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Provider-independent production workflow for agents assembling, auditing, executing, and handing off ComfyUI node-graph workflows for image, video, upscale, inpaint, conditioning, and batch media generation; use when a task involves ComfyUI workflow JSON/API graphs, model and custom-node inventories, reproducibility, provenance, safety/rights review, local or cloud execution hygiene, or production QA.

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。

Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For Chai prediction, use chai1-structure-prediction.

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。

Chai-1 structure prediction for protein complexes and design validation. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For ESM-based analysis, use esm2-sequence-scoring.

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。

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

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。

Internal — for Boundless team members only. Cross-reference Boundless indexer API data, broker telemetry, and service logs to investigate production and staging operational issues. Use when the user wants to understand why slashings happened on prod/staging, diagnose prover or service failures in deployed environments, correlate market events with broker behavior, investigate fulfillment rate drops, look at prover/service logs, or perform any analysis that requires combining on-chain indexer data with off-chain broker telemetry and CloudWatch logs. Also use when the user asks to "investigate", "diagnose", or "find root cause" for prover, service, or market issues on live networks. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.

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。

Interview an instructor and generate a complete MATLAB and Simulink enabled course in IMS Common Cartridge format. Use when the user asks to create a MATLAB course, Simulink course, MATLAB and Simulink curriculum, MATLAB Course Designer-ready course shell, Common Cartridge course package, .imscc export, MATLAB Exercises with validated .m files, or Simulink Exercises with starter and solution model files. Coordinates IDStack, MATLAB Agentic Toolkit, Simulink Agentic Toolkit, matlab-create-course-activity, simulink-create-course-activity, and the existing matlab-generate-grader-assessments skill.

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 MATLAB Course Designer Simulink Exercise learning activities with starter and solution Simulink model files. Use when the user asks to create a Simulink activity, Simulink Exercise, starter model, solution model, model-based learning activity, or Course Designer-ready Simulink artifact. Uses MATLAB MCP Server tool calls and Simulink Agentic Toolkit guidance to create, inspect, edit, simulate, and validate model files.

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。

Execute approved, bounded work in an isolated implementation conversation and return a structured evidence receipt. Use with inherited history or an explicitly identified source; no particular planning, testing, or review skill is required.

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 and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error 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。

This skill should be used when the user says "/ultrawork", "ultrawork", or wants to run the full specify → execute pipeline automatically with a single command. Automated end-to-end workflow that chains specify and execute skills.

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。

This skill should be used when the user asks to "analyze session", "evaluate skill execution", "check session logs", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.

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。

Process multiple documents in bulk with parallel execution. Use when a user asks to batch process files, convert many documents at once, run parallel file operations, bulk rename, bulk transform, or process a directory of files concurrently. Covers parallel execution, error handling, and progress tracking.

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 algorithmic trading systems — backtesting, strategy development, live execution, and risk management. Use when tasks involve backtesting trading strategies, connecting to exchange APIs (Binance, Alpaca, Interactive Brokers), implementing technical indicators, portfolio optimization, order execution, risk management rules, market data processing, or building trading bots. Covers both crypto and traditional markets.

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。

Database design specialist for schema modeling, query optimization, indexing strategies, and data integrityUse when "database design, schema, indexes, query optimization, migrations, normalization, database scaling, foreign keys, data modeling, database, sql, postgres, mysql, mongodb, schema, indexes, migrations, normalization, optimization" mentioned.

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。

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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。

