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概要と使いどころ
**MEASUREMENT SKILL** — Benchmark framework for measuring skill effectiveness. A/B tests agent outputs with and without a target skill, scoring correctness, completeness, and pattern adherence. USE FOR: validating skill quality, comparing before/after skill changes, proving skill ROI with data. DO NOT USE FOR: general testing, unit tests, CI pipelines.
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。
One-command multilingual blog creation. Writes a blog post, translates it into user-specified languages, applies cultural adaptation, and emits hreflang tags, sitemap entries, and a CMS-ready language map. The complete write-to-publish pipeline for international content. Orchestrates blog-write, blog-translate, blog-localize, and (optionally) seo-hreflang. Use when user says "multilingual blog", "blog multilingual", "write in multiple languages", "international blog", "mehrsprachiger Blog", "blog multilingue", "blog multilingue", "create blog in German and French".
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 up a paid-media client, brand, account, data-source, privacy, and mutation-guardrail profile for Claude Ads. Use for onboarding, initial configuration, brand DNA, API tokens or credential profiles, environment-variable or keychain setup, connecting exports or read adapters, safe native or verified-local installation, curl-pipe-bash install requests, declaring KPIs, or preparing a new advertising project.
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。
Deep cultural adaptation of an already translated blog post for one target market: local examples, CTAs, legal references, statistic sources, and formality (Sie/du, tu/vous). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets. Use when the user says "localize blog", "cultural adaptation", "adapt for Germany", "lokalisieren", "localiser", "adaptar" and a translation already exists. Not for translating (use blog-translate), auditing a folder of translations (use blog-locale-audit), or the full write-translate-localize pipeline (use blog-multilingual).
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。
AI 视频生成全流程：通过 6 个阶段（剧本→角色/场景设计→分镜→参考图→视频生成→后期剪辑）将用户想法转化为完整视频。支持临时工作台（单独调用 LLM、VLM、文生图、图生图、视频生成）和一次性短流程 Pipeline（文艺短视频、动作迁移、数字人口播）。触发词：视频生成、AI视频、AIGC、创作视频、制作视频、AI画图、文艺短视频、静态短视频、动作迁移、数字人口播。
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。
Minimal personal narrated-video pipeline — a topic becomes a talking-head-free explainer MP4 (1080p or 4K) via script → Azure TTS (SSML) → Remotion. Use when the user wants a quick narrated video from a topic without the full video-podcast-maker machinery (no extra skills, no thumbnails/shorts/publish matrix). Do NOT trigger for heavy production needs — use video-podcast-maker for those.
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。
Run or plan Yuzu's `/test` pre-commit and pre-push validation pipeline with quick, default, full, instructions, and quarantine modes. Use when the user says `/test`, `/test --quick`, `/test --full`, asks for the full test gate, or needs the Yuzu test-runs DB workflow.
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。
Run Yuzu's Codex governance review pipeline on a commit range with slash-name parity to Claude `/governance`. Use when the user says `/governance <range>`, "full governance", or asks for the multi-gate security/docs/domain/ops review on commits or a PR.
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 working on the SAM3 plus Sapiens2 pose estimation pipeline, batch GPU processing, or VRAM and throughput issues.
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。
Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.
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。
Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.
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。
Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.
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 working on the FIFA Skeletal Tracking Light 2026 pipeline, monocular broadcast pose estimation, or vaila_sam.py FIFA commands.
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。
Write and harden reusable agent-loop specification documents for biomechanics, data science, and vailá coding work. Use when the user says `/preto-loop`, asks to write/create/forge/specify/document an agent loop, turn an iterative vailá task into an autonomous loop, make an overnight loop, or design a biomechanics, data-pipeline, markerless-tracking, testing, refactoring, or performance loop. Include external verification, scientific validity, GUI/CLI parity, reproducibility, memory, guardrails, and named terminal states. Do not use merely to repeat a fixed scheduled prompt, execute an existing loop, or create unrelated documents.
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。
Teaches the fundamentals of Google Cloud Build (GCB). Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
Full SRS workflow: Brainstorm → Spec → Plan (per-section files) → User Review → SRS Generation → Validation → Improvement Report → Context Save. Use when user provides a project topic and wants a complete requirements 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。
Full-pipeline orchestrator. Runs all 7 roles (BA → TechLead → PM → BE Dev → FE Dev → Tester → QA/QC) in sequence by loading and following each per-role skill. Uses pre_write_validator.py hook enforcement and each role's self-validation. Use per-agent commands ($team-ba, $team-techlead, etc.) for manual control.
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。
Plan a feature or system before implementation. Use when the user says "plan this", "I want to build X", "how do I implement Y", or when /ck:brainstorm produces a spec.md. Always run before /ck:cook. Modes (pick one): --fast (simple, single-file), --hard (research + red-team + validate). Composable flag (combine with any mode): --tdd — propagates into the cook pipeline. Every phase gets Design Constraints and Quality/Testing State; ck:cook confirms optional test and quality checks per phase.
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。
Independent senior-engineering quality auditor — architecture, ownership, domain integrity, abstraction, and maintainability. Use as `--gate` inside the ck:cook pipeline (blocks a phase on BLOCKER/HIGH/current-change MEDIUM findings) or call directly with `--audit`, `--diff`, `--changed` on any repository, directory, or file. Never writes tests and never edits production code — it returns structured findings only; ck:cook or ck:fix apply the fix.
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。
FE Dev (Frontend Developer) agent. Reads BA + TechLead + PM + BE artifacts and generates frontend source code files and pr-description.md. 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。
Always use this skill before claiming any work is done, fixed, passing, or complete — even if it seems obvious. Also use when receiving code review feedback from any source (human or automated), when finishing a task or major feature, before committing or creating a PR, or when about to express satisfaction with work. Covers three practices: receiving feedback with technical rigor (no performative agreement), triggering code review via /code-review command or cook/fix pipelines, and verification gates (run the command, read the output, then make the claim). Never skip — the cost of false completion claims is higher than the cost of verifying. References: code-review-reception.md, requesting-code-review.md, verification-before-completion.md.
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。
Tester agent. Reads ALL preceding artifacts (BA + TechLead + PM + BE + FE) and produces test plan, test cases (unit/integration/e2e), and bug report template. Declares Gate 2: UAT Readiness. 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。
Full SRS workflow: Brainstorm → Spec → Plan (per-section files) → User Review → SRS Generation → Validation → Improvement Report → Context Save. Use when user provides a project topic and wants a complete requirements 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。