Testing Claude Skills
3 543 testing skills from 425 authors. They run test suites, check accessibility and catch what broke. Half of them fit into 1 799 tokens or less — that is what one costs your context window when the agent loads it. 404 ship runnable scripts rather than instructions alone. 2 of them cannot work without an MCP server, most often rube. We also found 429 copies of these same skills sitting in other people's repositories — counted once here, not 429 times.
3 543 unique 425 authors 2 updated this month 331 from vendors
Test message variants on synthetic audiences. Use when: predicting response rates, sentiment, or objections before live tests.
A/B test content variations. Use when: comparing quality scores across prompt approaches, headline styles, or content versions.
Generate quality trends report. Use when: reviewing eval scores over time, content type performance, or regression alerts.
Simulate revenue impact via Monte Carlo. Use when: testing channel mix changes, budget shifts, or new channel launches.
Compare budget scenarios side-by-side. Use when: testing 2-4 allocation variants with projected outcomes.
> Pull and interpret production experiment query-performance data from the staff-only slowest experiment queries, precompute read/build health, and preaggregation cache footprint. and response field semantics (exception codes, exposure paths, precompute skip reasons, job states). Use when investigating slow or failing experiment queries, precompute regressions, 307/159/241 errors, preaggregation table growth, or when asked how experiment query performance or the precompute rollout is doing in production.
> Guides an agent through reproducing, root-causing, fixing, and validating flaky tests in the PostHog monorepo. Stabilizing is not the only valid outcome — the skill also gates whether the test should exist, so deleting a test that catches nothing real, or re-leveling one that flakes because of the level it runs at, are first-class endings.
> When and how to add scenarios, chaos events, and invariants to the personhog e2e test harness (rust/personhog-test-harness). Use after fixing a bug or regression in the personhog leader path (leader, router, writer, replica, coordination protocol) so the fix gets a permanent regression scenario; when adding a new failure mode to test (crashes, drains, zombies, lag, failover); or when a new correctness property needs asserting during runs. Trigger handoff bug, eviction, writer lag, acked write.
> Internal PostHog developer frontend/browser QA skill. Use only when a PostHog developer explicitly asks to run frontend QA, browser-test a PR, verify a UI flow against the local PostHog stack, use qa-frontend, or QA current frontend changes with browser/runtime evidence. Do not use for generic code review, PR review, "check my changes", CI debugging, or security audit; use qa-team, debugging-ci-failures, or security-audit instead. Runs in PR mode or local mode, plans adaptive browser and visual checks, drives browser MCP/tooling such as Playwright MCP or Chrome DevTools MCP, captures evidence, and applies only approved/narrow fixes.
Use when writing a Storybook story for a component gated on a feature flag — boolean flags or multivariate/experiment-arm variants. Covers the `featureFlags` story parameter and why imperatively setting flags renders the flag-off branch in visual-regression snapshots while passing in jest.
> Teaches how to write and run evals on the `products/posthog_ai/eval_harness/` harness — sandboxed agent suites that execute the real coding agent in a Docker or Modal sandbox against a seeded Hedgebox project, and one-shot suites that score a single in-process model invocation per case. Use when adding or changing eval suites, cases, scorers, seeders, or synthesizers under `products/posthog_ai/evals/` or `products/*/evals/`, when touching the harness under `products/posthog_ai/eval_harness/`, or when running or debugging those evals (`hogli evals`). Covers suite kinds and discovery, case anatomy, the seeder/synthesizer split, the one-branch scorer patterns, and how to read results. Not for `ee/hogai/eval/ci/` pytest evals, and not for the LLM Analytics product's evaluation features.
> Gates whether a new test should exist and forces it to be efficient, protecting CI from low-value test bloat. Use before adding or substantially changing any pytest, Jest, or Playwright test — whenever an agent or engineer is about to write tests for a new feature, bugfix, or PR. Front-loads the value bar (every test must catch a realistic regression no existing test already catches; test behavior through the public interface, not implementation details; collapse near-duplicates into parameterized cases) and the efficiency bar (deterministic, isolated, fast; pick the cheapest test level; Django TestCase over TransactionTestCase; no sleeps, no real network). Includes a "don't write it" decision tree. For fixing an existing flaky test use `/fixing-flaky-tests`; after this gate says a Playwright test is warranted, use `/playwright-test` for mechanics.
Expert knowledge for Azure App Testing development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Azure Load Testing with VNets/private endpoints, JMeter/Locust, Playwright Workspaces, CI/CD, or quotas, and other Azure App Testing related development tasks. Not for Azure Test Plans (use azure-test-plans), Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure App Service (use azure-app-service).
Expert knowledge for Azure Test Plans development including limits & quotas, security, and integrations & coding patterns. Use when tuning test retention, configuring access for manual testers, or automating suites via tcm.exe CLI, and other Azure Test Plans related development tasks. Not for Azure DevOps (use azure-devops), Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure App Testing (use azure-app-testing).