招聘与猎头 (招聘与猎头 (Recruiting & Executive Search) — 面向甲方招聘负责人/HR 与市场化猎头顾问「双侧视角」的『把一个模糊的用人需求，变成一个来得了、干得好、留得住的人』的判断体系；不是候选人一侧的求职与简历面经（那是 job-hunting-coach 的范围），也不是薪酬绩效/员工关系/组织发展的泛 HR 全模块，更不是劳动法条文解读。覆盖：(a) 第一性张力 —— 这行的核心世界观分歧：**需求诊断优先 ⇄ 快速供给优先**（用人经理提来的岗位需求几乎总是一张愿望清单，资深招聘先开 intake 会把『必须有』与『最好有』拆开、把画像逼成可证伪的成功标准；但按成单计酬的猎头把诊断时间压不进单价里，于是行业里同时存在『先诊断再搜寻』和『先扔简历再说』两种作业习惯）；**结构化面试 ⇄ 看人的直觉**（工业与组织心理学近一个世纪的效度元分析一致指向：结构化面试、工作样本、认知能力测验的预测效度明显高于非结构化面谈与经验年限，但一线用人经理绝大多数仍相信自己聊两句就能看准人——这是本行最大的知行落差，也是所有循证招聘流派的出发点；顺带一提，业内长期奉为圭臬的 Schmidt-Hunter 效度排序近年被重新校正，工作样本的效度被下调，这场修正本身就是本行必须如实呈现的争议）；**能力素质 ⇄ 文化匹配**（culture fit 既是最常被写在拒信里的理由，也是被批评得最凶的偏见外壳，Google 一派主张改口叫 culture add，看的是这个人补上了团队缺的东西，而不是像不像我们）；**主动寻访 sourcing ⇄ 被动投递漏斗**（高端与稀缺技能岗只有 outbound 人才地图一条路；量大岗位靠渠道效率、转化率与自动化，两条路的技能树几乎不重叠）；**人才地图是资产 ⇄ 人才地图是浪费**（保留制猎头把 mapping 当交付物的一部分，成功付费猎头认为地图做完单子已经黄了）；**保留制 retained ⇄ 成功付费 contingency**（按阶段预付、独家、深度地图 vs 多家赛马、入职才收钱、速度压倒深度；两种商业模式塑造出两种截然不同的作业节奏、候选人对待方式与职业道德边界，比如 off-limits 不挖回自己安置过的人）；**内部招聘团队 ⇄ 外部猎头与 RPO**（成本、速度、知识沉淀、雇主品牌归属谁的长期争论；embedded recruiter 是近年的折中）；**候选人体验 ⇄ 筛选效率**（漏斗越严体验越差，被拒者也是客户与口碑）；**AI 筛选的效率 ⇄ 合规与偏见**（简历解析、AI 面试评分、自动排序已在大规模使用，同时被监管盯上：纽约市 Local Law 144 要求自动化决策工具做偏见审计并公示、欧盟 AI 法案把招聘划为高风险场景、伊利诺伊州对 AI 视频面试单独立法；业内既离不开它，也在诉讼与审计里学做记录）；**offer 谈判的锚定 ⇄ 薪酬透明**（多地薪酬区间披露立法把谈判起点从『你现在多少』改成『这个岗位值多少』，问薪酬历史在不少法域已被禁止）；**counter-offer 该不该接**（猎头几乎一致反对，认为留下来的人多数一年内还是会走，但这条业内定论的证据基础本身也在被质疑）；**入职后早期流失算谁的**（保证期与退款条款把这场争论直接写进了合同）。(b) 工具栈 —— 申请人追踪系统 ATS（Greenhouse、Lever、Ashby、Workday Recruiting、SmartRecruiters、iCIMS、Taleo，中国侧 Moka、北森、大易、e成）、猎头 CRM 与交付系统（Bullhorn、Vincere、Loxo、Clockwork、Recruit CRM）、寻访与触达（LinkedIn Recruiter、SeekOut、hireEZ、Amazing Hiring、GitHub 与学术检索，中国侧 BOSS 直聘、猎聘、脉脉、拉勾、牛客）、评估与测评（HackerRank、CodeSignal、CoderPad、结构化面试题库与 scorecard 评分卡、Hogan、SHL、Predictive Index、Criteria、案例作业与工作样本）、面试调度与视频（GoodTime、Calendly、HireVue、Spark Hire）、背景调查与合规（HireRight、Checkr、Sterling，中国侧 i背调、八方锦程，以及个人信息保护法与 GDPR 下的授权链、FCRA 的不利行动通知）、数据与漏斗分析（time to fill 与 time to hire 的口径差异、offer 接受率、通过率与转化漏斗、quality of hire 的度量困境、招聘成本 cost per hire 的口径战争）、以及最被低估的纸笔工具（intake 提纲、成功标准清单、scorecard、人才地图表格、Boolean 检索式、一页纸的岗位价值主张）—— 选型的真正分岔从来不是功能清单，而是『你的岗位是靠触达赢还是靠转化赢』。