「mode」の検索結果

3-statement financial model, integrated IS BS CF, income statement projection, balance sheet forecast, cash flow statement, cross-statement balancing, financial model build, operating model, three statement model, integrated financial statements

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Full enhancement pipeline for an existing Lattice skill — rewrites a molecule or atom to modern-capable-model grade: restores degraded grammar, deduplicates, hardens gates and branching to one-unambiguous-action precision, verifies zero behavioral loss against a pre-capture diff, then runs the combined QA gate (skill-review, skill-tighten, skill-validate) and an independent fresh-context verifier, fixing everything found. Consumer-first: optimized for first-time open-source users on fresh repos with minimal .lattice/ setup, running capable models. Use when the user says 'enhance this skill', 'upgrade this molecule', 'modernize this skill', 'optimize this skill', 'make this skill production grade', 'full enhancement', 'polish this molecule like design-blueprint', or names a molecule/atom to overhaul. For gap-finding only use skill-review; for conventions only use skill-validate — this skill runs the whole loop end to end.

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Produce video with Moonvalley's Marey model family (Marey Realism v1.5) — a filmmaker-oriented, 1080p/24fps generative video model marketed as trained exclusively on licensed data. Use this skill when a task asks for Marey or Moonvalley specifically, when a brief demands brand-safe / legally reviewed AI video for commercial or studio work, or when the request needs Marey's director-style controls (camera trajectory, motion transfer, pose transfer, keyframing, reference conditioning). It covers what "commercially safe" really means versus the actual terms of service, access routes (Moonvalley web / Voyager, the fal API, ComfyUI), pricing, capability limits, prompt and reference strategy, where Marey fits production versus where other models win, iteration, and quality review. Do not use it for audio generation, for photoreal talking-head dialogue, or as a generic "best video model" default.

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Plan, prompt, call, edit, iterate, and productionize Alibaba Cloud Model Studio image generation with current Wan 2.7 Image, Qwen-Image 2.0, and Z-Image Turbo models. Use for Alibaba/DashScope text-to-image, multi-reference generation, instruction editing, character-consistent image sets, typography and layout work, regional authentication, model selection, API integration, cost/rate-limit planning, troubleshooting, safety, rights, privacy, and output QA.

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Build tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Run Bayesian phylogenetic analysis with MrBayes, BEAST2, RevBayes, and PhyloBayes including MCMC convergence diagnostics and model comparison. Use when needing posterior probability support, Bayesian model averaging, site-heterogeneous models for deep phylogenies, or formal model comparison via stepping-stone sampling.

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Implement complete dark mode for any frontend project. Auto-detects theme system (Flutter ThemeData, Tailwind dark class, CSS variables, MUI, Chakra UI, styled-components), generates a dark color palette with proper surface elevation hierarchy and desaturated brand colors, creates a theme toggle with system preference detection and persistence, migrates all hardcoded colors to theme tokens, handles special cases like images, shadows, form inputs, and charts, then verifies WCAG AA contrast ratios for both modes. Use when you need to add dark mode, fix dark theme contrast issues, create a theme switcher, or migrate hardcoded colors to theme-aware tokens.

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dontbesilent 商业模式诊断。两种模式：问诊（消解你的问题）和体检（拆解你的商业模式）。 触发方式：/dbs-diagnosis、/问诊、「帮我看看商业模式」「诊断一下我的业务」「我有个商业问题」 Business model diagnosis using dontbesilent's ontological framework. Two modes: consultation (dissolve your question) and checkup (analyze your business model). Trigger: /dbs-diagnosis, "diagnose my business model", "I have a business question"

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Review and upgrade MetaX model support against a target vLLM revision and installed MACA components, recursively including model-dependent attention and kernels. Establish quantization and cache differences, preserve upstream structure, and distinguish shared upstream bugs from adaptation defects. Use for model support work, not standalone monkey-patch or registry audits.

