「mutation」の検索結果

Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".

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Use when you want to benchmark and improve an existing repo skill through repeated evals, prompt mutations, and score tracking.

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。

Skill Optimization: Auto Research. Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

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Audit and safely clean Xcode-related developer storage on macOS. Use whenever a developer mentions low disk space, Xcode storage, DerivedData, simulator devices or runtimes, stale beta platforms in Xcode Settings, DeviceSupport, archives, dSYMs, CocoaPods caches, simulator dyld caches, project .build folders, downloaded Xcode DMGs/XIPs, duplicate Xcode installers, or old Xcode applications. Also use this skill proactively when you hit low storage during other work, such as a build, download, or install failing with an out-of-disk-space error. Always measure first, report recoverable GiB with evidence, and obtain explicit itemized approval before any mutation.

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kubernetes API analysis, service-account trust, RBAC edges, admission and controller behavior, cluster secrets, workload mutation, and namespace-scoped drift. Use when the user asks to inspect kube API permissions, service-account tokens, RoleBinding or ClusterRoleBinding edges, admission webhooks, controller-created pods, secret exposure, or why live workloads differ from manifests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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实现 API 调用 / 缓存 / 加载 / 错误处理 / 乐观更新时使用。覆盖 TanStack Query / SWR / RTK Query / Apollo Client / urql / Pinia Colada / Pinia Query。融合 stale-while-revalidate + 乐观更新 + Mutation + 无限加载。

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Records and replays browser sessions using rrweb recording library with DOM mutation serialization. Integrates with LogRocket API and FullStory Data Export API for session analytics.

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Inspect and maintain the repository's .pm workstreams, inbox notes, milestones, and tasks. Use when the user asks for PM board status or requests a supported board mutation such as creating, promoting, adding, or completing work.

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 fail-closed consent, scope, undo, and verification rules to TouchDesigner mutations made with tdmcp.

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 or refine open-source .NET code-quality gates for CI: formatting, `.editorconfig`, SDK analyzers, third-party analyzers, coverage, mutation testing, architecture tests, and security scanning. Use when a .NET repo needs an explicit quality stack in `AGENTS.md`, docs, or pipeline YAML.

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Canon TDD for humans and AI agents. Use for production code changes by writing tests first, proving RED, implementing minimal GREEN, and refactoring safely. 2026 edition adds TDP, flakiness gate, ralph-loop integration, memory-search in Step 0, PBT as Step 5.5, mutation testing gate in Step 4, and CJS LSP warning.

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Living catalogue of 7 known hazard entries (SE-01 through SE-07) specific to agent-studio: Windows backslash paths, prototype pollution, hook exit codes, async swallowing, ReDoS in glob-to-regex, DST arithmetic, array mutation during iteration. Each entry: symptom, root cause, fix, test assertion.

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Hunt GraphQL-specific vulns — introspection abuse, field-level authorization (IDOR), batched queries for brute force, alias-based rate limit bypass, deep query DoS, mutation injection, schema disclosure, suggestion leakage, CSRF via GET, and CSRF via POST without preflight. Use when a GraphQL endpoint is present.

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Hunt reflected, stored, and DOM-based Cross-Site Scripting (XSS) including CSP bypass, polyglot payloads, mutation XSS (mXSS), markdown XSS, and template XSS. Use when the user has identified a parameter or input that reflects to the page, or wants to test for XSS in a specific input.

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Drive a GitHub Copilot (or any bot reviewer) pull-request review to a clean pass without babysitting it: fix each finding in its own commit, reply to the review thread with the fix sha, resolve the thread, re-request the bot, and poll for the async re-review. Covers the thread node-id (PRRT_...) vs comment databaseId distinction, the resolveReviewThread GraphQL mutation, the copilot-pull-request-reviewer[bot] slug, and how to read the bot's verdict (COMMENTED plus a "human review recommended" banner is not a blocking finding). Use when Copilot has left review comments on your PR, when bot review threads must be closed out with an auditable fix-reply-resolve trail before merge, or when you need to verify that a re-review actually landed on the new HEAD rather than the old one.

