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Found 3,814 Skills
Orchestrate F1 test drives to validate the Cyrus agent system end-to-end across issue-tracker, EdgeWorker, and activity rendering.
Systematically audit, improve, and enforce test coverage in any repository. Use when asked to improve coverage, add missing tests, set up coverage thresholds, audit test gaps, or wire coverage into CI/hooks. Works across ecosystems (TypeScript, Python, Go, Rust, etc.). Composes with the hk skill for pre-commit enforcement. Triggers on: test coverage, missing tests, coverage threshold, coverage report, untested code, coverage gap, coverage audit.
Debugging and Root Cause Localization for AscendC Operator Precision Issues. Used when operator precision tests fail (such as allclose failure, result deviation, all-zero/NaN output, etc.). Process: Error Distribution Analysis → Code Error-Prone Point Review → Experimental Isolation → printf/DumpTensor Instrumentation → Fix Verification. Keywords: precision debugging, precision issue, result inconsistency, error localization, allclose failure, output deviation, NaN, all-zero, precision debug.
Use when running Playwright via terminal CLI — `npx playwright test` (test runner), `codegen` (interactive recording), `screenshot` / `pdf` (one-off captures), and CI sharding. NOT for agent-driven real-time browser control (use `claude-in-chrome` MCP tools for that).
Background knowledge for droid-control workflows -- not invoked directly. True-input driver mechanics for real terminal emulator automation via headless Wayland compositor.
E2E 시나리오 생성, 실행, 자동수정을 통합 처리하는 스킬. "시나리오 만들어줘", "시나리오 실행", "e2e 실행", "E2E 돌려", "시나리오 돌려", "시나리오 변환", "녹화 변환", "e2e 변환", "codegen 변환", "e2e 생성", "시나리오 생성", "시나리오 검증" 키워드로 트리거.
Test-driven development with strict RED-GREEN-REFACTOR enforcement. Enforces: write failing test first, minimal implementation, then refactor. Use when adding new features or fixing bugs.
Ghost Inspector integration. Manage data, records, and automate workflows. Use when the user wants to interact with Ghost Inspector data.
Evaluate Omni AI query generation accuracy by running test prompts through the Omni CLI, comparing generated query JSON against expected results, and scoring accuracy. Use this skill whenever someone wants to evaluate Omni AI, benchmark Blobby, run regression tests, compare AI output across branches or configurations, test prompt variations, measure AI quality, run A/B tests on model changes, assess impact of context changes, or any variant of "run evals", "test Blobby", "benchmark query generation", "compare AI results", "regression test", "how accurate is the AI", or "measure the impact of my changes".
Converts CXAS golden evaluations to SCRAPI SimulationEvals test cases. Use when generating high-level, goal-oriented test cases from turn-by-turn evaluation JSONs, and when enriching test expectations with inferred tool calls.
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.
Visual + DOM diff between two recorded sessions at matching trajectory step ids; used for visual regression and replay verification