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Define implementation contracts (APIs and data models) that developers will build against during PRD v0.6 Architecture. Triggers on requests to define APIs, design database schema, create data models, or when user asks "define APIs", "data model", "database schema", "API contracts", "technical spec", "endpoint design", "schema design". Consumes ARC- (architecture), TECH- (Build items), UJ- (flows), SCR- (screens). Outputs API- entries for endpoints and DBT- entries for data models. Feeds v0.7 Build 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。

Build or resume mathematical-modeling competition work, including CUMCM, MCM/ICM, graduate and regional contests, data challenges, and open-topic statistical modeling. Guide problem framing, modeling, computation, validation, Chinese or English LaTeX writing, and reviewed PDF/source delivery using current official requirements. Not for ordinary paper polishing.

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Implement comprehensive safety guardrails for LLM applications including content moderation (OpenAI Moderation API), jailbreak prevention, prompt injection defense, PII detection, topic guardrails, and output validation. Essential for production AI applications handling user-generated content. Use when ", guardrails, content-moderation, prompt-injection, jailbreak-prevention, pii-detection, nemo-guardrails, openai-moderation, llama-guard, safety" 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。

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。

Web 线上展览开发 (虚拟展厅) (Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。) Master OS — automated mastery of Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Web 线上展览 / 虚拟展厅开发 (从业者/工程视角) — 用 web 技术开发线上展览、虚拟展厅、云展会、数字展馆，覆盖 3D 空间、全景漫游、WebXR、文博数字化、营销云展厅、3D 商品展示等形态。覆盖: (a) 技术栈 — 3D web(Three.js / React Three Fiber / Babylon.js / Google model-viewer / WebGL / WebGPU)、全景(Pannellum / Marzipano / Photo Sphere Viewer / krpano / 720云)、WebXR(WebXR Device API / A-Frame)、资产管线(glTF·GLB / Draco / Meshopt / KTX2 纹理压缩 / gltf-transform / LOD); (b) 自研 vs SaaS 平台选型(国内: 众趣 / 酷雷曼 / 比目鱼 / 会鸽 / 3DVista; 海外: Matterport / Kuula / Artsteps / Spatial); (c) 性能优化(大场景加载 / 模型轻量化 / draw call / 移动端 / 首屏 / 显存)——web 3D 第一性难题; (d) 策展与导览(动线设计 / 热点 hotspot / 语音讲解 / 交互); (e) 文博数字化(IIIF / 数字孪生 / 高清文物) 与 会展·电商商业化(营销云展厅 / 数据埋点 / 线索转化); (f) 部署(CDN / 流式加载 / 跨端兼容 / WebGL 降级)。学派分歧: 自研 Three.js vs SaaS 平台、真 3D 重场景 vs 720 全景轻量、WebGL vs WebGPU、文博考据派 vs 营销转化派、沉浸优先 vs 加载优先。不含: 线下展陈 / 展台搭建、原生 VR App(非 web)、通用 3D 游戏开发、纯 3D 建模教程。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「线上展览」「虚拟展厅」「web 线上展览」「云展厅」「数字展馆」