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Vue 3 form patterns: native v-model, vee-validate + zod (most common), VueUse helpers, defineModel for custom inputs, controlled vs uncontrolled, field arrays, multi-step wizards. Use this skill to: - Wire vee-validate with a validation schema (zod / yup). - Build custom form components with defineModel (Vue 3.4+). - Implement multi-step forms. - Handle async validation. - Integrate forms with TanStack Query mutations or Pinia actions. Do NOT use this skill for: - General SFC conventions (see vue-conventions). - State management (see vue-state-management). - Routing (see vue-routing). - Testing forms (see vue-testing).

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State management decision tree for React SPAs: useState/useReducer, Context API, Zustand, Jotai, Redux Toolkit, TanStack Query, SWR. Detect what's installed and apply matching patterns. Use this skill to: - Pick the right state tool for the data shape (local / shared UI / server / form). - Use Context correctly (and know when to switch to Zustand/Jotai). - Implement a Zustand or Jotai store. - Set up TanStack Query for server state with caching, pagination, mutations. - Avoid common mistakes (overusing Redux, prop drilling, forgetting query invalidation). Do NOT use this skill for: - General hook conventions (see react-conventions). - Form state specifically (see react-forms). - Routing state (see react-routing). - Testing stores/queries (see react-testing).

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。

Form patterns for React: react-hook-form (most common), Formik (legacy/stable), TanStack Form (newer). Validation via zod / yup / valibot. Controlled vs uncontrolled inputs, field arrays, multi-step wizards. Use this skill to: - Wire react-hook-form with a validation schema. - Pick between controlled and uncontrolled patterns. - Build multi-step forms with state preservation. - Handle async validation (e.g., username availability). - Integrate forms with TanStack Query mutations. Do NOT use this skill for: - General state management (see react-state-management). - Routing (see react-routing). - Component conventions (see react-conventions). - Testing forms (see react-testing).

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。

Data fetching, caching, and revalidation patterns in Next.js App Router. Covers native fetch caching, ISR/SSG/SSR per-route choice, generateStaticParams, unstable_cache, revalidatePath/revalidateTag, Route Handlers, dynamic vs static rendering. Use this skill to: - Pick the right rendering mode (SSG / ISR / SSR / streaming) per route. - Use native fetch caching options correctly. - Build Route Handlers that match REST conventions. - Invalidate cached data after mutations. - Use generateStaticParams for dynamic SSG. Do NOT use this skill for: - RSC vs Client boundaries (see server-component-patterns). - Routing primitives (see nextjs-routing). - General conventions (see nextjs-conventions).

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Assemble the human genetic evidence for and against a target before a programme commits to it — the evidence class that most improves the odds of surviving clinical development. Use this skill to pull gnomAD constraint metrics (LOEUF, pLI, observed/expected) that show whether loss of function is tolerated in people, retrieve GWAS Catalog associations and fine-mapped credible sets for a gene, and read a natural human knockout as a safety readout. Also trigger on gnomAD, LOEUF, pLI, loss-of-function intolerance, mutational constraint, GWAS Catalog, credible set, human knockout, genetic support, or target safety dossier.

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Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), RNAi DEMETER2 scores, PRISM compound sensitivity, and gene effect profiles across the cell-line panel. Use for identifying cancer-selective vulnerabilities, separating pan-essential genes from selective ones, finding synthetic lethal interactions, correlating dependency with mutation, expression and copy number, and validating oncology drug targets. Also trigger on DepMap, Chronos gene effect, CRISPRGeneEffect.csv, DEMETER2, PRISM repurposing, co-essentiality, pan-essential, or ACH- cell line identifiers.

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Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.

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 and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein and protein-ligand systems with PDBFixer, choose force fields and water models (AMBER14, CHARMM36m, ff19SB, GAFF2, TIP3P), solvate and add ions, run energy minimization, NVT/NPT equilibration and production MD on GPU, then analyze trajectories for RMSD, RMSF, radius of gyration, hydrogen bonds, native contacts, PCA and free energy surfaces. Use this skill for protein stability under mutation, ligand binding-mode and residence-time questions, conformational sampling, membrane proteins, and disordered ensembles. Also trigger on OpenMM, MDAnalysis, mdtraj, Simulation.step, LangevinMiddleIntegrator, PDBFixer, DCD or XTC trajectory, RMSD analysis, or production 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。

Use when the user asks to rank deployment platforms and stacks against their product with quantitative trade-offs. Not for source or remote mutation.

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。