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The art of reviewing code that improves both the codebase and the developer - sharing knowledge, maintaining standards, and building cultureUse when "code review, PR review, pull request, merge request, review comments, LGTM, review feedback, approve PR, request changes, review checklist, code quality, review standards, code-review, pull-request, PR, quality, standards, feedback, collaboration, mentoring" mentioned.

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Challenges a recommendation, design, large change, or any artifact submitted for hard review (spec, proposal, policy, plan) across 5 dimensions (consistency, completeness, actionability, alignment, risk), from the standards of a named senior expert in the artifact's domain and ideally from a fresh context or a different model. Searches assuming defects exist and reports only those that survive evidence. Panel mode runs several independent lenses in parallel on a release candidate, verifies every finding against the current head, and routes confirmed ones to file owners. Use before shipping a significant recommendation, design, large branch, or multi-agent release, or when asked to tear something apart, be brutally honest, or poke holes in it.

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。

软件架构 (含 应用架构 / 系统架构 / 分布式 后端架构 / 前后端 数据流 与 数据模型 设计 / API 边界 / 可扩展性 / 演进式架构 — 不含 硬件架构 / 纯企业架构 / 纯 DevOps / 纯安全架构) (software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).) Master OS — automated mastery of software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「软件架构」「系统设计」「system design」「应用架构」「service design」

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。

德州扑克策略 (德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs Exploitative 剥削打法 (针对对手漏洞偏离均衡最大化 EV)，以及二者的实战取舍; (b) solver 时代工作流 — PioSOLVER/GTO Wizard 跑解、范围构建、节点锁定 (node-locking) 做剥削、用 HUD+数据库 (Hold'em Manager/PokerTracker/Hand2Note) 复盘找漏洞 (leak finding); (c) 牌型分层 — 现金局 (cash game) vs 锦标赛 (MTT) 的根本差异，后者叠加 ICM (独立筹码模型) 与跳台/泡沫期博弈; (d) 核心概念栈 — 范围 (range)/权益 (equity)/EV/位置 (position)/翻前开池与 3-bet/4-bet/翻后下注理论 (c-bet、极化 vs 浓缩范围、MDF 最小防守频率、阻断牌 blockers、超池下注 overbet)、SPR、底池赔率; (e) 元层 — 资金管理 (bankroll management)、桌位选择 (table selection)、方差与心态 (mental game/tilt 控制)、抽水 (rake) 与场次选择对长期赢率 (winrate, bb/100) 的影响; (f) 智识演进 — 从 Sklansky 古典手牌价值 → Janda/Tipton 博弈论 → solver 普及 → GTO Wizard 民主化求解 → AI 超人 (Libratus/Pluribus) 对人类策略的反哺。学派分歧: GTO 派 vs 剥削派、现金局 vs 锦标赛、理论自上而下 vs 牌局自下而上、solver 纯学院派 vs 实战感觉派。不含: 线下牌场运营/发牌荷官、博彩合规与赌场管理、扑克之外的牌类 (桥牌/斗地主)、纯概率赌博 (老虎机/轮盘)。) Master OS — automated mastery of 德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs Exploitative 剥削打法 (针对对手漏洞偏离均衡最大化 EV)，以及二者的实战取舍; (b) solver 时代工作流 — PioSOLVER/GTO Wizard 跑解、范围构建、节点锁定 (node-locking) 做剥削、用 HUD+数据库 (Hold'em Manager/PokerTracker/Hand2Note) 复盘找漏洞 (leak finding); (c) 牌型分层 — 现金局 (cash game) vs 锦标赛 (MTT) 的根本差异，后者叠加 ICM (独立筹码模型) 与跳台/泡沫期博弈; (d) 核心概念栈 — 范围 (range)/权益 (equity)/EV/位置 (position)/翻前开池与 3-bet/4-bet/翻后下注理论 (c-bet、极化 vs 浓缩范围、MDF 最小防守频率、阻断牌 blockers、超池下注 overbet)、SPR、底池赔率; (e) 元层 — 资金管理 (bankroll management)、桌位选择 (table selection)、方差与心态 (mental game/tilt 控制)、抽水 (rake) 与场次选择对长期赢率 (winrate, bb/100) 的影响; (f) 智识演进 — 从 Sklansky 古典手牌价值 → Janda/Tipton 博弈论 → solver 普及 → GTO Wizard 民主化求解 → AI 超人 (Libratus/Pluribus) 对人类策略的反哺。学派分歧: GTO 派 vs 剥削派、现金局 vs 锦标赛、理论自上而下 vs 牌局自下而上、solver 纯学院派 vs 实战感觉派。不含: 线下牌场运营/发牌荷官、博彩合规与赌场管理、扑克之外的牌类 (桥牌/斗地主)、纯概率赌博 (老虎机/轮盘)。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 德州扑克策略 (Texas Hold'em) — solver/GTO 时代的无限注德州扑克竞技打法，牌手/职业视角。覆盖: (a) 理论两极 — GTO 博弈论均衡 (不可剥削的基线策略) vs Exploitative 剥削打法 (针对对手漏洞偏离均衡最大化 EV)，以及二者的实战取舍; (b) solver 时代工作流 — PioSOLVER/GTO Wizard 跑解、范围构建、节点锁定 (node-locking) 做剥削、用 HUD+数据库 (Hold'em Manager/PokerTracker/Hand2Note) 复盘找漏洞 (leak finding); (c) 牌型分层 — 现金局 (cash game) vs 锦标赛 (MTT) 的根本差异，后者叠加 ICM (独立筹码模型) 与跳台/泡沫期博弈; (d) 核心概念栈 — 范围 (range)/权益 (equity)/EV/位置 (position)/翻前开池与 3-bet/4-bet/翻后下注理论 (c-bet、极化 vs 浓缩范围、MDF 最小防守频率、阻断牌 blockers、超池下注 overbet)、SPR、底池赔率; (e) 元层 — 资金管理 (bankroll management)、桌位选择 (table selection)、方差与心态 (mental game/tilt 控制)、抽水 (rake) 与场次选择对长期赢率 (winrate, bb/100) 的影响; (f) 智识演进 — 从 Sklansky 古典手牌价值 → Janda/Tipton 博弈论 → solver 普及 → GTO Wizard 民主化求解 → AI 超人 (Libratus/Pluribus) 对人类策略的反哺。学派分歧: GTO 派 vs 剥削派、现金局 vs 锦标赛、理论自上而下 vs 牌局自下而上、solver 纯学院派 vs 实战感觉派。不含: 线下牌场运营/发牌荷官、博彩合规与赌场管理、扑克之外的牌类 (桥牌/斗地主)、纯概率赌博 (老虎机/轮盘)。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「德州扑克」「德扑」「Texas Hold'em」「poker strategy」「扑克策略」