(c) 心智模型层 —— 招聘是一次双向销售而不是一场考试；岗位画像要写成结果（这个人半年内要做成什么）而不是资历清单；信号与噪音（公司名与学历是弱信号，工作样本与具体行为证据是强信号）；结构化与评分卡的意义在于让判断可比而不是显得严谨；漏斗每一层都在丢人，所以要从入职倒推触达量；稀缺人才不看招聘广告，只在熟人与私域里流动；offer 是谈判也是承诺，谈崩与反悔的代价高度不对称；招聘的质量要到入职之后才结算。(d) 工作流 —— intake 需求诊断会、岗位画像与成功标准、渠道策略与人才地图 mapping、Boolean 与主动触达、简历筛选与电话初筛、结构化面试与 debrief 定校准、工作样本与技术评估、reference check 与背调、offer 设计与谈判、counter-offer 应对、入职前 no-show 防范与 pre-boarding、试用期与早期留存复盘、招聘漏斗与数据复盘、以及岗位关闭或撤销时的收尾。(e) 表达 DNA、质量基准（什么算一次好的招聘）、反模式（外行与新手最容易犯的错：照抄 JD、面试问脑筋急转弯、用薪酬历史锚定、把候选人当库存、背调走过场、offer 口头承诺）、智识谱系（工业与组织心理学的效度学派、循证与结构化招聘派、保留制高管寻访派、增长型 sourcing 与招聘运营派、雇主品牌与候选人体验派、中国互联网大厂的人才盘点与赛马派）。) Master OS — automated mastery of 招聘与猎头 (Recruiting & Executive Search) — 面向甲方招聘负责人/HR 与市场化猎头顾问「双侧视角」的『把一个模糊的用人需求，变成一个来得了、干得好、留得住的人』的判断体系；不是候选人一侧的求职与简历面经（那是 job-hunting-coach 的范围），也不是薪酬绩效/员工关系/组织发展的泛 HR 全模块，更不是劳动法条文解读。覆盖：(a) 第一性张力 —— 这行的核心世界观分歧：**需求诊断优先 ⇄ 快速供给优先**（用人经理提来的岗位需求几乎总是一张愿望清单，资深招聘先开 intake 会把『必须有』与『最好有』拆开、把画像逼成可证伪的成功标准；但按成单计酬的猎头把诊断时间压不进单价里，于是行业里同时存在『先诊断再搜寻』和『先扔简历再说』两种作业习惯）；**结构化面试 ⇄ 看人的直觉**（工业与组织心理学近一个世纪的效度元分析一致指向：结构化面试、工作样本、认知能力测验的预测效度明显高于非结构化面谈与经验年限，但一线用人经理绝大多数仍相信自己聊两句就能看准人——这是本行最大的知行落差，也是所有循证招聘流派的出发点；顺带一提，业内长期奉为圭臬的 Schmidt-Hunter 效度排序近年被重新校正，工作样本的效度被下调，这场修正本身就是本行必须如实呈现的争议）；**能力素质 ⇄ 文化匹配**（culture fit 既是最常被写在拒信里的理由，也是被批评得最凶的偏见外壳，Google 一派主张改口叫 culture add，看的是这个人补上了团队缺的东西，而不是像不像我们）；**主动寻访 sourcing ⇄ 被动投递漏斗**（高端与稀缺技能岗只有 outbound 人才地图一条路；量大岗位靠渠道效率、转化率与自动化，两条路的技能树几乎不重叠）；**人才地图是资产 ⇄ 人才地图是浪费**（保留制猎头把 mapping 当交付物的一部分，成功付费猎头认为地图做完单子已经黄了）；**保留制 retained ⇄ 成功付费 contingency**（按阶段预付、独家、深度地图 vs 多家赛马、入职才收钱、速度压倒深度；两种商业模式塑造出两种截然不同的作业节奏、候选人对待方式与职业道德边界，比如 off-limits 不挖回自己安置过的人）；**内部招聘团队 ⇄ 外部猎头与 RPO**（成本、速度、知识沉淀、雇主品牌归属谁的长期争论；embedded recruiter 是近年的折中）；**候选人体验 ⇄ 筛选效率**（漏斗越严体验越差，被拒者也是客户与口碑）；**AI 筛选的效率 ⇄ 合规与偏见**（简历解析、AI 面试评分、自动排序已在大规模使用，同时被监管盯上：纽约市 Local Law 144 要求自动化决策工具做偏见审计并公示、欧盟 AI 法案把招聘划为高风险场景、伊利诺伊州对 AI 视频面试单独立法；业内既离不开它，也在诉讼与审计里学做记录）；**offer 谈判的锚定 ⇄ 薪酬透明**（多地

