epic-react-patterns
Guide on React patterns, performance optimization, and code quality for Epic Stack
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Guide on React patterns, performance optimization, and code quality for Epic Stack
Guides implementation of new or changed eval checks in the giskard-checks package—Check subclasses, discriminated kind registration, public exports, and tests. Use when adding or modifying built-in checks, LLM-based judges, @Check.register kinds, serialization of TestCase or Scenario, or when editing files under libs/giskard-checks/src/giskard/checks related to checks.
Describes the merge-ready workflow for the giskard-oss monorepo using the repository root Makefile and the same steps as GitHub Actions—format, check, unit tests, optional pre-commit. Use when preparing or updating a pull request, confirming CI will pass, running pre-push validation, or when the user asks for a branch to be merge-ready.
Run the full code quality gate for the giskard-oss monorepo—format, lint, typecheck, and unit tests across all packages. Use when finishing a feature, before opening a PR, or when the user asks to check quality or run the gate.
Find the root cause of failing or flaky Karmada E2E tests from CI runs. Use when a user reports an e2e test failure, a flaky CI job, or asks to find the root cause of an e2e timeout, providing a GitHub Actions run/job URL, a PR number/URL, or logs.
Check health of MSBuild CI pipelines, VS repo PR insertion statuses, and VMR codeflow PRs. Use when asked about pipeline health, build failures, infrastructure issues, CI status, insertion PR status, VMR codeflow status, or for periodic health monitoring.
Profiler-driven hill-climbing to close the inference throughput gap between TorchTitan's unified model (running inside vLLM) and vLLM's native model. Benchmark with generate.py --benchmark, climb optimization rungs (compile / cudagraph / fused kernels), profile torchtitan vs the native target, then patch the single biggest gap at a time and re-measure. Use when the user wants to benchmark or optimize RL inference generation speed, reproduce previous hill climbing study, or invokes /inference_perf_hillclimb.
修复 DooTask 可写目录(bootstrap/cache、docker、public、storage)的属主/权限:chown 回当前用户 + 目录 chmod 775,对齐 install 的赋权逻辑,赋权不删数据。
首次部署 DooTask:前置检查后执行 `sudo ./cmd install`(建库 + migrate --seed 的重操作),刚性流程、单次确认、失败即停。