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概要と使いどころ
Read or configure the vault filing methodology and suggest destinations for planned knowledge creation under Generic, LYT, PARA, or Zettelkasten. Use for wiki mode, methodology mode, what is my vault mode, set vault mode, switch to PARA, use LYT, Zettelkasten setup, change mode, configure mode, or methodology routing. This skill does not save content or migrate notes.
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Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
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。
The scheduled model sweep. Reads the team's models channel for hints, discovers new LLM models, adds them to ml_model_list.py, runs the paid tests, marks dead provider entries deprecated, opens PRs under the team's rules, announces them in Slack, and checks whether remote_config needs a publish PR. Use when asked to run the model sweep by hand, to add or deprecate models unattended, or to change how the daily sweep behaves.
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。
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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 the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. End-to-end: assesses the codebase, rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Also has an access-only mode for checking or enabling Bedrock model access for specific models (e.g. 'request access to Claude on Bedrock', 'which models do I turn on') with no code migration — it checks each model and walks the right access step, then stops. Not for agent runtime selection or agent migration planning (use agent-advisor), nor standalone cost estimates or infra-only migration. The full migration REQUIRES gcp-to-aws OR azure-to-aws installed alongside this skill (gcp-to-aws preferred when both are present); access-only mode needs neither.
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, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
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。
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
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。
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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 this skill for Node.js backend AI via @cloudbase/node-sdk (>=3.16.0) — cloud functions, CloudRun, Express/Koa/NestJS, serverless APIs, scheduled jobs, LLM proxies, agent orchestration. The only SDK supporting image generation (ai.createImageModel + generateImage). Text via ai.createModel with groups cloudbase, hunyuan-exp, or custom-*; model ids (e.g. deepseek-v4-flash, glm-5, kimi-k2.6) go in the `model` field of generateText/streamText. MUST run two-step preflight before code — see body. NOT for browser/Web (use ai-model-web) or Mini Program (use ai-model-wechat).
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。
Migrate, modernize, and upgrade codebases to AWS. Run analysis on repos for tech debt, security vulnerabilities, and modernization opportunities. Transforms .NET Framework to .NET 8/10, mainframe COBOL to Java, VMware VMs to EC2, SQL Server to Aurora, and upgrades Java/Python/Node.js versions and AWS SDKs. Use when the user says "migrate .NET to AWS", "upgrade Java to 17/21", "modernize COBOL", "modernize mainframe", "move VMware to EC2", "convert SQL Server to Aurora", "upgrade Python version", "migrate AWS SDK", "transform this codebase", "analyze for issues", "find tech debt", "what tech debt", "security vulnerabilities", "CVEs", "what's wrong with my code", "assess my repos", "where do I start", "find what's outdated", "analyze my repos", "AWS Transform - continuous modernization", "continuous modernization" or "continuous-modernization". Don't use for infrastructure provisioning, CI/CD pipelines, or general coding 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。
Query the bundled BZD mathematical-modeling dictionary for a user-selected model, explain its assumptions, inputs, outputs, limitations and validation, then judge whether it fits the current competition problem and data. Use when a user asks whether a proposed model is appropriate, requests model feasibility review, or wants dictionary-based alternatives for a modeling 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。
Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.
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。
Automatically extract abstract finite-state models in SMV/NuSMV format from source code (C/C++, Java, Python) for formal model checking. Use when users need to: (1) Generate SMV models from program code for verification, (2) Extract state-transition models from protocol implementations, (3) Analyze control flow and data flow to construct formal models, (4) Create models for checking safety and liveness properties, (5) Convert imperative code to declarative state machines. Particularly effective for protocol implementations, concurrent systems, and control logic with clear state transitions.
