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概要と使いどころ
Claude Code Bash tool patterns with hooks, automation, git workflows. Use for PreToolUse hooks, command chaining, CLI orchestration, custom commands, or encountering bash permissions, command failures, security guards, hook configurations.
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 bounded investigation, review, or implementation tasks to Antigravity CLI from a supervising agent. Includes a background client for task progress, follow-up prompts, conversation recovery, cancellation, and visible failures.
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 task may benefit from a bounded subagent for implementation, large-codebase discovery at task start, independent review, requested or pre-commit code simplification, or expert work on complex decisions, modeling, automation, or repeated failures. The main agent chooses whether to delegate and accepts the result. Do not use for simple questions or short work with no meaningful review or delegation.
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 privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.
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。
Think and work like an expert Mining Geologist. Use when a task calls for Mining Geologist judgment. Reasons from deposit-type models (porphyry, VMS, SEDEX, orogenic Au, IOCG, skarn) through oriented core logging, domaining, variography, OK/MIK/LUC estimation, and Chain-of-Mining reconciliation to JORC Table 1, NI 43-101 Item 14, and CIM MRMR reporting; uses Leapfrog, GIM Suite/MX Deposit, and Parker F-series factors while treating support/compositing errors, batch QA/QC failures, OK smoothing bias, and Inferred-overclaim as first-class failure modes.
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。
Think and work like an expert Health Informatician. Use when a task calls for Health Informatician judgment. Reasons from semantic interoperability, provenance, and patient safety through FHIR/US Core, SNOMED-LOINC-RxNorm terminology mapping, OMOP/OHDSI ETL with DQD/Achilles, and chart-review PPV validation while treating immortal-time and confounding-by-indication bias, patient-matching/MPI failures, billing-code phenotypes, and vocabulary version drift as first-class failure modes.
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。
Think and work like an expert Galactic Astronomer. Use when a task calls for Galactic Astronomer judgment. Reasons from distance ladders, dust extinction, and survey selection functions through Gaia DR3 cross-matches, isochrone and Bayesian SFH fitting (PARSEC/MIST, Starfish), and orbit integration in named potentials (McMillan17, MWPotential2015) via galpy, Agama, and Gala, while treating parallax-S/N and RUWE failures, unresolved binaries and fiber collisions, dust-or-crowding overdensities, and spiral-arm-mimicking-streams as first-class failure modes.
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。
Think and work like an expert Computer Graphics Researcher. Use when a task calls for Computer Graphics Researcher judgment. Reasons from the rendering equation, Monte Carlo bias/variance, sampling theory, and BSDF energy conservation through pbrt-v4/Mitsuba 3 references, MIS/ReSTIR/path guiding, DXR 1.2 real-time stacks, 3DGS and NerfBaselines protocols, FLIP/ColorVideoVDP evaluation, OpenUSD/MaterialX/OpenPBR interchange, and ACES 2/OCIO color while treating unconverged references, unequal-time comparisons, denoiser and temporal-reuse bias, scene- vs display-referred color errors, and white-furnace energy failures as first-class failure modes.
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。
Think and work like an expert Civil Engineer. Use when a task calls for Civil Engineer judgment. Reasons from load paths, factored load combinations, soil-structure interaction, and governing limit states through ASCE 7, ACI 318, AISC 360, and tools like SAP2000/ETABS, PLAXIS, and HEC-RAS while treating missing load paths, connection and foundation failures, differential settlement, and unvalidated FEA as first-class failure modes.
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。
Think and work like an expert Aerospace Engineer. Use when a task calls for Aerospace Engineer judgment. Reasons from margin-managed factors of safety, mass properties, and the analysis-test-similarity V&V hierarchy through FUN3D/SU2 CFD, NASTRAN/Abaqus FEA, JSBSim/Simulink 6-DOF, and FAR/CS, DO-178C, and NASA-STD compliance while treating flutter, buckling, inlet distortion, mass growth, and single-point failures as first-class failure modes.
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 the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.
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。
Continuous self-improvement system for Hermes Agent. Analyzes your past sessions to find recurring failures and skill gaps, then automatically creates or patches skills and runs self-evolution to fix them. Set it to run overnight and wake up to a better agent. Use /dojo to start.
