mutation-testing plugins

by Claude Code Plugins

Mutation testing to validate test quality by introducing code changes and verifying tests catch them

by Claude Code Plugins

Mutation testing to validate test quality by introducing code changes and verifying tests catch them

by Claude Skills Maintainers

Validate test effectiveness with mutation testing using Stryker (TypeScript/JavaScript) and mutmut (Python). Find weak tests that pass despite code mutations. Use to improve test quality.

by Alberto Leal

Shrink a test suite without losing an asserted fact or a covered branch: a fact matrix of what each test pins plus per-test branch coverage and a verified covering set, deleting only on the intersection, with a mutant check for every relocated fact. Use when asked to trim, dedupe, consolidate, or reduce tests or test SLOC, when a suite grew by accretion, when deciding whether to delete, fold, or parametrize tests, or before cutting a test on 'coverage is unchanged' alone. Python/pytest.

by Melodic Software

Measures whether a test suite detects faults, not merely runs code. /mutation-testing:principles answers operator, mutant-state, and metric questions from the literature. /mutation-testing:setup verifies the mutation tool and writes tracked config. /mutation-testing:audit runs mutation analysis on the diff (or, with --exercised, on code the changed tests call), reports surviving mutants and why each lived, verifies source was restored, and can save survivors as findings.

by Matt Corbett

QA & test-automation team — agents (test-strategy-architect, e2e-automation-engineer, test-infrastructure-engineer) for a test suite that catches real defects without becoming a tax: the test pyramid (many fast unit, fewer integration, few E2E), deterministic end-to-end automation (Playwright/Cypress, resilient selectors, no fixed sleeps), test data and ephemeral environments, meaningful coverage (mutation over line %), and flaky-test quarantine. Deepens ravenclaude-core/tester-qa. skills, a decision-tree knowledge bank (test-level + flake-triage + double-selection + seam + blocking-gate trees, plus selector-resilience + test-data-strategy trees and a dated 2026 tooling map), a field-notes bank, a stdlib flake-rate/pyramid-ratio analyzer, LSP code-intelligence config (TS/JS + Python), best-practices, templates, commands, an advisory hook. Seams: contract tests -> api-engineering, CI gating -> devops-cicd, UI under test -> frontend-engineering. Requires ravenclaude-core@>=0.7.0.

by Aaron Tzeng

Cross-model delegation and adversarial review for CLI coding agents: one lead, six runtime adapters, and no change merges reviewed only by its own model family.

by XRenSiu

Done_When Pipeline v1.0 — turn fuzzy natural-language requirements into machine-verifiable completion contracts, then run a multi-agent acceptance loop against an implementation. Nine skills in a two-layer topology (per HTML v2 architecture). Layer 1: TWO contract producers + SIX independently-invocable review skills — `/acceptance-spec` (NL → EARS spec + done_when.yaml with existence/behavior/rules schema + spec-robustness.md anti-gaming companion), `/test-suite-generator` (EARS → 5-layer test pyramid: existence/unit/integration/e2e/mutation; the v0.x fitness rubric layer was retired per HTML v2 §3.5 fitness-check dissolution), and six review skills each user-invocable on their own: `/code-reviewer` (diff → findings, focus-driven: security/logic/perf/style/all; Detective Loop not flowchart; 5-finding cap; cross-vendor adversarial mode), `/qa-reviewer` (actually runs tests, classifies maintenance-vs-genuine failures, emits go/no-go), `/pm-reviewer` (Agent-as-Judge: LOCATE/READ/RETRIEVE atoms; requirements normalized from EARS/Jira/Linear/PRD/issue; 4-state TicketCompliance verdict where requires_human_verification is the formal home for genuinely-unautomatable evaluation), `/spec-drift-detector` (code archaeologist: detects spec/code factual divergence without judging which is correct; git_blame traces commit_introducing_drift; 3 divergence types: timing/behavior/contract), `/spec-gaming-detector` (assumes author is gaming; 6 RHD patterns absolute + diff mode; outputs spec_robustness_gaps for contract hardening), `/meta-judge` (synthesizes findings via 4 actions: dedupe/weight/arbitrate/classify; HARD WALL — does NOT re-review code; pluggable rules source). Layer 2: `/acceptance-fleet` is pure orchestrator — dispatches the 6 review skills in parallel against an impl, hands findings to /meta-judge, decodes verdict into four-state ratchet (DONE/FIX/SPEC_DRIFT/GAMING_RISK), persists every iteration to ratchet-log/iteration-NNN/. Anti-gaming structural guarantee: implementation agents MUST NOT see evaluator prompts; minimum medium isolation (mixed Claude sizes) enforced; cross-vendor (Codex/Gemini) preferred for adversarial-reviewer and spec-gaming-detector to break the Claude-reviewing-Claude sycophancy loop per Milvus benchmark. Borrows: PR-Agent diff schema + TicketCompliance 4-state; Greptile v3 Detective Loop; Anthropic Code Review verification step + fleet-by-focus; DevAI Agent-as-Judge (LOCATE/READ/RETRIEVE); Playwright Healer maintenance-vs-genuine; Dartmouth/Yale Meta-Judge (replaces multi-agent debate); 4-Eyes Principle from finance; Weaver framework for weighted weak-verifier ensemble; Komorebi AI Specification Self-Correction. Design philosophy: verifiable beats judgeable (even for things that feel subjective); debate amplifies bias (use meta-judge synthesis, not debate); independence-by-default (each review skill works standalone — done_when.yaml is just one of its consumers).

by Rhize Media

Rhize Media's software delivery workflow — plan a change, build it, test it, and get it independently reviewed before shipping — for developers building production Next.js, Sanity, and Vercel applications.

by Pavel Guzenfeld

Skills, slash commands, rules and a diff-scoped mutation gate for a ticket-to-PR software development lifecycle.

by Anselm Hahn

Audit AI-generated code quality across four independent domains — hallucinated dependencies, weak test assertions, contract drift, and agentic-loop reliability — with independent verification and a bounded self-optimization fix cycle.

by juntaki

Run and interpret MutantKit mutation testing for Swift and Apple-platform projects: check readiness, plan, run, inspect survivors, and gate CI on real test-suite strength rather than line coverage.

by Skylar

Verification-first workflows for finding real bugs, fixing them at root cause, and making measured improvements. Prove-it-or-don't-report-it: every finding carries an evidence tier and a proof artifact, and fixes ship a failing-then-passing regression test fixed at root cause. v2 adds test-suite validation (flaky + mutation testing), characterization safety nets, root-cause sibling sweeps, regression bisection, and a regression guard.

by Skylar

Verification-first workflows for finding real bugs, fixing them at root cause, and making measured improvements. Prove-it-or-don't-report-it: every finding carries an evidence tier and a proof artifact, and fixes ship a failing-then-passing regression test fixed at root cause. v2 adds test-suite validation (flaky + mutation testing), characterization safety nets, root-cause sibling sweeps, regression bisection, and a regression guard.

by Sadjad Asadi

Local MLX worker models make behaviour-preserving code changes and write unit tests, behind a gate a machine can run. Claude plans and reviews; only survivors come back.