基础设施与 DevOps技能

Use when skill or knowledge activation is unreliable, or setup reaches activation reliability. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when work needs an isolated git worktree: new work, an existing ref, a gated feature workspace, a manifest-tracked loop run, or a candidate patch captured without touching the current checkout (`capture-isolated-patch`). Not for plain branches: use create-branch. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when the user asks to create a new branch or start work on one. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when enrolling a project in OSS-Fuzz, running its helper workflow locally, or reproducing an OSS-Fuzz report. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when a LibAFL fuzzer needs an executor, observer, feedback, mutator, scheduler, or objective composed around a target. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when initializing, running, measuring coverage, or triaging a cargo-fuzz target in a Rust crate. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when an artifact or skill has just changed and is about to be called done, committed, or handed off. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when a workflow, plan, diff, or completed work cycle must be checked for practices that punish the project later. Not for source, remote, credential, publish, deploy, or irreversible changes.

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Use when a user needs coverage-guided fuzzing for Python code or a Python native extension using Atheris. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when non-trivial code needs a design, codebase design or architecture needs improving, or one module needs targeted interface narrowing, seams, or testability. Not for diagrams, deploy, or irreversible changes.

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Use when onboarding to a repository, capturing costly conventions, constraints, or rationale, or improving AGENTS.md. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when asked to run AST-based structural search, lint, or rewrite of code when regex is too fragile. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when the user says deslop, debloat, tidy, simplify, clean a diff, cleanup codebase, or deslop branch diff, or remove dead code or config. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when asked to implement under non-negotiable constraints: performance budgets, platform limits, legal or API rules. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when writing or verifying framework-specific code, boilerplate, or a documented, correct implementation. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when the user asks to make the skills framework work in a new harness, IDE, or CLI. Not for remote, credential, publish, deploy, or irreversible changes.

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Use when a user wants to define patterns the agent should not do. Don't use for remote, credential, publish, deploy, or irreversible changes.

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Use when asked to optimize code, make a path faster, make this as fast as possible, reduce allocations, repair a regression, profile a target, grill every inefficiency, or run a subsystem or repo-wide performance campaign against a measured floor; when a performance requirement, slowness report, Core Web Vitals miss, or profiling evidence identifies a bottleneck; or for an "extremely optimize" performance campaign, optimizing suspected hot paths without waiting for benchmarks, or estimating hot and complexity-neutral cold paths while refusing complexity theater. Not for remote, credential, publish, deploy, or irreversible changes.

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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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Expo (EAS) iOS/Android アプリのデプロイ自動化スキル。 App ID 登録、証明書・プロビジョニングプロファイル作成、 EAS ビルド、TestFlight 配布、App Store 提出までを CLI から実行する。 任意の Expo プロジェクトで使える汎用スキル。 Use when: App ID を登録したい、証明書を作りたい、Bundle ID を設定したい、 EAS でビルドしたい、TestFlight に配布したい、App Store に提出したい。 Triggers: "App ID", "Bundle ID", "証明書", "certificate", "provisioning", "プロビジョニング", "eas credentials", "eas build", "eas submit", "TestFlight", "App Store 提出", "署名", "signing"

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Setting up application error tracking — SDK integration, grouping, and context — works with any error-monitoring platform.

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FastAPI guidance — typed endpoints, Pydantic validation, dependency injection, async patterns, and production deployment.

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Google Cloud guidance — core services, IAM, VPC networking, GKE/Cloud Run choices, BigQuery, and cost control.

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Deploy and operate endpoint detection and response — policy tuning, behavioral detections, threat hunting, and response actions.

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