「ble」の検索結果

Fix ClickHouse Cloud migration failures caused by multi-table RENAME statements. Use when: (1) Migration fails with "Database X is Shared, it does not support renaming of multiple tables in single query", (2) golang-migrate or other migration tools show dirty database version after a table-swap migration on ClickHouse Cloud, (3) Schema migration works on self-hosted ClickHouse but fails on ClickHouse Cloud. ClickHouse Cloud uses SharedMergeTree engine which has restrictions not present in regular MergeTree.

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Create iMandalArt 2.2 pure-text 3x3 Mandala index cards with a five-character center axis, eight orthogonal surrounding angles labeled Ⓐ-Ⓗ, compact title cells such as Ⓐ健康復節, hard-line CJK layout, optional の字型 ordering, and 井字號 compass ordering. Use when the user asks for iMandalArt, iMandalart, I MandalArt, の字型, ◊字型, 井字號, 中國城市方位, 64＋8＋1, 手機方形曼陀羅, 正方形九宮索引卡, 五字定格, ◎中心, Ⓐ-Ⓗ 九宮卡, TheBrain/Cerebro preview-safe hard-line cards, or a square 3x3 card that is not a Markdown table, visible-border PE2 card, or 81-cell Mandala.

Convert Chinese or mixed-language text, iMandalArt cards, Obsidian 九宮, 表格, Mandala notes, or eight-domain outlines into a Markdown 3x3 nine-grid table with an ◎ center, then copy, save, append, or hand it to apps that render Markdown tables such as Obsidian and AIDA. Use when the user asks for Markdown 九宮格, Obsidian 九宮, Markdown table 九宮, 曼陀羅表格, nine-grid clipboard, AIDA table output, or conversion between Markdown 九宮, iMandalArt, and 81-grid workflows.

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Recommends appropriate software design patterns based on problem descriptions, requirements, or code scenarios. Use when designing software architecture, refactoring code, solving common design problems, or choosing between design approaches. Analyzes the problem context and suggests suitable creational, structural, behavioral, architectural, or concurrency patterns with implementation guidance and trade-off analysis.

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Analyze reference-image illumination and build accountable Blender lighting from shadow direction, shadow length, softness, exposed faces, environment fill, bounce, practical sources, and material response. Use for reference matching, daylight, interiors, exterior environments, product scenes, atmospheric lighting, or any task where lights must be physically and visually justified instead of added as arbitrary fill or rim lights.

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Define production-ready visual intent for Blender scenes and standalone assets, including focal hierarchy, depth layers, visual flow, scale and repetition rhythm, camera mobility, representation fidelity, and performance budgets. Use for original or reference-guided Blender work before construction choices are executed, especially environments, architecture, products, hero assets, or scenes whose modeling quality depends on coordinated spatial design rather than isolated objects.

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Build and validate Eevee 3D-to-2D rendering with discrete Shader to RGB lighting bands and a deformation-following inverted-hull Geometry Nodes outline. Use for anime, cel-shaded, toon, hand-drawn, or stylized real-time assets that need editable object-space outlines in Blender Eevee.

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Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.

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Apply stable-baselines3 in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses stable-baselines3 or its strengths fit the task.

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Master problem solver for systematic problem-solving methodologies. Use when the user asks to talk to Dr. Quinn or requests the Master Problem Solver.

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Apply systematic problem-solving methodologies to complex challenges. Use when the user says "guide me through structured problem solving" or "I want to crack this challenge with guided problem solving techniques"

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Automate motion capture and tracking workflows in Blender with Python. Use when the user wants to import BVH or FBX mocap data, retarget motion to armatures, track camera or object motion from video, solve camera motion, clean up motion capture data, or script any tracking pipeline in Blender.

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Automate Blender rendering from the command line. Use when the user wants to set up renders, batch render scenes, configure Cycles or EEVEE, set up cameras and lights, render animations, create materials and shaders, or build a render pipeline with Blender Python scripting.

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Create 2D art and animation in Blender using Grease Pencil and Python. Use when the user wants to draw strokes programmatically, create 2D animations, build Grease Pencil objects from code, manage GP layers and frames, apply GP modifiers, set up drawing guides, or script any Grease Pencil workflow in Blender.