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。

管理咨询 (管理咨询 (Management Consulting) — 战略与管理咨询的职业认知操作系统，从业者/咨询顾问/想入行者/采购咨询服务的客户视角。覆盖: (a) 第一性张力 — 假设驱动 (hypothesis-driven / answer-first / Day-1 answer) 的「先有答案再验证」⇄ 数据驱动 (data-led / bottom-up) 的「先穷尽事实再归纳」，这行的核心张力是「先射箭再画靶 vs 先画靶再射箭」，资深人偏 top-down 假设树减少 boil-the-ocean，但要时刻防 confirmation bias; 以及更深层的张力 — 「卖洞见 (insight / so-what) vs 卖工时 (analysis / 体力)」，「客户影响 (client impact / 落地) vs 智识严谨 (intellectual rigor / 漂亮 deck)」; (b) 方法论正典 (最标准化、最易蒸出高质量) — MECE (相互独立完全穷尽)、issue tree / logic tree (问题树/逻辑树)、hypothesis-driven problem solving (假设驱动)、Pyramid Principle (Minto 金字塔原理: SCQA 情境-冲突-疑问-回答 + 结论先行 + 自上而下 + 归纳/演绎)、80/20 (帕累托/抓大放小)、so-what (所以呢/洞见提炼)、storyline (故事线/ghost deck 鬼影稿)、driver tree / profit tree、2x2 矩阵、value chain、frameworks (Porter 五力/3C/4P/7S/BCG 矩阵/价值链) 既是脚手架也是陷阱 (套框架 vs 真洞见); (c) 行业结构 — MBB (McKinsey/BCG/Bain 战略三巨头) vs Big Four 咨询臂 (Deloitte/PwC Strategy&/EY-Parthenon/KPMG) vs 精品战略所 (Oliver Wyman/Kearney/Roland Berger/L.E.K./Arthur D. Little) vs 专业/职能 boutique (运营/数字化/PE 尽调); up-or-out (非升即走)、leverage model (杠杆模型: 1 个合伙人带多个 EM/咨询师/分析师的金字塔)、partner/principal/EM(项目经理)/associate(咨询师)/analyst(分析师) 职级; (d) 项目交付模型 (the case/study/engagement) — 提案/SOW (scope of work)、problem definition (问题定义/真问题 vs 表象问题)、workplan (工作计划)、hypothesis tree (假设树)、analysis (分析/建模)、synthesis (综合)、recommendation (建议)、implementation (落地); 客户管理 (steering committee 指导委员会、stakeholder 利益相关方、对齐); (e) 产出物 — the deck (PPT 幻灯片是咨询的母语)、executive summary、action title (行动标题/每页一句话结论)、ghost deck (先搭骨架后填数据)、Excel 财务模型、framework 图; (f) 职业/招聘 — case interview 生态 (案例面试: Victor Cheng LOMS/Case in Point/market sizing 市场估算/估算题/fit 行为面)、网申-笔试-案例面、up-or-out、exit options (退出路径: PE/VC/corp dev 企业发展/战略岗/创业/industry); (g) 争议/批判 — 咨询的价值之辩 (「借你的表告诉你时间」「frameworks 是剧场表演 vs 真洞见」Matthew Stewart《The Management Myth》)、McKinsey 的丑闻 (Purdue 阿片类/独裁政府/ICE/利益冲突, Bogdanich & Forsythe《When McKinsey Comes to Town》)、咨询 vs 自建战略部、generalist 通才 vs specialist 专才之争、AI/GenAI 对初级分析师工作的冲击; (h) 流派 — 战略经典 (Porter 定位学派/五力 vs 资源基础观 RBV vs 蓝海战略 Kim&Mauborgne vs 颠覆式创新 Christensen vs Mintzberg 应急战略批判 planning school); 三巨头气质分野 (McKinsey 事实驱动/CEO 信任/通才, BCG 概念/思想领导力/经验曲线/增长-份额矩阵, Bain 结果导向/NPS 净推荐值/PE 关系/与客户深度绑定)。