Reviews, test discipline, debugging, and accessibility audits.
Quality gates you can hand to an agent: code review passes that catch real defects rather than style nits, test generation and test discipline, systematic debugging workflows, and accessibility audits. Several of these are adversarial by design, built to argue with your code instead of agreeing with it. Reach for this category when the goal is finding what is wrong, not producing more.
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Full Sentry SDK setup for Node.js, Bun, and Deno. Use when asked to "add Sentry to Node.js", "add Sentry to Bun", "add Sentry to Deno", "install @sentry/node", "@sentry/bun", or "@sentry/deno", or configure error monitoring, tracing, logging, profiling, metrics, crons, or AI monitoring for server-side JavaScript/TypeScript runtimes.
Find and fix issues from Sentry using MCP. Use when asked to fix Sentry errors, debug production issues, investigate exceptions, or resolve bugs reported in Sentry. Methodically analyzes stack traces, breadcrumbs, traces, and context to identify root causes.
Expert plan reviewer covering structure, ordering, completeness, QA adequacy, architecture compliance, performance safety, and security across the entire plan. Use when reviewing or writing plans.
Debug and fix a Sentry issue — find it (by link, ID, or search), pull full context (stack trace, breadcrumbs, trace, logs), optionally run Seer root-cause / autofix, apply the code fix, and resolve it via a Fixes PROJECT-NAME-12A commit/PR. Use when working a known error or hunting one down to fix.
Set up Sentry, debug production issues, and configure application monitoring.
Multi-lens code review pipeline: deep review (Claude or Codex), automated fix loop, interactive walkthrough, manual promote, external-finding injection.
Make Sentry stack traces readable — upload source maps for JavaScript/TypeScript, or debug files for native and mobile (dSYM, ProGuard/R8, NDK symbols, Dart obfuscation maps, .NET PDBs). Use when frames in Sentry show minified names, bundled paths, hex addresses, "unknown", or method names with no file/line, instead of your original source.
Complete testing strategy covering TDD workflow, test pyramid, unit/integration/E2E/property testing, framework best practices (Jest, Vitest, pytest), mock strategies, and CI integration. Use when writing tests, reviewing test quality, or establishing testing standards.
Compare evaluated runs in a retort experiment along factor dimensions. Surfaces effects of each factor, aggregates across replicates, and highlights cells that diverge qualitatively — complementing (not replacing) retort's ANOVA analysis.
Determine the TRUE cause of a failed retort run before attributing it. Ground-truth every failure (run its tests, read its agent logs, inspect its workspace) and classify it as an infrastructure false-fail, a genuine model miss, or an environment issue — never trust the gate verdict or a log signature alone. Use whenever a run is recorded failed (test_coverage=0 / gate fail), a pass rate looks low, or you're deciding whether a "failure" is real before reporting or proceeding.
A Symfony-focused toolkit for Claude Code providing TDD (Pest/PHPUnit), Doctrine ORM, API Platform, Messenger, Voters, quality checks (PHP-CS-Fixer/PHPStan), and pragmatic architecture patterns - Docker Compose and Symfony Docker (FrankenPHP) compatible
Aggregate a retort run's findings.jsonl into a machine-readable assessment.json summary with severity counts, penalty score, requirement coverage, and top findings.
Evaluate a single retort experiment run. Score the generated code against the task's TASK.md requirements, run its build and tests, compute metrics, and emit a structured evaluation report plus a machine-readable findings file.
Run JUnit tests in Eclipse projects — all tests, by package, by class, or individual test methods. Also build Maven projects.
Composable AI skills framework covering the full software delivery lifecycle — design, architecture, implementation, testing, and review.
Load when investigating a failing PR CI pipeline or checking PR health. Attributes each CI failure as flaky, infra, or regression, proposes a targeted action, and reports code coverage and quality/security status.
Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.
AI-powered code review in Claude Code, powered by CodeRabbit
Generate a live Single Step Instrumentation (SSI) onboarding confirmation report — verifies APM instrumentation is working end-to-end with deep links into the Datadog UI. Only use after agent-install and enable-ssi have both completed successfully.
Load when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.
Create, edit, evaluate, and package Claude Code agent skills — full draft→test→review→improve lifecycle.
AI-assisted deep planning with research, interview, external LLM review, and TDD approach
Testing and quality assurance specialist for Solana programs. Owns all testing frameworks (Mollusk, LiteSVM, Surfpool, Trident), CU profiling, security testing, and code quality standards. Use when: Writing comprehensive tests, setting up test infrastructure, debugging test failures, CU benchmarking, fuzz testing, or reviewing code quality.
Persistent AI coding companion with 21 species and personality stats.
Systematic debugging approach for identifying and fixing issues
Write thorough tests following TDD and BDD principles
Graceful degradation and meaningful error messages. Errors are first-class citizens, not afterthoughts. Every error path is designed, not discovered.
Measure first, optimize second. Data-driven performance improvements with before/after benchmarks and production validation.
Ship software systematically: project lifecycle, TDD, parallel agents, code review, security auditing, and infrastructure validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
Detects and mitigates LLM hallucinations in production pipelines. Validates AI-generated facts, code, and decisions before they reach end users or downstream systems.
Sets up your project for effective AI-assisted development — optimized CLAUDE.md files, effective coding guidelines, formatter hooks, built-in code-quality agents, TDD and test coverage, all tailored to your actual codebase.
Test real system boundaries, not mocks of mocks. Integration tests verify that components work together, not that they work in isolation.
Systematic root cause analysis for production and development bugs. Hypothesis-driven debugging — never guess-and-check.
Plugin-based MCP server for React Native/Expo runtime debugging, inspection, and automation via Metro/CDP
Create, update, and fix Cypress tests. Connect to Cypress Cloud to see test results and use data to manage your test suite.
Analyzes Move language packages against the official Move Book Code Quality Checklist. Use this skill when reviewing Move code, checking Move 2024 Edition compliance, or analyzing Move packages for best practices. Activates automatically when working with .move files or Move.toml manifests.