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。

カレンダー予定を確認するスキル。ICS（Googleカレンダー等）と手動追加（画像読み取り）を統合。「今日の予定」「明日の予定」「カレンダー画像送って」「スケジュール確認」で使用。`/calendar` で発動。

Check Google Calendar for today's schedule. Use during daily planning or when user asks about their calendar.

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。

Check Google Calendar for today's schedule. Use during daily planning or when user asks about their calendar.

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。

Unpack a broad standard or competency descriptor into specific, assessable success criteria and sub-skills. Use when interpreting curriculum standards or writing learning objectives.

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 compliance calendars. TRIGGERS - Use when user needs help with insurance-compliance-calendar related 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。

Conduct Fundamental Rights Impact Assessments (FRIA) for high-risk AI systems as required by EU AI Act Article 27. Use when (1) deployers of high-risk AI systems (Annex III) need to assess fundamental rights impacts before first use, (2) public bodies or private entities providing public services are deploying AI systems, (3) credit scoring or insurance risk assessment AI systems are being deployed (Annex III 5b, 5c), (4) evaluating impact on natural persons' fundamental rights (dignity, liberty, privacy, data protection, non-discrimination, children's rights, etc.), or (5) creating EU AI Act Article 27 compliance documentation with market surveillance notification.

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Process vehicle insurance Excel data using Pandas - file handling, data cleaning, merging, validation. Use when processing Excel/CSV files, handling data imports, implementing business rules (negative premiums, zero commissions), debugging data pipelines, or optimizing Pandas performance. Keywords: data_processor.py, Excel, CSV, Pandas, merge, deduplication, date normalization.