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。
Internal — for Boundless team members only. Cross-reference Boundless indexer API data, broker telemetry, and service logs to investigate production and staging operational issues. Use when the user wants to understand why slashings happened on prod/staging, diagnose prover or service failures in deployed environments, correlate market events with broker behavior, investigate fulfillment rate drops, look at prover/service logs, or perform any analysis that requires combining on-chain indexer data with off-chain broker telemetry and CloudWatch logs. Also use when the user asks to "investigate", "diagnose", or "find root cause" for prover, service, or market issues on live networks. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.
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 the user wants to author, design, scope, or refine an Agent Skill (a SKILL.md file). Trigger phrases include "build a new skill", "design an agent skill", "scope a SKILL.md", "how should I structure this skill", "write a skill for X", "my skill isn't working well", or any request to improve an existing SKILL.md. Walks the user through an empirical, test-first process — probe the agent for real failures, design only for genuine knowledge gaps, iterate against runnable examples, and verify across 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。
Core agent-browser usage guide. Read this before running any agent-browser commands. Covers the snapshot-and-ref workflow, navigating pages, interacting with elements (click, fill, type, select), extracting text and data, taking screenshots, managing tabs, handling forms and auth, waiting for content, running multiple browser sessions in parallel, and troubleshooting common failures. Use when the user asks to interact with a website, fill a form, click something, extract data, take a screenshot, log into a site, test a web app, or automate any browser 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。
Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.
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。
Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.
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 production telemetry as INPUT to design new tests. Covers OpenTelemetry integration with tests, trace-based assertions, log-informed test creation, production-error analysis for coverage gaps, and telemetry-driven test prioritization. Use when: "trace-based testing," "design tests from logs," "OpenTelemetry assertions," "production errors point to test gaps," "telemetry-driven testing." Not for: safe rollout techniques (flags, canary) during release — use testing-in-production. Not for: scheduled post-deploy probes — use synthetic-monitoring. Not for: triaging CI failures — use ai-bug-triage. Related: testing-in-production, synthetic-monitoring, qa-metrics, ai-bug-triage.
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。
Remove AI-generated patterns from QA reports, bug reports, test summaries, status updates, and quality communications. Detects and rewrites robotic test-result language, template-sounding status updates, inflated severity descriptions, and generic stakeholder reports — without inventing facts. Makes QA writing sound like a real engineer wrote it. Use when: "humanize report," "rewrite QA summary," "fix test report," "make this sound human," "clean up status update." Not for: general prose, blog, or marketing-copy cleanup — use the global humanizer skill. Not for: classifying or routing CI failures — use ai-bug-triage. Related: ai-bug-triage, qa-metrics, qa-dashboard, quality-postmortem.
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。
Hybrid fingerprint + LLM pipeline for bug classification, deduplication, and ticket generation. Normalizes CI logs, creates stable fingerprints, clusters near-duplicates, then uses LLM for severity classification and ticket writing. Includes bug reporting templates and severity/priority matrix. Use when: "bug triage," "classify bugs," "failure analysis," "auto-classify," "CI failures," "bug report," "defect template." Not for: runtime self-healing of one flaky locator — use test-reliability. Not for: designing new tests from production telemetry — use observability-driven-testing. Related: qa-metrics, qa-dashboard, ci-cd-integration, qa-project-context.
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。
Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.
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。
Diagnose and fix a failing scheduled (or standalone) TMDb Integration workflow run — re-run transients, or fix real drift on a branch off main, then PR and (optionally) merge. Use for the weekly Sunday Integration cron failure, or any red Integration run not tied to an open 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。
Autonomously cut a Rove (`@sma1lboy/rove`) release end-to-end — detect the semver bump from pending changesets (flagging an upstream `minor` you didn't intend), run the release gates, dispatch the Changesets workflow (or run `scripts/release.sh` locally), then poll the GitHub Actions Release workflow with `gh` until npm publish completes, diagnosing CI failures (npm token, registry 404, lint, branch mismatch) instead of leaving them silent. Use when the user says "cut a release", "ship a version", "release Rove", "release rove", "发版", "release.sh", or "bump the version". Never force-pushes; always verifies the release landed on `main`.
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。
Production incident response - from detection through resolution to post-mortem. Effective communication, systematic investigation, and blameless learning from failuresUse when "incident, outage, production issue, site down, on-call, post-mortem, war room, severity, pages, alerts, rollback, incident, outage, on-call, post-mortem, production, reliability, SRE, communication" 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。