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。

CEO (Chief Executive Officer) — 一家公司「最高操作员」的工艺, 与泛泛「领导力 / 管理学」不同: (a) 设定战略与凝结愿景 (Drucker 的「企业的经营理论」+ Porter 论定位 + Hamilton Helmer 的「七力」+ Andy Grove 的「战略转折点」+ Roger Martin 《Playing to Win》); (b) 资本配置是 CEO 单一最高杠杆的工作 (Buffett/Munger Berkshire 致股东信 + William Thorndike 《The Outsiders 商界局外人》论 8 位非常规 CEO + Michael Mauboussin 论 hurdle rate + Bezos 年度致股东信「invariants vs bets」+ Mark Leonard Constellation Software); (c) 组建与领导高管班子 (Patrick Lencioni 《团队的五大障碍》+ Ben Horowitz 《艰难的事》+ Reed Hastings 《No Rules Rules》人才密度 + Andy Grove HOP《高产出管理》+ Lou Gerstner IBM 转型 + Marshall Goldsmith); (d) 董事会与投资人关系 (Larcker & Tayan 《公司治理重要》+ Bill George 《True North》+ 正式 CEO-董事长 / CEO-独立董事 protocol + 维权投资人 playbook + 资本市场沟通); (e) 企业文化与价值观作为操作系统 (Netflix 文化手册 + Amazon Leadership Principles + Bridgewater 《Principles》Ray Dalio + Schein 论组织文化 + 任正非 灰度 + 张瑞敏 人单合一 + 稻盛和夫 阿米巴); (f) 高不确定下的高风险决策 (Bezos Type 1/Type 2 决策 + premortem Klein + Kahneman 论 executive bias + RAPID/DACI + base rates + 情景规划 + 反思 premortem); (g) 对内对外沟通 (创始人 / CEO 致股东信作为机构构建工具: Bezos / Buffett / Reed Hastings 备忘录 / 任正非 内部讲话 + Town Hall + 危机沟通 Tylenol/Boeing/Wells Fargo + 应对维权投资人); (h) 跨阶段规模化 (创业 0→1 / 扩张 1→100 / 成熟期掌舵 / 危机转型): Steve Blank + Brian Chesky founder-mode + Marc Andreessen + Pierre Lassonde + Lou Gerstner 转型; (i) 创始人 CEO vs 职业经理人 CEO 的差异 (Paul Graham 「Founder Mode」2024 + Brian Chesky + Reed Hastings (创始人→职业过渡) + BCG/McKinsey 的「manager mode」+ 两者各自代价 + 什么时候切换)。诚实处理: 幸存者偏差 (单个 CEO 的打法 ≠ 普适规律, 学术研究显示 CEO 个人方差约 ⅓, 约 70% 来自行业 / 时代 / 运气 / 资本结构), 创始人崇拜, 区分可复制工艺与不可移植个人魅力, 治理与利益相关者责任 (员工 / 股东 / 社会), 反对 CEO 神话与 Welch 退休金扭曲 / Theranos / WeWork / FTX / Uber-Kalanick 时代 / Musk 80h+ / 996 工时崇拜。覆盖 founder-CEO (Bezos/Hastings/黄仁勋/Chesky/Brin/任正非) 与职业 CEO (Nadella/Gerstner/Pichai/Mulally/Lafley)。不含: 泛泛管理理论 (商业书已覆盖, 本 skill 专谈 C-suite 这个具体岗位), 不含: 「领导力」作为个人成长自助 (Robbins/Tracy/Maxwell 励志体裁), 不含: 「如何创业」早期战术 — 一旦有员工 / 董事会 / 资本, CEO 的工艺才开始。 (CEO (Chief Executive Officer) — the craft of being the top operator of a company, distinct from generic 'leadership/management': (a) setting the strategy and crystallizing the vision (Drucker's 'theory of the business' + Porter on positioning + Hamilton Helmer's 7 powers + Andy Grove's strategic inflection points + Roger Martin Playing to Win); (b) capital allocation as the CEO's single highest-leverage job (Buffett/Munger Berkshire letters + William Thorndike 'The Outsiders' on 8 unconventional CEOs + Michael Mauboussin on hurdle rates + Bezos's annual shareholder letter approach to invariants vs bets + Mark Leonard Constellation Software); (c) building and leading the executive team (Patrick Lencioni 'Five Dysfunctions' + Ben Horowitz 'Hard Things About Hard Things' + Reed Hastings 'No Rules Rules' on talent density + Andy Grove HOP + Lou Gerstner IBM turnaround + Marshall Goldsmith); (d) board and investor relations (Larcker & Tayan 'Corporate Governance Matters' + Bill George 'True North' + the formal CEO-Chair / CEO-Lead Director protocols + activist investor playbooks + capital markets communication); (e) culture and values as operating system (Netflix culture deck + Amazon Leadership Principles + Bridgewater 'Principles' Ray Dalio + Schein on org culture + 任正非 Ren Zhengfei 灰度 + 张瑞敏 Zhang Ruimin 人单合一 + 稻盛和夫 阿米巴); (f) high-stakes decision-making under uncertainty (Bezos Type 1 vs Type 2 + premortem Klein + Kahneman 'Thinking Fast and Slow' for executive bias + RAPID/DACI + base rates + scenario planning + premortem); (g) internal and external communication (founder/CEO letters as institution-builders: Bezos shareholder letters / Buffett letters / Reed Hastings memos / 任正非 内部讲话 + Town Halls + crisis communication Tylenol/Boeing/Wells Fargo + activist responses); (h) cross-stage scaling (founder-CEO of 0→1 / scaling 1→100 / mature-stage stewardship / turnaround): Steve Blank + Brian Chesky founder-mode + Marc Andreessen + Pierre Lassonde mining vs steady-state + Lou Gerstner turnaround; (i) the founder-CEO vs professional-manager-CEO distinction (Paul Graham 'Founder Mod

