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概要と使いどころ
Verify an open factory PR before human review - re-runs gates, checks the test proves the fix, checks scope and test tampering. Use when reviewing a factory PR, running the PR review routine, or asked to independently check a change.
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。
Independently verify a factory pull request, including fail-closed gates, negative test proof, scope, and load-bearing review.
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。
Claim and implement one ready GitHub issue, run fail-closed gates, obtain independent verification, and open a draft pull request.
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。
Bootstrap a Claude-assisted on-call for this channel/repo: discover the available connectors, mine incident history into draft triage playbooks, interview the human for policy, validate against held-out incidents, and install the scheduled routines. Use when the user wants to "set up on-call", "bootstrap the on-call kit", "onboard this channel", or has just installed the oncall-kit plugin. Five gated phases — never run more than one phase per turn, and never activate anything before Phase 4 sign-off.
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。
Investigate an alert or incident in this channel: classify the symptom, load the matching triage reference, check lessons.md for known causes, and post a grounded first-pass diagnosis with evidence links and a proposed (never executed) fix. Use when an alert fires, a routine detects a new anomaly, or someone reports something broken ("tests aren't running", "deploys look stuck", "is CI down?").
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。
Automated security scanning for dependencies, code, containers with Trivy, Snyk, npm audit. Use for CI/CD security gates, pre-deployment audits, compliance requirements, or encountering CVE detection, outdated packages, license compliance, SBOM generation errors.
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。
Integrates payment processing with Stripe, PayPal, or Square including subscriptions, webhooks, and PCI compliance. Use when implementing checkout flows, recurring billing, or handling refunds and disputes.
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。
Interview employees to identify recurring daily work, office workflows, and repetitive tasks that can be automated, co-piloted, or supported by AI. Use when the user wants to find what AI can take off their plate, map available tools such as ChatGPT/OpenAI, Claude, Microsoft Copilot, Excel or PowerPoint add-ins, Google Workspace, no-code/RPA tools, coding agents, or internal automation platforms, and write tool-aware specs with fallback paths.
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。
Code review practices with technical rigor and verification gates. Use for receiving feedback, requesting code-reviewer subagent reviews, or preventing false completion claims in pull requests.
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。
When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort. Use when the user asks for Codex or `codex exec`, when a change is complex or high-stakes enough that an independent reviewer would change the outcome, or when a delegated run needs its model, effort, or permissions chosen. For agents other than Codex 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。
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。
Apply a disciplined engineering workflow to any code change. Use whenever implementing a feature, fixing a bug, or refactoring — before writing code, not after. Walks orient → baseline → smallest change → test → verify → self-review, and enforces language-agnostic hard gates (don't mass-reformat, keep the linter and type-checker clean, keep the build and tests green, make interface changes additive, protect security invariants).
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。
Dependency upgrades: outdated/vulnerable deps planned into semver waves (patch/minor batched, majors gated and changelog-read), applied incrementally with lockfiles regenerated and tests green after each. Runs on green main. Triggers on "absolute upgrade", "upgrade our dependencies", "bump deps", "update packages", "move off the deprecated X", "clear the Dependabot backlog".
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。
Diátaxis-driven documentation for AI coding agents: write, improve, or audit tutorials, how-tos, reference, explanation, and developer docs (README, CONTRIBUTING, ADRs). Detects the docs stack; gates on the outline before writing prose; verifies every claim against the code before it ships. Triggers on "absolute docs", "write docs", "write a tutorial", "write a README", "document this", "improve this doc", "audit our docs".
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。
End-to-end, phase-gated SDLC for AI coding agents: relentless design interview → reviewed spec → dependency-graphed task board → safe-wave TDD execution → verification → converge. Handles features, bugs, refactors, greenfield projects, planning breakdowns, and migrations. Triggers on "absolute work", "build this end-to-end", "plan and build", "break this into tasks", "grill me on this plan", "pick up this ticket", "run this migration".
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。
Conduct structured threat modeling for software systems using established methodologies to identify, prioritize, and mitigate security threats before they are exploited. Use when the user requests threat modeling or provides relevant inputs for this 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。
Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows. Use when deciding which agent actions require review, adding approve/reject or dual-control flows, preventing unauthorized autonomous effects, creating decision records, reducing rubber-stamping, or recovering safely from rejected, expired, or failed actions.
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 reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.
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, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.
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 Metrology Scientist. Use when a task calls for Metrology Scientist judgment. Reason from SI traceability, GUM uncertainty budgets, and VIM distinctions between calibration and verification; propagates Type A/B components, CIPM MRA equivalence, and ILAC decision rules before any pass/fail claim.
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 MLOps Engineer. Use when a task calls for MLOps Engineer judgment. Reasons from data contracts, feature parity, evaluation gates, and rollback-readiness through MLflow/W&B registries, Feast feature stores, KServe/Triton serving, Great Expectations/TFDV validation, and Evidently PSI/KS drift monitors while treating train-serve skew, data leakage, silent degradation, and schema/concept 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 Metabolomics Scientist. Use when a task calls for Metabolomics Scientist judgment. Reasons from MSI annotation levels, pooled-QC RSD and D-ratio gates, MZmine/MS-DIAL/XCMS pipelines, HMDB/GNPS identification, and MetaboAnalyst batch correction (ComBat, QC-RLSC); treats injection-order drift, ComBat over-correction, and Level-5 pathway stories 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 Manufacturing Engineer. Use when a task calls for Manufacturing Engineer judgment. Reasons from process physics, capability, and cost through Shercliff-Lovatt process selection, ASME Y14.5 GD&T, CAM simulation, and AIAG APQP/PPAP with MSA-gated SPC capability, treating high %GRR masquerading as variation, false Cpk on unstable or short runs, datum-scheme mismatch, and uncontrolled ECN tweaks 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。