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概要と使いどころ
Implement or change ROS 2 Jazzy packages, nodes, interfaces, launch files and configuration in a colcon workspace. Use for building and validating a development change, including tests that report success without running. For a live QoS, TF, IMU or odometry fault, use ros2-troubleshooting.
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Run structured exploratory testing sessions using test charters, timeboxing, and heuristics. Use when user says "exploratory testing", "charter-based testing", "explore the app", "unscripted testing", "session-based testing", "find unknown bugs", or needs to test an area without predefined scripts - even if they don't explicitly say "exploratory".
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Write an empowered team brief. Use when the user says "team brief", "product brief", "give the team a problem", "empowered team", "how do I brief engineering", "delegate to the team", "product mission", or wants to give a team clear goals without dictating the solution - even if they don't say "team brief".
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Assess team health using Patrick Lencioni's Five Dysfunctions of a Team model. Use when a leader says "my team feels off", "we have trust issues", "people aren't speaking up in meetings", "accountability is a problem", "we keep missing commitments", "the team is siloed", "we're not working well together", or "I want to do a team health check". Also trigger when someone describes symptoms like passive agreement in meetings, blame culture, missed deadlines without escalation, or people optimizing for their own metrics over team goals.
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Define the scope, success criteria, roles, and boundaries for a consulting engagement. Use when a consultant says "help me scope this engagement", "I need to define what's in and out", "the client keeps adding things", "scope is getting fuzzy", "we need a contracting conversation", "I'm not sure what we actually agreed to", "help me set boundaries with a client", or "what should be in a kickoff". Also trigger when describing an engagement that feels like it is expanding without agreement, or when starting a new client relationship and preparing the initial contract or SOW.
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**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。
Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.
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 a bounded, extractive, structurally idempotent rollup of recent Obsidian wiki log entries, with dry-run preview by default and one optional transaction apply. Use for manual log compression without modifying child pages. Triggers: fold the log, run a fold, run wiki-fold, log rollup, roll up log entries, commit the fold.
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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.
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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。
Use when the user wants Reasonix to build, refine, test, or validate a CLI-Anything harness for a GUI application or source repository. Adapts the CLI-Anything methodology to Reasonix without changing the generated Python harness format.
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 Hermes Agent to build, refine, test, or validate a CLI-Anything harness for a GUI application or source repository. Adapts the CLI-Anything methodology to Hermes without changing the generated Python harness format.
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 OpenClaw to build, refine, test, or validate a CLI-Anything harness for a GUI application or source repository. Adapts the CLI-Anything methodology to OpenClaw without changing the generated Python harness format.
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Use when the user wants Codex to build, refine, test, validate, or list CLI-Anything harnesses for GUI applications or source repositories. Adapts the full CLI-Anything methodology to Codex without changing the generated Python harness format.
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Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.
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。
Prove existing work meets acceptance conditions without expanding scope. Use for validation-only tasks, completion checks, focused gate runs, and last-mile proof.
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。
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads. Don't use for simple text-based chat applications or workloads without real-time streaming requirements.
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Inspects, searches, and reads files and POSIX metadata on Google Cloud Filestore (NFS) instances without local NFS client packages or root privileges. Use when browsing Filestore shares, searching files, reading remote logs, or inspecting file attributes; don't use for Cloud Storage buckets, Cloud NetApp Volumes, Persistent Disks, or modifying/deleting files.
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, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.
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Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre).
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。
Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.
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。
Phase 9 of the SRS workflow. Saves all project context to _context/ files so future sessions can resume without re-reading the full SRS.
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。
Phase 9 of the SRS workflow. Saves all project context to _context/ files so future Claude sessions can resume without re-reading the full SRS.
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。