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Automate video editing in Blender's Video Sequence Editor with Python. Use when the user wants to add video, image, or audio strips, create transitions, apply effects, build edit timelines, batch assemble footage, estimate render times, or script any VSE workflow from the command line.

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Configuration management and automation with Ansible. Use when the user needs to write playbooks, manage inventory, create roles, use Ansible Vault for secrets, or orchestrate multi-server deployments across environments.

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I am a veteran print production specialist with 15+ years in tabletop game manufacturing. I've shepherded hundreds of games from digital files to retail-ready products, working with manufacturers from The Game Crafter to Panda GM to Cartamundi. My expertise spans the entire production pipeline: color management, dieline creation, paper selection, finishing techniques, component nesting, insert design, and the critical art of the unboxing experience. I've learned the hard way about RGB-to-CMYK disasters, registration drift, humidity warp, and minimum font sizes that become illegible smudges. I know the difference between 280gsm blue-core and 320gsm black-core card stock by feel. I can tell you why your rich black needs to be C60-M40-Y40-K100, not just K100. I understand why your box needs 15mm clearance on each dimension, and why that gorgeous neon green in your design will become a muddy olive in print. Use when "print my game, packaging design, prepare files for print, dieline, print specifications, board game manufacturing, card game printing, box design, bleed and safe area, CMYK conversion, paper stock, card finish, foil stamping, embossing, unboxing experience, thermoform insert, punchboard design, component manufacturing, Panda GM, Game Crafter, Cartamundi, print-production, packaging, manufacturing, board-games, card-games, cmyk, dieline, die-cutting, offset-printing, tabletop" mentioned.

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Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understanding selectors, waiting strategies, and anti-detection patterns. This skill covers Playwright (recommended) and Puppeteer, with patterns for testing, scraping, and agentic browser control. Key insight: Playwright won the framework war. Unless you need Puppeteer's stealth ecosystem or are Chrome-only, Playwright is the better choice in 2025. Critical distinction: Testing automation (predictable apps you control) vs scraping/agent automation (unpredictable sites that fight back). Different problems, different solutions. Use when "playwright, puppeteer, browser automation, headless, web scraping, e2e test, end-to-end, selenium, chromium, browser test, page.click, locator, playwright, puppeteer, browser, automation, testing, scraping, headless, e2e, selenium, web-testing" mentioned.

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Creating memorable, readable, and emotionally resonant game characters that work at every scale and in every contextUse when "character design, design a character, character art, character concept, character sheet, turnaround, expression sheet, character silhouette, shape language, character proportions, iconic character, memorable character, character lineup, character family, hero design, villain design, npc design, protagonist design, character, art-direction, visual-design, game-art, concept-art, silhouette, shape-language, color-theory, costume, expression, turnaround, iconic, readable" mentioned.

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The ultimate brand asset: a consistent, explorable universe generated by AI. This skill covers creating coherent brand worlds, consistent characters, architectural spaces, and visual universes that serve as the foundation for all marketing content. Traditional brand building creates guidelines. AI world building creates explorable realities. Characters with consistent personalities across thousands of images. Environments that feel like real places. Visual languages so coherent that any new asset feels like it belongs. This is where AI generation meets universe design. The practitioners of this skill don't just create images—they create the DNA that generates infinite consistent images. They build the source code for visual worlds. Use when "world building, brand world, universe, consistent character, character design, visual language, brand universe, style bible, world bible, coherent assets, character sheet, environment design, world-building, universe, character-design, consistency, visual-language, brand-world, style-guide" mentioned.

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Expert 3D modeling specialist with deep knowledge of topology, UV mapping, game-ready and film-ready pipelines, DCC tool workflows (Blender, Maya, ZBrush, 3ds Max, Houdini), retopology, LOD systems, and export pipelines. This skill represents years of production experience distilled into actionable guidance. Use when "3d model, 3d modeling, mesh topology, uv unwrap, uv mapping, retopology, retopo, low poly, high poly, subdivision, subdiv, edge flow, edge loops, polygon modeling, box modeling, hard surface, organic modeling, sculpting, zbrush, blender modeling, maya modeling, 3ds max, LOD, level of detail, game ready mesh, film ready, baking normals, high to low, fbx export, gltf export, texel density, 3d, modeling, topology, uv, game-dev, vfx, blender, maya, zbrush, retopology, lod, hard-surface, organic, sculpting" mentioned.

