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Activate this skill when a student provides study material (PDF or pasted notes) and a syllabus, and wants to prepare for an exam. Extracts key definitions, points, keywords, diagrams, exam-ready sentences, and practice questions strictly from the provided material.

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Analyze logs and source from a completed manual examples run. Never execute or control examples.

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Analyze logs and source from a completed repository example run. Never execute or control examples.

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Practise the essay paper (申论) of the Chinese civil service examination (国考 / 省考): work a question from the given materials (给定资料), find the scoring points (要点), draft an answer in the expected format, and mark it the way examiners do. Use when asked 申论怎么写, 帮我批改申论, 归纳概括题怎么答, 申论大作文, 公文写作题, or practise the shenlun paper. Produces a question-type diagnosis, the points extracted from the materials with their source paragraphs, a model answer within the word limit, a marking of the person's own answer against the points, and a practice plan.

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Create an interactive digital worked example sequence with fading for online or blended delivery. Use when building e-learning modules, LMS content, or app-based instruction.

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Use when a Java change needs independent Behavior-Driven Development guidance from trusted behavior facts through concrete examples and observable scenarios, with focused clarification when behavior is pending or ambiguous. This should trigger for requests such as Apply BDD; Facilitate behavior examples; Discover scenarios with Given When Then; Review these examples for shared domain language; Complete BDD discovery as a self-contained interaction. Part of Plinth Toolkit

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Curated few-shot examples for construction AI tasks: classification, extraction, analysis. Domain-specific examples for improved LLM performance.

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帮助学生在临考前进行结构化极速复习：解析课程资料/大纲/重点，按章节生成 wiki 知识库与标准题库，组织针对性刷题与判分，并记录复习进度和错题。当用户即将考试、需要快速复习计划、练习题、错题复盘或考前小抄时使用（关键词：期末/备考/复习/刷题/划重点/错题；exam, cram, study plan, quiz, review）。不适用于长期学习规划、与考试无关的写作或编程任务。

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Automatically generates executable test cases from model checking counterexample traces. Translates abstract counterexample states and transitions into concrete test inputs, execution steps, and assertions that reproduce property violations. Use when working with model checker outputs (SPIN, CBMC, NuSMV, TLA+, Java PathFinder, etc.) and needing to create regression tests, validate bug fixes, or reproduce verification failures in executable test suites.

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Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.

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Debug proof failures using counterexamples from Nitpick (Isabelle) or QuickChick (Coq) to identify specification errors, missing preconditions, and proof strategy issues. Use when: (1) A proof attempt fails and you need to understand why, (2) Counterexamples are generated by Nitpick or QuickChick, (3) Specifications may be incorrect or incomplete, (4) Theorems need validation before proving, (5) Missing preconditions or lemmas need identification, or (6) Proof failures need explanation and correction suggestions. Supports both Isabelle/HOL and Coq equally.

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Generate concrete counterexamples when formal verification, assertions, or specifications fail. Use this skill when debugging failed proofs, understanding why verification fails, creating minimal reproducing examples, analyzing assertion violations, investigating invariant breaks, or diagnosing specification mismatches. Produces concrete input values, execution traces, and state information that demonstrate the failure.

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侍酒师与葡萄酒 (侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。) Master OS — automated mastery of 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「侍酒师」「sommelier」「葡萄酒」「wine」「盲品」

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Create an interactive digital worked example sequence with fading for online or blended delivery. Use when building e-learning modules, LMS content, or app-based instruction.

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Theory-backed design audit — names the principle violated, cites the source, shows the fix. Use when asked to "evaluate design quality", "check if this follows design principles", "theory check", "design exam", "audit against best practices", or "what's wrong with this design".

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Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

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Use when keeping imp's docs and config examples coherent after a change - updating docs/internals/ARCHITECTURE.md / README / docs/GOAL.md / docs/MODELS.md / docs/roadmap.md / imp.conf.example / CHANGELOG / MISSION_JOURNAL, or "is this doc stale", "document this change", "the example config is out of date", "the README says X but the code does Y", "add a roadmap ledger row". Do NOT use for layer/frontmatter/lint questions (docs-layers), structural code audits (codebase-audit), measuring perf or refreshing the perf baseline (benchmark-cuda), or the agent's private memory (MEMORY.md).

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Recommends performing a dexamethasone suppression test to evaluate for autonomous cortisol secretion in patients with primary aldosteronism and adrenal adenoma. Triggered when a clinician considers whether to perform a dexamethasone suppression test in a patient with PA and adrenal adenoma.

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Synchronize and verify code examples in documentation. Use when function signatures change, API interfaces update, imports shift, or documentation snippets become outdated and need correction.

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Use this skill to verify that a driver's medical certificate was issued by a certified examiner on the FMCSA National Registry. Covers the search process, what to verify, and how to handle examiner removal.

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Use this skill when the user asks about CDL driver medical certification — 49 CFR 391.41-49 medical qualifications, medical examiner's certificate (Long Form 5876 + Short Form 5875), National Registry of Certified Medical Examiners (NRCME), medical restrictions, federal disqualifying conditions, exemption programs (vision, diabetes, hearing), the FMCSA Medical Examiner's Handbook, or what to do when a driver's medical cert is downgraded. Cite specific 49 CFR 391 sections.

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Use this skill when the user asks how to interpret a DOT medical examiner's findings — what restrictions mean (corrective lenses, hearing aid, no insulin, etc.), what conditions trigger short-term vs 24-month certificates, vision/hearing thresholds, blood pressure categories, sleep apnea screening, when to seek an exemption program, and how to handle a downgrade or denial. Cite 49 CFR 391.41-49 and the FMCSA Medical Examiner Handbook.

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Generates code examples for PHP documentation. Creates minimal, copy-paste ready examples with expected output.

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