Results for “mutation”

Analyzes a repository and its test suite to generate customized mutation operators tailored to the project. Use this skill when setting up mutation testing, improving test quality, or generating project-specific mutants. Considers language constructs, business logic patterns, API calls, data types, and code complexity to produce effective mutation operators that maximize test sensitivity while avoiding trivial or equivalent mutations. Generates prioritized operator sets (high/medium/low value) with impact summaries. Triggers when users ask to generate mutation operators, customize mutation testing, analyze code for mutation opportunities, or create project-specific mutants.

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Optimize test suites using mutation testing to maximize mutation kill rate with minimal tests. Use when you need to reduce test suite size while maintaining quality, identify redundant tests, improve mutation coverage, or validate test effectiveness. The skill analyzes test coverage, execution intervals, and redundancy using mutation operators, selects or generates a minimal subset of tests that maximizes mutation kill rate, and outputs an optimized test suite with detailed reports showing killed and surviving mutants. Supports multiple programming languages and mutation testing frameworks.

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Assess test strength by introducing deliberate code changes with mewt or muton. Configure test runs, examine changes that tests miss, distinguish harmless equivalents from testing gaps, and investigate potential bugs in the affected code.

• Set up and scope a mutation testing run
• Investigate code changes that tests fail to detect
• Distinguish equivalent changes from testing gaps

Analyzes surviving mutants from mutation testing to identify why tests failed to detect them. Takes repository code, test suite, and mutation testing results as input. Identifies root causes including insufficient coverage, equivalent mutants, weak assertions, and missed edge cases. Automatically generates actionable test improvements and new test cases. Use when analyzing mutation testing results, improving test suite effectiveness, investigating low mutation scores, generating tests to kill surviving mutants, or enhancing test quality based on mutation analysis.

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Evaluate how well existing tests catch code changes, then combine the results with function relationships. Get a report separating harmless findings, missing unit tests, and targets for fuzzing, which tests many generated inputs.

• Triage code changes that existing tests failed to catch
• Prioritize missing unit tests
• Find functions suited to fuzzing

Retrieve study, mutation, clinical, and sample data from cBioPortal and turn the results into concise summaries. Use it to browse studies, inspect molecular profiles, or look up mutations for specified genes.

• Find studies by keyword
• Look up mutations for a specified gene
• Review clinical and sample information

Implement and review browser API requests in OpenBot using shared patterns for reading and changing data. Covers cache keys, refreshing stored results after changes, error handling, and how components display request states.

• Adding a screen that displays server data
• Implementing create, update, and delete actions
• Investigating stale lists after an update

Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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Strengthen existing tests with fast-check by checking properties across generated inputs. Find bugs that example tests miss and reduce failing inputs to small, reproducible cases. Property testing is available; the other listed workflows remain planned.

• Checking parser behavior across generated inputs
• Testing serialization and deserialization round trips
• Finding pure functions covered only by example tests

Generates cryptographic test vectors, inputs with expected outcomes, by deliberately changing implementation code and finding changes existing tests miss. Verifies new vectors across independent implementations and compares detection rates before and after adding them.

• Finding coverage gaps in cryptographic test vectors
• Creating invalid inputs that exercise validation checks
• Building test vectors shared across independent implementations

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences

Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.

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Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.

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Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.

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Expert guide for property-based testing (fast-check, Hypothesis) and mutation testing (Stryker, mutmut) — AI-generated test properties, invariant discovery, mutation score optimization, and test suite quality verification / Panduan ahli property-based testing (fast-check, Hypothesis) dan mutation testing (Stryker, mutmut) — properti tes yang dihasilkan AI, penemuan invarian, optimasi skor mutasi, dan verifikasi kualitas suite tes.

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Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.

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Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.

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Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.

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Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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Evaluate whether tests catch defects in business logic, calculations, or state machines. Using Stryker, the skill introduces small code changes, reviews which changes escape detection, and helps strengthen tests where important behavior is insufficiently checked.

• Checking test quality beyond code coverage
• Introducing Stryker for a single module
• Strengthening tests for critical calculations

Explore public pathogen genome data to verify lineage names, summarize weekly sequence proportions, examine reporting delays, and compare mutation frequencies. Results include filters and denominators, with clear limits on what submitted sequences reveal about infections.

• Tracking weekly lineage proportions in submitted sequences
• Verifying current Pango lineage names
• Examining delays between sample collection and reporting

Build and update Convex backend code, including schemas, queries, authentication, storage, and scheduled jobs. Apply input and output validation, indexed data access, and deployment checks to verify the implementation.

• Designing schemas and indexed queries
• Implementing queries, mutations, and actions
• Wiring authentication, storage, and scheduled jobs

Find changes in observable behavior that existing tests could miss. Map public results to test assertions, identify gaps in boundaries, permissions, and calculations, and propose focused tests, with optional verification when requested.

• Check whether existing tests would catch a bug
• Find missing boundary and permission-denial tests
• Review exact amounts and rounding in financial calculations

Convert protein sequences into numerical features with Meta AI's ESM-2 for machine learning. Extract features for individual amino acids or whole sequences, score mutations, and predict which positions in a protein may contact each other.

• Extracting protein features for machine learning
• Scoring the effects of amino acid mutations
• Predicting contacts from protein sequences