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。
Figma 转 Photoshop (PSD) (Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及).) Master OS — automated mastery of Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Figma → Photoshop (PSD) 设计稿转换 (从业者视角) — 把 Figma 设计文件高保真转换为 Photoshop 分层 PSD, 核心是对齐两个工具的概念/渲染差异, 让产出的设计稿视觉一致. 覆盖: (a) 概念映射 — Figma frame / auto-layout / component / variant / constraint / vector-network / boolean / effect (drop·inner shadow / layer·background blur) / mask / blend mode / gradient / text-engine / color 与 Photoshop artboard / group / smart-object / linked / layer-style / shape-layer / path / clipping·layer mask / blend-mode 的对应关系与不可逆损耗点; (b) 转换方法栈 — 插件直出 PSD / 导出图层 (PNG·SVG) 再重组 / SVG·PDF 桥接 / 手工重建 / Figma REST API + Photoshop UXP·ExtendScript 脚本化, 各法在保真度-可编辑性-工时上的权衡与选型; (c) 保真度 QA — 字体回退与缺字处理 / 颜色空间 (sRGB·ICC profile) / 效果参数对齐 / @Nx·DPI 与栅格化 / 像素 diff 验收; (d) 两种交付目标 — 可编辑分层 PSD vs 像素保真扁平 PSD 的方法选择. 主战场 = Figma Community 插件 + Adobe 官方文档/论坛 + 设计交付 (design handoff) 实践 + 设计师社区. 不含: PSD→Figma 反向转换 / Sketch / Adobe XD / 纯前端 code handoff (HTML·CSS) / 印刷 CMYK 专业制版 (仅触及). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「figma 转 psd」「figma to psd」「figma 转 photoshop」「figma 导出 psd」「设计稿转换」
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。
arkcli 模型查询与基础模型服务激活能力：列出、搜索、获取火山公共基础模型详情，以及用户明确要求的开通/激活模型服务（`arkcli models activate`）；Volc 还支持 TTFT/TPOT 性能排名、延迟趋势和输入长度对比。激活已有基础模型服务不等于部署/创建 Endpoint，不得转成 `+deploy`。优先使用产品命令 `arkcli models ...`，而不是直接调用 Raw API。反触发：用户的最终目标是创建 / 部署 Endpoint 时，本 skill 承担有界的只读候选查询，owning skill 由创建路径确定：普通产品创建走 arkcli-deploy；用户显式要求 raw CRUD / CI / 无守卫的 `infer endpoint create` 走 arkcli-infer-endpoint。候选首先执行 `models search ... --size 10 --format json`；成功完整结果直接复用，空结果与捕获缺损按 reference 有界恢复，并把实时返回的 `name` 与非空 `primary_version` 组合成可直接传给 `--model` 的完整 ID，不能拉全量清单、把裸家族名当可部署 ID，或逐候选追加 `models get`。注意：查询/管理账号下自传或精调的自定义模型（`cm-xxx`）走 arkcli-custommodel。
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。
Regression analysis, ANOVA, generalized linear models, Bayesian methods, and model selection. Covers the full modeling workflow from problem formulation through diagnostics -- linear regression, logistic regression, Poisson regression, mixed-effects models, prior specification, posterior inference, AIC/BIC comparison, cross-validation for model selection, and assumption checking. Use when fitting models, testing hypotheses, or selecting among competing statistical explanations.
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。
Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.
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。
"Apply Structural Equation Modeling (SEM) to test hypothesized causal structures by combining measurement models (CFA) and structural models (path analysis). Use this skill when the user needs to validate latent constructs, test mediation or moderation paths, assess model fit with CFI/TLI/RMSEA/SRMR, or when they ask 'do these variables form a causal chain', 'how do I test my theoretical model', or 'is my measurement model valid'.".
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。
Read or configure the vault filing methodology and suggest destinations for planned knowledge creation under Generic, LYT, PARA, or Zettelkasten. Use for wiki mode, methodology mode, what is my vault mode, set vault mode, switch to PARA, use LYT, Zettelkasten setup, change mode, configure mode, or methodology routing. This skill does not save content or migrate notes.
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。
Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.
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。
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
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 this skill when deploying a trained Einstein Discovery model to production records declaratively — activating a prediction definition, mapping output fields to page layouts, adding the Einstein Discovery Action to a Flow, running bulk predict jobs, and monitoring model health via Model Manager UI. Trigger keywords: prediction definition activation, bulk predict job, Einstein Discovery Flow action, Model Manager, prediction field mapping, model refresh activation, scoring job, Einstein Discovery recommendations on record. NOT for authoring the story in CRM Analytics Studio — use admin/einstein-discovery-setup. NOT for programmatic scoring through the API — use agentforce/einstein-discovery-development.
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。
Designing Campaign Hierarchy for program-level ROI tracking, configuring Customizable Campaign Influence (CCI) attribution models, and interpreting multi-touch attribution from MCAE B2B Marketing Analytics. Trigger keywords: campaign ROI, attribution model, campaign hierarchy, first-touch, last-touch, multi-touch, CCI, campaign influence, revenue attribution, campaign member status. NOT for deciding which attribution model and KPIs to adopt before any config - use admin/marketing-reporting-requirements. NOT for MCAE connector setup or the campaign sync that writes Campaign Member records - use admin/mcae-pardot-setup. Also covers: CampaignSettings, enableCampaignInfluence2, enableAutoCampInfluenceDisabled, enableB2bmaCampaignInfluence2, enableAccountsAsCM, CampaignInfluenceModel metadata, isDefaultModel, isModelLocked, recordPreference, AllRecords, RecordsWithAttribution, CampaignInfluence.RevenueShare, ModelType, CampaignMemberStatus IsDefault HasResponded SortOrder, HierarchyActualCost, HierarchyAmountWonOpportunities, TotalNumberofResponses, Opportunity.CampaignId primary campaign source.
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。
Sync OpenRouter models used by OpenClaw into this installation's config. Fetches the OpenClaw app leaderboard from OpenRouter, verifies model IDs against the API catalog, and adds missing models with correct specs and aliases. Use when asked to "sync openrouter models", "add missing models", "update openrouter config", or "check openrouter models".
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。