不含: 投行 M&A/财务顾问 (估值/承销超出范畴)、纯 IT 实施/系统集成 (SI 人力外包)、纯审计/税务、纯人力 RPO/猎头、内部战略部 only (虽相关但本 skill 聚焦专业服务咨询的外部视角)。) Master OS — automated mastery of 管理咨询 (Management Consulting) — 战略与管理咨询的职业认知操作系统，从业者/咨询顾问/想入行者/采购咨询服务的客户视角。覆盖: (a) 第一性张力 — 假设驱动 (hypothesis-driven / answer-first / Day-1 answer) 的「先有答案再验证」⇄ 数据驱动 (data-led / bottom-up) 的「先穷尽事实再归纳」，这行的核心张力是「先射箭再画靶 vs 先画靶再射箭」，资深人偏 top-down 假设树减少 boil-the-ocean，但要时刻防 confirmation bias; 以及更深层的张力 — 「卖洞见 (insight / so-what) vs 卖工时 (analysis / 体力)」，「客户影响 (client impact / 落地) vs 智识严谨 (intellectual rigor / 漂亮 deck)」; (b) 方法论正典 (最标准化、最易蒸出高质量) — MECE (相互独立完全穷尽)、issue tree / logic tree (问题树/逻辑树)、hypothesis-driven problem solving (假设驱动)、Pyramid Principle (Minto 金字塔原理: SCQA 情境-冲突-疑问-回答 + 结论先行 + 自上而下 + 归纳/演绎)、80/20 (帕累托/抓大放小)、so-what (所以呢/洞见提炼)、storyline (故事线/ghost deck 鬼影稿)、driver tree / profit tree、2x2 矩阵、value chain、frameworks (Porter 五力/3C/4P/7S/BCG 矩阵/价值链) 既是脚手架也是陷阱 (套框架 vs 真洞见); (c) 行业结构 — MBB (McKinsey/BCG/Bain 战略三巨头) vs Big Four 咨询臂 (Deloitte/PwC Strategy&/EY-Parthenon/KPMG) vs 精品战略所 (Oliver Wyman/Kearney/Roland Berger/L.E.K./Arthur D. Little) vs 专业/职能 boutique (运营/数字化/PE 尽调); up-or-out (非升即走)、leverage model (杠杆模型: 1 个合伙人带多个 EM/咨询师/分析师的金字塔)、partner/principal/EM(项目经理)/associate(咨询师)/analyst(分析师) 职级; (d) 项目交付模型 (the case/study/engagement) — 提案/SOW (scope of work)、problem definition (问题定义/真问题 vs 表象问题)、workplan (工作计划)、hypothesis tree (假设树)、analysis (分析/建模)、synthesis (综合)、recommendation (建议)、implementation (落地); 客户管理 (steering committee 指导委员会、stakeholder 利益相关方、对齐); (e) 产出物 — the deck (PPT 幻灯片是咨询的母语)、executive summary、action title (行动标题/每页一句话结论)、ghost deck (先搭骨架后填数据)、Excel 财务模型、framework 图; (f) 职业/招聘 — case interview 生态 (案例面试: Victor Cheng LOMS/Case in Point/market sizing 市场估算/估算题/fit 行为面)、网申-笔试-案例面、up-or-out、exit options (退出路径: PE/VC/corp dev 企业发展/战略岗/创业/industry); (g) 争议/批判 — 咨询的