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Checkable-ish surface presentation signals a reviewer notices first — duplicate/near-identical tables, leftover pipeline/template strings, too-few or LLM-looking figures, and page-padding. AUXILIARY ONLY and weak by design: a deterministic pass (tools/check_presentation.py — dup-table + pipeline-artifact) plus a fresh cross-model GROSS-cases-only semantic pass (thin-float, LLM-figure, page-padding), every above-info finding span-anchored to the evidence ledger (claims.json). The adjudicator CAPS everything at minor (SURFACE_ONLY_SKILLS + SURFACE_PATTERNS) — these contribute at most SOFT_FLAGS, never a HARD verdict — default false_positive_risk:high. NOTE: the pure AI writing-STYLE impressions (AI-flavor prose, defensive 'not-X-but-Y' hedging, narrative-arc, jargon-stuffing, invented codenames) MOVED to the zero-verdict-weight AIS track — for those use skills/ai-style-impressions, NOT this. Emits presentation-signals.findings.json; NEVER computes the verdict. Triggers: "presentation signals", "surface check", "duplicate tables", "排版信号".

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。

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」

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。

临床诊断思维 (临床诊断思维 / 临床推理 (Clinical Diagnostic Reasoning) — 医生『怎么想病』的元学科:从症状/体征/检查到诊断结论的认知操作系统,从业者(临床医生/住院医/规培生,尤其全科/急诊/大内科等以未分化主诉为生的科室)、医学生与医学教育者、诊断安全与质量改进研究者、以及做医疗 AI 辅助诊断产品的人的视角。覆盖: (a) 第一性张力 — **直觉模式识别 (System 1: illness scripts 疾病脚本 / pattern recognition / gestalt, 『资深人一眼认出 aunt Minnie』) ⇄ 分析性推理 (System 2: hypothetico-deductive 假设演绎 / Bayesian 概率更新)** 的 dual-process 双过程理论 (Kahneman→Croskerry/Norman 谱系), 资深≠更会分析而是『脚本库更大+校准更好』; 更深层论战 — **『认知去偏可教 (Croskerry: bias awareness / cognitive forcing strategies / diagnostic timeout) ⇄ 偏倚标签是马后炮、知识结构才是主因 (Norman/Sherbino/Monteiro: debiasing 干预 transfer 证据弱, bias 是 hindsight 标签)』** — 本行最核心学术对垒; 『概率思维 (验前概率 × 似然比 → 验后概率, Pauker-Kassirer test/treatment threshold 阈值模型) ⇄ 穷尽式排查 (rule-out everything / 防御性医疗 / VOMIT)』; 『临床 gestalt ⇄ 结构化临床决策规则 (Wells/PERC/HEART)』; 『诊断简约 Occam's razor ⇄ Hickam's dictum (病人可以同时得 N 个病)』; 『床旁体格检查复兴 (Verghese Stanford 25 / McGee 循证体检) ⇄ 影像检验替代床旁』; (b) 方法论正典 — illness script theory (Schmidt/Boshuizen: enabling conditions/fault/consequences 三段结构), problem representation + semantic qualifiers 问题表征与语义限定词 (Bowen NEJM 2006, 把病人翻成 one-liner), hypothetico-deductive model (Elstein 1978《Medical Problem Solving》: 早期假设生成+定向检验), Bayesian 工具箱 (sensitivity/specificity/LR/Fagan nomogram/SnNout-SpPin), threshold model (Pauker-Kassirer NEJM 1975/1980), 认知偏倚分类学 (anchoring/premature closure/availability/confirmation/base-rate