5,180 件 ・ 関連度順
概要と使いどころ
Audit rehabilitation setback prediction systems for clinical risk modeling and early intervention. Triggers: you need to evaluate risk factor models (Charlson, Elixhauser), early warning indicators for functional decline.
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。
Audit factory production scheduling systems for finite capacity planning, Drum-Buffer-Rope (DBR) Theory of Constraints implementation, dispatching rules (EDD, SPT, critical ratio), sequence-dependent changeover optimization, MRP integration.
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。
Audit manufacturing predictive maintenance systems for OPC-UA/MQTT sensor data pipelines, time-series storage retention, ML model lifecycle (training-serving skew, drift detection, retraining triggers), MTBF/MTTF/MTTR reliability calculations, Weibull survival analysis.
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。
Generate a production-grade OEE (Overall Equipment Effectiveness) measurement pipeline for a manufacturing line — data ingestion from MQTT/OPC-UA/Modbus/CSV, A×P×Q calculation engine, Six Big Losses categorizer, MTBF/MTTR/TEEP metrics, Grafana or Streamlit dashboard.
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。
Whole-codebase analysis powered by Gemini 2.5 Pro's 2M token context window. Triggers: bounded per-module analysis misses inter-module patterns or when you need a full-graph view before a major refactor.
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。
Audit a manufacturing energy management system for monitoring quality, cost optimization, and compliance. Triggers: building or reviewing industrial energy platforms, building management systems, or sustainability reporting tools.
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。
Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.
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。
Audit a student information system for dropout risk prediction. Analyzes attendance patterns, grade trajectories, behavioral indicators, early warning model accuracy, intervention tracking, equity in risk scoring, and socioeconomic factor integration..
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。
Analyze manufacturing defect detection and quality control systems — computer vision inspection pipelines, SPC control charts, Six Sigma process capability (Cp/Cpk), defect classification taxonomies, root cause analysis tooling, and measurement system analysis.
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。
Audit construction project management software for budget tracking accuracy, earned value management (EVM) formula correctness, risk factor modeling, schedule-cost integration, change order workflow completeness, and early warning detection..
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。
Analyze pharmaceutical batch production records for yield optimization, process parameter tuning, deviation trending, and cycle time reduction under cGMP compliance. Triggers: phrases: "optimize batch yield", "analyze batch records", "pharma manufacturing analysis".
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 @vercel/error to design, implement, migrate, audit, or review structured TypeScript errors. Trigger when a project uses or adopts the package, or when work involves its codes, fields, factories, HTTP APIs, telemetry, or terminal output. Use native Error for local failures that need no stable identity, recovery guidance, observability, or transport.
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。
Set a repo up to prove engineering-task work actually works before it ships. Investigates the repo, ensures a one-command dev stack (`dev-local`) exists, asks whether verification runs locally or in a sandbox (crabbox), confirms/installs the driver (the `playwright-cli` skill for web by default). Outputs three artifacts: a committed `/verify` skill (per-task verification SOP — spawn a verifier sub-agent → drive the app → screenshot/video proof → open a PR with the proof embedded), the `/dev-local` skill + script, and the installed driver skill. Use when someone says "set up verification", "make this repo verifiable", "scaffold a verify skill", "set up the verifier".
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。
Audit a repo's agent context — CLAUDE.md files, codebase docs, skills, and tool/MCP designs — against Anthropic's Claude 5 context-engineering guidance ("unhobbling": Anthropic cut ~80% of Claude Code's system prompt with no eval loss). Finds overconstraint, conflicting instructions, redundancy, stale facts, and missing "unknown knowns"; produces a scored findings report with concrete rewrites, then applies approved fixes. Use when someone says "audit my CLAUDE.md", "context audit", "unhobble this repo", "review our agent docs/skills/tools", or after upgrading to Claude 5-generation 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。
**WORKFLOW SKILL** — Persist analysis so it survives context compression and chains between tasks. USE FOR: any task whose decisions affect future work — features, refactors, architecture changes, integrations. Produces a lightweight Task Analysis Map that externalizes intent, decisions, and chain context. Use to document decisions, create continuity, prevent context loss, bridge tasks, externalize reasoning. DO NOT USE FOR: quick intent validation (use task-intent), deep structured analysis (use contextação).
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。
Translate existing blog posts into one or more target languages with SEO-optimized localization. Produces native-quality translations that preserve markdown structure, frontmatter, schema JSON-LD, image and chart embeds, and evidence-backed explanations. Localizes keywords, meta tags, numbers, dates, currencies, and quote styles per locale. Flags machine-translation artifacts for review. Run BEFORE blog-localize: this handles language conversion; localize handles cultural adaptation after translation completes. Use when user says "translate blog", "blog translate", "uebersetzen", "traduire", "traducir", "translate post", "blog auf Deutsch", "blog en espanol".
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 and manage writing personas with NNGroup 4-dimension tone framework (Funny-Serious, Formal-Casual, Respectful-Irreverent, Enthusiastic-Matter-of-fact). Personas define readability targets, sentence length distribution, vocabulary tier, contraction frequency, and summary box label. Used by blog-write and blog-rewrite to enforce consistent voice. Use when the user says "create persona", "use persona", "list personas", or wants tone sliders and writing rules saved as structured JSON. Not for measuring style from existing posts (blog-style) or for audience, positioning, and taboo-phrase context files (blog-brand).
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。
Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Not for verifying statistics (blog-factcheck) or gathering authority sources. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".
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。
Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).
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 art-route layer of the Forge skills. route_media.py is the one command that generates an image or an image-to-video clip - through the paid API of any provider whose key is configured (OpenAI, Google Gemini, xAI, BytePlus ModelArk, fal.ai; the configured key is the owner's consent), else through the user's own signed-in Codex or Grok CLI - and prints one JSON line with the route, artifact, sha256 and cost estimate; with no route it answers no-route (codeart2d is then the last resort). Also the capability check (forge_doctor), API key configuration, the spend ledger, resume and Codex image adoption. Use when a Forge skill sends a generation here, for the capability check, or when the user asks about keys, routes or spend. Not for planning, processing or QA of sprites, video frames or maps (generate2dsprite, video2dsprite, generate2dmap) or for code-drawn art (codeart2d).
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。
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
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 designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, realtime voice agents, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.
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。
PM (Project Manager / Scrum Master) agent. Reads BA and TechLead artifacts and produces sprint plan, task breakdown, and story point estimates. Creates one tracked todo/checklist item per sprint task for live pipeline tracking (FR-42). Part of the Virtual Team Skill pipeline.
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。
BE Dev (Backend Developer) agent. Reads BA + TechLead + PM artifacts and generates backend source code files, .env.example, and pr-description.md. No hardcoded credentials. Part of the Virtual Team Skill pipeline.
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。