Integration and E2E test design principles, ROI calculation, test skeleton specification, and review criteria. Use when designing integration tests, E2E tests, or reviewing test quality.
Implements React/TypeScript unit, integration, and browser E2E tests with the repository's configured runner, mocks, setup, and browser harness. Use when creating or completing frontend tests and generated test skeletons.
Language-agnostic testing principles including TDD, test quality, coverage standards, and test design patterns. Use when writing tests, designing test strategies, or reviewing test quality.
Add integration/E2E tests to existing codebase using Design Docs
Verifies the work plan is implementable end-to-end and resolves verification-lane / fixture / E2E-environment gaps before the build phase begins. Use when "implement-ready/verification readiness/lane setup/E2E environment missing" is mentioned, or before any build phase begins on a work plan whose readiness has not been preflight-checked.
Add integration/E2E tests to existing codebase using Design Docs
Verifies the work plan is implementable end-to-end and resolves verification-lane / fixture / E2E-environment gaps before the build phase begins. Use when "implement-ready/verification readiness/lane setup/E2E environment missing" is mentioned, or before any build phase begins on a work plan whose readiness has not been preflight-checked.
Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.
Performs comprehensive codebase analysis covering architecture, code quality, security, performance, testing, and maintainability. Use when user wants to audit code quality, identify technical debt, find security issues, assess test coverage, or get a codebase health check.
Build the project, run tests, lint, format, spell check, generate mocks, or verify the build passes for Bitwarden iOS. Use when asked to "build", "run tests", "lint", "format", "verify build", "check if it compiles", "run swiftlint", "run swiftformat", "generate mocks", or to execute any part of the build/test/verify pipeline.
Performs comprehensive code reviews for Bitwarden iOS projects, verifying architecture compliance, style guidelines, compilation safety, test coverage, and security requirements. Use when reviewing pull requests, checking commits, analyzing code changes, verifying Bitwarden coding standards, evaluating unidirectional data flow pattern, checking services container dependency injection usage, reviewing security implementations, or assessing test coverage. Automatically invoked by CI pipeline or manually for interactive code reviews.
Convert an XCTest file to Swift Testing framework. Use when asked to "convert to Swift Testing", "migrate XCTest", "convert test file", "xctest to swift testing", "migrate tests to Swift Testing", or when explicitly asked to convert existing XCTest-based tests.
> Diagnose and fix flaky (intermittently failing) tests in Bitwarden iOS. Finds the root cause of non-deterministic test failures (race conditions, timing issues, shared state, order dependence), applies a targeted fix, then stress-tests the fix by running the test 100 times to confirm stability. Use this skill whenever a test is failing intermittently, sometimes passes / sometimes fails, or someone describes a test as flaky, unstable, or non-deterministic — even if the exact "intermittent failure", "test is non-deterministic", "test fails sometimes", "test randomly fails".
Write tests, add test coverage, unit test, or add missing tests for Bitwarden iOS. Use when asked to "write tests", "add test coverage", "test this", "unit test", "add tests for", "missing tests", or when creating test files for new implementations.
Creates unit test YAML definitions that mock upstream model inputs and validate expected outputs. Use when adding unit tests for a dbt model or practicing test-driven development (TDD) in dbt.
Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.
Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.
| dd-trace-py integration development guide. Use when creating, modifying, or debugging contrib integrations in the Python tracer. Covers the patch module system, context_with_data, context_with_event (new), registration, testing with riot, and common anti-patterns. LLM/AI integrations should use this skill for APM-side workflow only; use llmobs-integrations for LLMObs-specific lifecycle, extraction, streaming, and VCR guidance. Pin is DEPRECATED. "trace_handlers", "PATCH_MODULES", "context_with_data", "context_with_event", "TracingEvent", "VCR", "cassette", "generative-ai", "LLM integration", "riot", "riotfile", "suitespec", "new integration", "wrap", "unwrap".
| dd-trace-py LLMObs integration development guide. Use when creating, modifying, or debugging LLMObs integrations for LLM/AI libraries in the Python tracer. Covers BaseLLMIntegration, stream handling, message extraction, token counting, tool call parsing, and VCR-based testing patterns. "_llmobs_set_tags", "BaseStreamHandler", "submit_to_llmobs", "integration.trace", "LLM span", "VCR", "cassette", "anthropic", "openai", "google_genai", "claude_agent_sdk", "generative-ai", "LLM integration", "llmobs_enabled".
> Validate code changes by intelligently selecting and running the appropriate test suites. Use this when editing code to verify changes work correctly, run tests, validate functionality, or check for regressions. Automatically discovers affected test suites, selects the minimal set of venvs needed for validation, and handles test execution with Docker services as needed.
>- Adversarially stress-tests a legal text — a contract, statute, regulation, or people governed by it will later disagree about what it means and turns each into a concrete dispute scenario with both sides' arguments, the likely outcome, and a fix. Use it whenever someone wants to pressure-test, red-team, audit, or find weak spots, loopholes, gaps, ambiguities, or drafting problems in a legal document; whenever a drafter wants to tighten a contract, statute, regulation, or opinion before it issues; whenever a litigator wants to mine an opinion or contract for arguments; or whenever someone hands over a legal text and asks where it will be fought over or for issue-spotting. Trigger it for contract review, statutory-ambiguity analysis, judicial-opinion scope analysis, and drafting QA — even if the user never says "stress-test" or "ambiguity."
Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.
> Builds a high-fidelity interactive legal assessment as a single self-contained HTML artifact. Output includes a live countdown timer, contract review tasks with hover-annotated problem clauses, candidate answer textareas, model answers hidden behind reveal blocks, scenario-based legal memo tasks, strategy and function-building questions, and a pre-submission checklist that encodes the marking criteria. Use when the user needs to (1) assess a legal candidate with a realistic timed exercise, (2) train or onboard junior lawyers using problem sets rather than doctrine, (3) help a candidate prepare for a real take-home assessment they are facing, (4) build educational materials for law students, in-house teams, or compliance training, or (5) produce scenario-based training modules on specific legal topics. Triggers on "legal test", "take-home", "mock exam", "contract redline exercise", "candidate assessment", "legal training exercise", "practice test", or similar phrasing even when informal.
Generate a spreadsheet of test entries — newly designated names from OFAC, OFSI, and EU sanctions lists plus deliberate variations of those names — to validate that a sanctions screening system catches fresh designations and is tuned to the right fuzziness threshold. Use this whenever the user asks for sanctions list update test data, screening regression test data, screening QA, fuzzy match calibration, or wants to verify their screening lists are current. Trigger even if the user doesn't say 'screening' explicitly — phrases like 'test my sanctions list', 'check our SDN coverage', 'is my list up to date', or 'build me a regression set from the latest designations' should also invoke this skill.
>- Act as experienced opposing counsel to attack, undermine, and expose weaknesses in a legal argument, submission, witness statement, or structured reasoning. 1. A core theory of attack identifying the single most effective way to defeat the argument; 2. A reconstructed version of the opposing argument stripped of rhetoric to expose its fragility; 3. Primary lines of attack grouped by category (legal misstatement, evidential gaps, causation failures, internal inconsistency, over-reliance on assertion, procedural weakness); 4. An "if I were the judge" section showing how a sceptical tribunal would dismantle the argument; 5. Surgical strikes - 3 to 5 high-impact points ready for oral submissions; and 6. An analysis of what the argument is trying to hide. Written in formal, adversarial British English for a legally trained audience.
| Prepare a lawyer for an adversarial proceeding the way Neal Katyal's "Harvey" prior opinions and questions, predict the specific questions you will face, map narrow "escape routes" each judge can walk through without abandoning prior commitments, and spar adversarially until only the strongest answers survive. The model is the sparring partner. The human still wins the case. evidentiary hearing, deposition (taking or defending), arbitration, mediation, or any proceeding where a known decision-maker will fire questions at you in real time. Also useful for stress-testing a brief before filing. named judges or arbitrators, drafts a brief and wants it pressure-tested, asks "what will Justice X / Judge Y ask me," or talks about preparing for a specific bench.
This skill stress-tests a proposed settlement position before an offer goes out or comes back: the assumptions it depends on, your leverage and the opponent’s, the evidential weaknesses, the likely opponent response, and the timing and costs pressures around it. It structures settlement judgment for a better-informed decision; it does not advise whether to settle.
>- Produces an adversarial attack on a legal argument that survives reply. Runs the turned on that pass to cut every point that collapses under challenge - attacks on conceded facts, wrong-forum objections, speculation about documents and motives, gotchas with innocent explanations, overclaims, self-refuting assertions, and padding. The deliverable is one standalone attack containing only what can be defended, capped at five heads, not a discussion of the discarded draft. Use to attack, stress-test, red-team or rebut a submission, brief, motion, witness statement, letter or structured legal reasoning. Triggers on "attack this but only with points that hold", "no cheap shots", "what survives reply", "which points can I actually defend", "give me the version I can file", "double-pass adversarial review". Also use when an earlier adversarial review came back overlong or scattershot. Formal British English.
Use when creating a Cubit or Bloc, modeling state with sealed classes or status enums, wiring BlocBuilder/BlocListener/BlocProvider, writing bloc tests, or choosing between Cubit and Bloc.
Use when calling callable functions (httpsCallable), passing data to server-side logic, handling function errors/timeouts, configuring regions, or testing with the Emulator Suite.
Use when running in-app message campaigns, triggering/suppressing messages, configuring opt-in data collection, testing on specific devices, or handling message callbacks.
Use when implementing feature flags, running A/B tests, setting parameter defaults, fetching/activating config, or enabling real-time config updates.
Use when writing E2E/integration tests, testing native interactions like permissions or system dialogs, capturing UI regressions, or validating cross-platform behavior (Patrol 4.x).
Use when testing RevenueCat purchases/subscriptions, setting up sandbox testing, debugging a purchase/restore/trial, verifying entitlements or events, or writing an IAP QA plan.