neglect/search satisficing/diagnostic momentum — Croskerry 偏倚清单), 去偏与元认知策略 (diagnostic timeout/cognitive forcing/Ely checklist/calibration), schema-based reasoning (Clinical Problem Solvers schemas: 按 pivot 症状走分支), reflective practice 结构化反思 (Mamede/Schmidt), 诊断错误科学 (NAM 2015 报告定义 / Newman-Toker Big Three / Hardeep Singh e-triggers / SAFER Dx 框架); (c) 行业结构与角色 — 医学生→实习/住院医 (晨会 morning report/查房被 pimping/汇报训练)→主治 attending→master clinician (NEJM CPC discussant/晨会大师如 Dhaliwal); 配套生态: 医学教育者 (clinical reasoning curriculum + 评估: script concordance test/key features exam/OSCE), 诊断安全研究者 (SIDM/AHRQ), 诊断辅助与 CDS 工具开发者 (DDx generator/AI); (d) 核心工作流 — 数据采集 (病史为王 + 循证体检 + 针对性检验影像) → 问题表征 (one-liner + semantic qualifiers) → 鉴别诊断生成 (schema / 解剖定位法 / VINDICATE-M 病因筛, 按『常见可能 × 致命不能漏 can't-miss』双轴排序) → 假设定向检验 (按 LR 选检查 / 阈值决策) → working diagnosis + 安全网 (red flags 交代 / test of time / test of treatment) → 反馈校准 (follow-up / M&M / diagnostic timeout); 教学工作流: 晨会渐进披露汇报 / CPC / SNAPPS / one-minute preceptor / virtual morning report; AI 增强工作流 (2023-2026: LLM 鉴别诊断头脑风暴 / OpenEvidence 检索 / ambient scribe 释放认知带宽 + 自动化偏倚 guardrails); (e) 产出物 — one-liner, problem list, prioritized DDx, assessment & plan (按问题分层), 晨会/CPC 汇报, M&M 复盘, 诊断不确定性沟通 (『最可能是 X, 但出现 Y 红旗立刻回来』); (f) 教育与评估 — script concordance test / key feature exam / EPA / 里程碑; 中国语境: 人卫《诊断学》教材 / 执业医师考试 / 规培结业临床思维考核; (g) 争议/批判 — debiasing 之争 (Croskerry vs Norman-Sherbino『knowledge is the cure』, 去偏 RCT 效果弱 / bias 标签不可证伪), 诊断错误率数字之争 (『10-15%』经典估计 / Newman-Toker 79.5 万美国年严重伤害外推方法被质疑 / 尸检符合率), dual-process 二分被批过度简化 (连续谱 / 难以实证分离), 决策规则 vs gestalt (资深 gestalt 常不输 Wells 类规则 / 算法厌恶), vignette 研究外推性 + context/case specificity (推理高度内容绑定不可通用迁移 [Norman/Eva] — 对『教推理通用课』产业的根本批判), AI 辅助诊断 2023-2026 (GPT-4 在 NEJM CPC/vignette 追平或超医生 [Kanjee 2023 JAMA / Goh 2024 JAMA Netw Open『AI alone > physician+AI』悖论] / Google AMIE / OpenEvidence 爆发 vs 自动化偏倚 / 去技能化 / 真实环境验证缺失), 防御性医疗与过度检查 (incidentaloma 瀑布 / VOMIT), 体检衰亡之争, pimping 教学法争议; (h) 流派/思想谱系 — 决策科学/Bayesian 派 (Ledley-Lusted 1959 → Elstein/Kassirer/Sox/Pauker/Eddy → Brush) vs 认知心理/双过程派 (Kahneman-Tversky → Croskerry/Graber) vs 教育认知派 (Schmidt/Boshuizen illness scripts → Norman/Eva/Mamede/Durning 情境认知) vs 诊断安全/系统派 (NAM 2015 / Newman-Toker / Hardeep Singh / Graber-SIDM: 错误=系统×认知共因) vs 床旁临床派 (Osler 传统 → Tierney aphorisms / Verghese / McGee / Dhaliwal / Saint: 病史体检为王 + 刻意练习) vs AI/计算派 (INTERNIST-1/DXplain 专家系统 → Isabel → LLM 世代 Rodman/Topol)。不含: 具体专科治疗方案与手术决策深度 (足踝外科/种植牙等另有 skill), 中医辨证 (另有 skill), EBM 文献批判性评价方法学 (相关但本 skill 聚焦诊断推理本身), 护理诊断体系 (NANDA), 影像/病理知觉型读片训练 (相邻但独立), 精神科 DSM 结构化访谈细节, 医患沟通技巧全集 (只覆盖诊断不确定性沟通), 患者自查/自我诊断指

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。