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。

Generate progressive disclosure indexes for GitHub repositories to use as Claude project knowledge. Use when setting up projects referencing external documentation, creating searchable indexes of technical blogs or knowledge bases, combining multiple repos into one index, or when user mentions "index", "github repo", "project knowledge", or "documentation reference".

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。

Manage git worktrees across any repository with the `gwm` Rust binary (CLI + ratatui TUI). Use when the user asks to create / list / remove / bootstrap / switch / link worktrees, run a command across worktrees (`gwm exec`) or reclaim build artifacts (`gwm clean`), materialise a PR into a worktree (`gwm review`), drive a multi-repo workspace (`--workspace`), run the JSON daemon / statusline (`gwm daemon`, `gwm statusline`), list or pin AI-agent sessions per worktree (`gwm agents`, Claude Code / Codex / opencode / Mistral Vibe detection), seed config from a preset (`gwm init --preset`), diagnose with `gwm doctor`, drive tmux/zellij, or wire `gcd` via `gwm shell-init`. Also covers multi-forge (#419): GitHub via `gh` **or** GitLab via `glab`, the `forge` / `[forge_hosts]` keys, and `gwm trust add`. Triggers on `gwm`, `gwq`, `git worktree`, `.gwm.toml`, `forge`, `forge_hosts`, `glab`, `gwm trust add`, GitLab MR, `gwm create`, `gwm list`, `gwm exec`, `gwm clean`, `gwm review`, `gwm daemon`, `gwm statusline`, `gwm agents`, `gwm note`, `gwm bootstrap`, `gwm doctor`, `gwm switch`, `gwm tmux`, `gwm zellij`, `gwm link`, `gwm status`, `gwm shell-init`, `gcd`, `feat/#`, `fix/#`, GitHub issue/PR linking on a worktree.

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。

MBB-level management consultant with mastery over structured thinking, frameworks, guesstimation, industry analysis, and executive-grade deliverables. Use this skill for: solving business problems, structuring cases, market sizing, profitability analysis, market entry, M&A, pricing, operations, digital transformation, org design, and any consulting deliverable (deck, memo, one-pager). Trigger on: "consulting framework", "MECE", "issue tree", "hypothesis-driven", "80/20", "McKinsey", "Bain", "BCG", "strategy consulting", "management consulting", "problem statement", "root cause", "executive summary", "due diligence", "case study", Porter's Five Forces, 3C's, Ansoff, value chain, or any structured business problem-solving and strategic analysis task.

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 a direct path to structured data through ready-made workflows, data APIs, and indexes. Follow the search skill to discover and inspect tools, then the scrape skill to execute them.

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。

Improve executive updates for clarity, decision impact, and concise speaking flow.

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 go-to-market strategy and launch execution skill. Use when planning a product launch, feature announcement, market entry, or release strategy. Activates for: go to market, GTM, product launch, launch strategy, launch plan, launch execution, launch marketing, GTM playbook, launch readiness, technical launch, beta launch, early access, waitlist, Product Hunt, feature release, announcement, how do I launch this, launch checklist, we're about to ship, market entry, startup GTM, PLG vs sales-led, GTM motion, launch marketing pack, press outreach, developer product launch.

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。

How to write — and how to use — a skill that IS one aesthetic rather than a catalogue of them. A catalogue lets an agent pick, and it picks the modal option; a committed aesthetic makes it execute one thing precisely, against rules you can check. Use when a design keeps coming out competent and forgettable, when starting a new brand surface with no reference, or when authoring a house style you want executed the same way twice.

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。