalog for Y", "which attribute has SOURCE_ID + TARGET_ID + IS_DIRECTED". (narrative-common)
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。
Reconstructed reference notes on US NRC software assurance guidance for digital computer software in nuclear power plant safety systems, built from seven public-domain sources (Regulatory Guide 1.168 Revision 2, July 2013; Regulatory Guides 1.169, 1.170, 1.171, 1.172, and 1.173 Revision 1, July 2013; NUREG-0800 Branch Technical Position 7-14 Revision 6, August 2016; pinned 2026-10-12). Use for software life cycle process questions, software requirements specifications, software configuration management plans, verification and validation with reviews and audits, test documentation and unit testing, and what NRC staff examine in a BTP 7-14 software review of plans, implementation, and design outputs. SCOPE LIMITS: NRC Regulatory Guides and the BTP 7-14 staff review position only; the endorsed IEEE standards (IEEE Std 603-1991, 7-4.3.2-2003, 828-2005, 829-2008, 830-1998, 1008-1987, 1012-2004, 1028-2008, 1074-2006), IEC 60880, IEC 61513, IEC 62138, and IAEA guides are cited by designation and edition only, with no standard text; RG 1.152 Rev 4, BTP 7-18, BTP 7-19, other SRP Chapter 7 sections, and NUREG/CR contractor reports are outside the pack; reflects the pinned 2013 and 2016 editions, not later agency actions; synthesized reference notes, not legal or licensing advice and not a substitute for the source documents. LICENCE: Public Domain (US Government work, 17 U.S.C. § 105).
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。
Knowledge pack reconstructed from JSP 815 Defence Safety Management System V1.3 (Ministry of Defence, Open Government Licence v3.0): the SMS Framework, all 12 Elements with their Expectations and Performance Statement themes, and Annexes A to F plus H. Covers the Defence Safety Function and which JSP applies, leadership and safety culture, organisation and SEMS applicability, legislation and exemption certificates, risk assessment and safety cases, supervision and contracting, competence and letters of delegation, equipment and infrastructure safety, performance and incident management, and assurance, audit, and the ISO 45001 clause mapping. SCOPE LIMITS: single-source (JSP 815 V1.3 with Annex H V1.2); carries no DEF-STAN 00-051 or DEF-STAN 00-970 content, no JSP 375, JSP 816; acquisition safety policy is in jsp-376, no Annex G (Assurance Toolkit), and no ISO 45001 text (clause numbers only). This pack is reconstructed reference notes, not MOD guidance, and has no official status.
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。
Record the statements a journal requires and a reader checks — authors with their CRediT contributions, ethics approval that names the body and the reference number, how consent was taken, funding with grant numbers, conflicts of interest, data availability with an address or a reason, and the use of generative AI — as a validated record that the submission folder prints and the manuscript never carries. Use when a run is heading for submission, when an author asks who should be listed, when consent, approval or data sharing has to be stated, or when the venue's AI policy comes up.
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 building serverless AI SDK for Python endpoints, handling hook approvals, deferring hooks, or resuming runs across requests.
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 acceptance criteria for a Jira story using Given/When/Then scenarios. Use this skill when the user asks to write AC, acceptance criteria, write scenarios, improve a story, add AC to a ticket, or mentions a Jira ticket and wants better requirements. Also triggers on "write better AC", "this story needs AC", "add scenarios to this ticket", or when reviewing a Jira story for completeness.
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。
Break an application or feature into thin vertical user-story slices with clear acceptance criteria and user-observable value. Use when planning a backlog, decomposing a feature, writing implementation-ready stories, or turning requirements into deliverable slices.
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 one explicit escalation pass using the original glaze prompt words verbatim. Use when prompts say `/tw-glaze`, when the first answer feels merely adequate, or when you want the exact original rhetoric preserved while pushing for a materially stronger direction.
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。
Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness 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。
Load Remotion best practices for React video creation, including animations, composition setup, assets, audio, captions, sequencing, transitions, media metadata, Three.js, Tailwind, and rendering patterns. Use when creating, editing, debugging, or reviewing Remotion code.
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。
Conduct a Marty-Cagan-style opportunity assessment interview and produce a ~2-page Opportunity Brief in Confluence. Covers business objective, customer, problem, success metric, current alternatives, assumptions/risks, and what we don't know yet. Triggers on "opportunity brief", "opportunity assessment", "discovery brief", "who is this for", "why are we building this", "validate this idea", "product discovery", or any request to frame a feature before implementation. One question at a time. Records accepted research gaps as structured Confluence Decision pages with bidirectional links.
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。
Mine a codebase for breakthrough, evidence-backed opportunities — features, additions, refactors, simplifications, DX, UX, reliability, performance, or architecture cleanup. Researches project reality first, then forces two escalation gates (reject the merely-adequate idea; expand ambition 10x then collapse to the smallest proof-bearing artifact). Outputs ranked opportunities, an escalation ledger, and one plan seed. Does not implement or create tickets.
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 implementing a new function, adding a feature, fixing a bug, refactoring with test coverage, or writing a new test. Enforces strict outside-in red-green-refactor TDD with one-failure-per-turn discipline, predict-the-failure-before-every-run checks, hardcode-first minimum changes, triangulation, and property-based testing for large input spaces. Triggers on explicit TDD requests and on any task phrased as "implement", "add", "write a function", "build a feature", or "fix a bug" in a test-capable codebase. Reports red/green validation evidence.
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。
Gauntlet for absolute claims (always/never/guaranteed/optimal); pressure-test, then refine with explicit boundaries. Use when users ask to prove or disprove strong certainty claims, request devil's-advocate challenge rounds, or want the /tw-prove-it gauntlet to run all 10 rounds continuously by default.
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。
Audit over-engineered codebases by factoring layers into live obligations, quotienting redundant distinctions, ablating unearned surface, and normalizing survivors while preserving required behavior. Use when change latency or agent difficulty comes from frameworks, plugins, DI, codegen, task runners, config indirection, ORMs, GraphQL, monorepo/infra tooling, or web stacks, or when asked to remove layers. Produces an evidence-backed Reduction Certificate, cuts, migration phases, proof signals, and rollback. Not for one local readability cleanup (use tw-complexity-mitigator).
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。
Mitigate incidental code complexity when control flow is tangled, nesting is deep, names are hard to parse, or reasoning requires cross-file hops. Use when a review stalls on readability, you need an analysis-first refactor plan before edits, or you want essential-vs-incidental verdicts, ranked simplification steps, a small structural sketch, and a TRACE assessment (analysis-only; no edits).
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。
Mine Claude Code session data for analytics — token usage, tool rankings, skill invocations, session search, and prompt history. Use when asked about session stats, token costs, tool frequency, skill usage patterns, or finding past sessions by prompt text.
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 deep planning with critiques and alternatives. Use when facing complex design decisions, cross-platform changes, native module modifications, or API changes requiring thorough analysis.
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。
Load when orchestrating Multi-Agent Debate (MAD), adversarial code reviews, or pull-request deliberation to enforce Response Anonymization, expert persona assignment, and weighted post-hoc voting.
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。
Load when the user asks to be grilled or stress-tested on a plan, OR when starting Tier 0 (Sovereign), Tier 1 (Security), or Tier 2 (Privacy) tasks with underspecified requirements.
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。
Load when configuring GitBook documentation sites, authoring GitBook-flavored markdown pages and custom blocks, integrating OpenAPI/Swagger specs, driving GitBook Change Request workflows (create, review, comment, resolve), or building GitBook platform integrations. Do not use for general repository code reviews, general .NET OpenAPI schema generation, or non-GitBook markdown.
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。