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Copilot Studio Standard エージェントを承認付き private API で初回プロビジョニングし、設定・外部トリガー追加・ニュース配信エージェント等を構築する。生成オーケストレーション（Generative Orchestration）モード一択。

Foundational skills for Zephyr RTOS development. Covers essential Embedded C patterns (BIT, CONTAINER_OF), real-time concurrency primitives (mutexes, semaphores, spinlocks), hardware literacy (datasheet-to-DTS mapping), and defensive programming. Trigger when writing core application logic, drivers, or troubleshooting foundational behavior.

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Bridge layer connecting the Agentic AI Foundation (AAIF) governance structure — Linux Foundation stewardship of MCP, goose, AGENTS.md — to the plurigrid/asi skill graph. Formalizes AAIF-compatible skill registration, IPSIE identity profiles, and the cross-protocol interoperability surface. Maps the AAIF platinum member ecosystem (AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI) to specific ASI skill integration points.

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Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.

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Reviews Python code across type safety, error handling, security, performance, modern patterns, design clarity, typed-boundary compliance, test quality, and documentation. Use when performing code review, PR review, pre-merge quality checks, or assessing Python for security vulnerabilities, bare except clauses, Any usage outside boundaries, or missing input validation at system boundaries.

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Reviews Python boundaries, typing, error handling, tests, dependencies, and supported-version idioms against the target project's contracts and active tools. Loaded by dh:code-reviewer for Python source; uses Python-engineering standards when available and retains standalone checks otherwise.

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Activates Standard of Excellence enforcement for the current session. Load before starting any task to apply completion standards: finish the whole thing, fix the root cause, ship the complete working result. Blocks partial solutions, workarounds, deferred threads, and invented content limits when the permanent solve is within reach. Triggers: 'do the whole thing', 'boil the ocean', 'standard of excellence', 'finish it completely', before starting any implementation, refactoring, or multi-step task where partial output is a risk. Does NOT apply to: read-only queries, one-line typo fixes, pure knowledge questions, or single-output summarize requests.

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Build a complete social media publishing calendar from a campaign goal, date range, audience, channels, and content constraints. Use when planning a week, month, launch window, or ongoing period of scheduled content.

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Institution ratings, consensus price targets, EPS/revenue forecasts, finance calendar, shareholder data, fund holders, insider trades (SEC Form 4), short interest, industry rankings, peer group analysis via Longbridge. Frameworks: investment proposals, coverage initiation, stock research, competitive analysis, financial planning, and DeFi/on-chain analysis. Triggers: "机构评级", "目标价", "一致预期", "EPS预测", "内部人交易", "空头", "行业排名", "投资提案", "首次覆盖", "竞争格局", "财务规划", "DeFi收益", "链上数据", "機構評級", "目標價", "一致預期", "內部人交易", "空頭", "投資提案", "首次覆蓋", "競爭格局", "鏈上數據", "analyst rating", "price target", "consensus", "insider trades", "short interest", "coverage initiation", "DeFi yield", "on-chain", "earnings calendar", "finance calendar", "财报日历", "下周谁财报", "下周财报", "下周有哪些财报", "哪些财报", "谁财报", "FOMC", "非农", "股东", "谁持有", "股東", "基金持仓", "基金持倉", "機構評級", "目標價", "財務規劃"

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Android APK hooking, Frida tracing, request-signing recovery, SSL pinning bypass, JNI boundary inspection, and app trust-boundary analysis. Use when the user asks to hook an APK, inspect signer logic, trace Java or native boundaries, bypass pinning or root checks, inspect shared prefs or app databases, or replay accepted mobile requests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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Structures secondary direct transactions with pricing methodology, transfer restriction analysis, and ROFR navigation. Use when modeling secondary purchases, pricing founder/employee shares, or structuring tender offers.

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Selects the active profile from triggering context. Evaluates a rule set against inputs (current time, calendar state, geofence, manual command, custom signals) and returns the profile name that consuming Skills (handoff-trigger-check, critic-gate, future Skills) should apply. Configuration-driven; rule set lives in a router config file conforming to the included schema. Deterministic precedence — manual override beats calendar beats time-window beats default. Required default profile guarantees a routing decision always exists. Use when an orchestrator needs to pick a profile automatically rather than having the caller specify one, when wiring calendar/time/Telegram triggers to gate behavior, or when a Skill's behavior should change with availability mode. Trigger on: "what profile should I use," "route to a profile," "which mode am I in," "auto-select profile," "active mode," "current profile." Do NOT use to evaluate work readiness or review proposed changes. Always returns a profile name.

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。