「mutation」の検索結果

Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and ranked HDR enhancers. Use when designing a donor for a point mutation, epitope/fluorophore tag, allele replacement, or knock-in, or when HDR efficiency is low. Guide design and base/prime editing are separate skills.

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Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.

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Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.

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Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).

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EXPERIMENTAL — mutation testing with muter to measure whether tests actually assert anything: mutants that survive reveal assertion-free coverage. Advisory report only, never a merge gate. Use on engine/logic targets when coverage numbers look good but bugs still slip through.

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Property-based testing strategies, mutation testing, shrinking, and combined PBT+mutation workflow for test quality validation

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Runs feature-scoped mutation testing to validate test suite quality. Use after implementation to verify tests catch real bugs (kill rate >= 80%).

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Operately test layout, naming (.projections.json), factories, feature/e2e steps, external query/mutation auth specs, and how to run Elixir/API/JS/EE tests. Use when adding, renaming, splitting, or reviewing tests; choosing where a test belongs; writing Factory/TurboCase/FeatureCase or ExternalApi QuerySpec/MutationSpec tests; or running make test / feature tests.

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Mutation testing ile test suite kalitesini olc. Stryker, mutmut, go-mutesting destegi.

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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 FOR: .NET quality gates in CI; analyzer, coverage, mutation, and architecture-test choices; standardizing `.editorconfig`, `dotnet format`, and warning policy. DO NOT USE FOR: non-.NET repositories; generic CI/CD guidance with no .NET quality stack decisions. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Use the open-source free `Stryker.NET` mutation testing tool for .NET. Use when a repo needs to measure whether tests actually catch faults, especially in critical libraries or domains. USE FOR: the repo uses or wants Stryker.NET; mutation testing is needed for high-risk code. DO NOT USE FOR: every PR path by default in a large repo; simple coverage collection. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Writes Convex queries, mutations, actions, and internal functions in the object form with args and returns validators, correct ctx usage, runtime boundaries, and error handling. Use when adding or changing anything in convex/*.ts that exports a function, or when deciding between query, mutation, and action.

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Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity.

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Query ClinVar for clinical variant significance. Use when user asks about variant pathogenicity, genetic variants, clinical significance, or disease-causing mutations. Triggers on "clinvar", "pathogenic", "variant significance", "clinical significance", "disease variant", "mutation pathogenicity".

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Write effective unit tests with Jest, Vitest, or pytest. Covers the test-doubles taxonomy (stub/spy/mock/fake), Arrange-Act-Assert, coverage threshold configuration and CI gating, snapshot testing, fake timers, and mutation testing with Stryker/mutmut. Use when: "unit test," "Jest," "Vitest," "pytest," "mock," "coverage threshold," "test doubles," "mutation testing," "fake timers," "snapshot test." Not for: interpreting coverage reports or finding coverage gaps — use coverage-analysis; AI generating the test code for you — use ai-test-generation; auditing existing tests for smells — use ai-qa-review; browser/component rendering assertions — use cypress-automation or visual-testing. Related: coverage-analysis, ci-cd-integration, ai-test-generation, shift-left-testing.

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予測モデリング（教師あり機械学習 / 予測モデル / 精度評価 / 交差検証 / リーク、決定木 / ランダムフォレスト / GBDT / LightGBM / XGBoost / CatBoost、モデル解釈 / 特徴量重要度 / SHAP / PDP / ICE、時系列 / 季節性 / ARIMA / SARIMA / Prophet / VAR / 変化点 / 時系列予測）を実行したら必ずセットで出す図と値のルーター。train_test_split, cross_val_score, GridSearchCV, Pipeline, TimeSeriesSplit, DecisionTree, RandomForest, lgb., xgb., CatBoost, dtreeviz, shap., permutation_importance, PartialDependenceDisplay, statsmodels.tsa, SARIMAX, STL, adfuller, plot_acf, Prophet, ruptures がコードに現れたとき、または ユーザーが「予測モデルを作って」「精度を出して」「どの変数が効いているか」「来月を予測して」「季節性を見て」と 言ったときに使う。診断・リーク・ベースライン・残差に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、 対応する references/<手法>.md を読んでから実行する。回帰係数の推論・検定は statistical-inference-diagnostics、 効果推定は causal-inference-diagnostics を使う。

Reviews Remix v2 form code for manual fetch() mutations, native <form> misuse, wrong useNavigation/useFetcher choice, missing pending state, unbounded uploads, and intent-pattern violations. Use when reviewing form/mutation code in a Remix v2 codebase.

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Guides pytest test architecture for Python 3.11+ using behavioral contracts, risk, and maintenance-adjusted value. Use when choosing unit/integration/property/e2e boundaries, fixture strategy, coverage measurement, or mutation testing. Preserves existing project gates but does not invent test-pyramid ratios, coverage percentages, mutation-score targets, or tests for implementation detail.

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Four-stage alchemical code transmutation — nigredo (decomposition), albedo (purification), citrinitas (illumination), rubedo (synthesis) — with meditate and heal checkpoints between stages. Transforms tangled or legacy code into optimized, well-structured output through systematic material analysis. Use when transforming legacy code into modern equivalents, refactoring deeply tangled modules where incremental fixes keep failing, converting a codebase between paradigms, or when simpler refactoring approaches have stalled and a full-cycle transformation is needed.

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Works through the surviving mutants that cargo-mutants reports for packages/engine — classify each one as a real test gap, a mutant not worth killing, or dead code, then fix it and verify with `just mutants-diff`. Use when picking up the weekly "Mutatietesten" issue, when asked to improve engine test coverage, or when a pull request fails the Mutation Testing (diff) gate.

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Covers object-level authorization bypass in GraphQL APIs where introspection reveals hidden fields or mutations that accept arbitrary user/resource IDs without ownership checks. Trigger on keywords like "GraphQL", "query", "mutation", "introspection", "resolver", "node ID", "relay", "object type", "schema", "batching", or "alias". Applies to dual-stack REST+GraphQL apps, Relay-style global IDs, and unauthenticated resolvers.

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The GraphQL pipeline for SKMTC generators — authoring generators whose input schema is GraphQL SDL rather than OpenAPI. Covers `toGqlOperationEntry`, `GqlOperation`, `synthesizeArgsObject` (mutation args -> object schema), the GQL enrichment routing (`[id][rootKind][fieldName][variant]` — two nested subject keys where OAS has path+method), the `to<Lang>GqlOperationProjectionBase` companion factories, and the `GeneratorKey` shape `id|rootKind|fieldName|variant`. Use this skill ALONGSIDE `skmtc-generator` whenever the schema source is GraphQL SDL or the task mentions "GraphQL", "SDL", "GqlOperation", "toGqlOperationEntry", or GraphQL query/mutation generators. Engine rules (producers, register/insert, the axioms) stay in `skmtc-generator`; this skill carries only what differs for GraphQL.

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Mutation testing workflow. Systematically mutates source code to verify tests actually catch bugs. Multi-session with progress tracking.

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