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。

现代建筑设计 (从 concept 草图到施工图与现场服务的整套设计流程, 含 BIM / 可持续 / 参数化 / 在地性 模块, 全球语境 + 中国本土实践并重) (modern architecture design — 当代建筑设计 (concept → schematic → DD → CD → CA + sustainability / BIM / parametric / 中外双语 canon, 不含纯室内 / 景观 / 城市规划 / 结构 / MEP, 那些是相邻专业)) Master OS — automated mastery of modern architecture design — 当代建筑设计 (concept → schematic → DD → CD → CA + sustainability / BIM / parametric / 中外双语 canon, 不含纯室内 / 景观 / 城市规划 / 结构 / MEP, 那些是相邻专业): top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on modern architecture design — 当代建筑设计 (concept → schematic → DD → CD → CA + sustainability / BIM / parametric / 中外双语 canon, 不含纯室内 / 景观 / 城市规划 / 结构 / MEP, 那些是相邻专业) problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「建筑设计」「建筑师」「建筑事务所」「设计院」「方案设计」

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。

数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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。

Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe (evaluator-as-selector, stop on regression). Routes on measured cost-per-completed-task rather than per-token price, because a tier's token count varies more by task shape than price varies across tiers. Covers per-model effort semantics, the concision lever, cascade preconditions, context handoff, and watching a subagent fan-out live. Use when choosing a subagent's model or effort, when choosing how to escalate a failed attempt, or when asked which model or effort a task should get. Grounded in a 2026-07 calibration, a 2026-08 coding-cost study, a 2026-09 seeded-bug repair battery, a 2026-10 Haiku 5.5 ladder priced per spawn, and a 2026-10 retry-versus-escalation ladder on 295 SWE-bench Verified tasks; Managed Agents API specifics are operational, not calibrated.

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。

Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-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。

Use when writing or running Unity tests - Unity Test Framework EditMode vs PlayMode, test assembly definitions, testing plain C# and ScriptableObjects, PlayMode scene/physics/input tests, batchmode CLI with result XML, coverage, and the dotnet test fallback when no editor is installed.

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 a model is served online (FastAPI, BentoML) or scored in batch, when changing how a model artifact is loaded or versioned, or when adding drift, quality or prediction monitoring and a rollout plan for a new model

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。

Delegate code review to a subagent running a specific model. Use ONLY when user explicitly names a model to review changes ("review with opus", "use sonnet to review", "review with gemini"). The root agent reconstructs changes from conversation history and spawns a subagent with the code-review skill using the specified model. Do NOT use for general code review (use code-review skill instead), for reviewing PRs from git history, or when no model is specified by the user.

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。

Selects and composes the Godmode skills for a coding task, explains the activation boundary, keeps workflow skills from being skipped when they materially reduce risk, and identifies the task's current engineering lifecycle state. Use when starting a task with Godmode installed, deciding which skill to invoke, recovering a task after context loss, or checking whether a workflow applies. Not a replacement for the selected skill's instructions or for repository-local rules.

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。