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Found 921 Skills
Code Review Expert: Perform in-depth code reviews using context-isolated subagents, covering security vulnerabilities, performance optimizations, and production reliability
Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.
Gate every generation through a brand policy file.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Scan GitHub Actions workflow files for security vulnerabilities by reading the YAML and reporting findings directly — no external tools, no installation, no shell execution. Use this skill whenever the user shares a `.github/workflows/` file, pastes workflow YAML, asks for a CI/CD security review, mentions `pull_request_target`, `workflow_run`, action pinning, `GITHUB_TOKEN` permissions, pwn requests, template injection, cache poisoning, secret exfiltration, supply chain risk, or any GitHub Actions hardening topic. Also trigger when the user is hardening an OSS repo, doing a CI/CD red team assessment, evaluating a target for supply-chain scanning, or writing publicly about CI/CD security. Bias toward triggering this skill rather than answering from memory — CI/CD security defaults are wrong almost everywhere and the rules are unintuitive.
Rebase the current branch onto the latest main. Use when asked to rebase, sync with main, update branch, or pull latest changes. Fetches and rebases onto latest main; does not push automatically.
Report which PR is being worked on in the current conversation. Use when asked about the current PR context.
Audit the OpenSSF Scorecard rating for py-lintro. Use when asked to check the scorecard, understand the rating, or find what's missing. Specific to github.com/lgtm-hq/py-lintro.
QSF project conventions for the Playwright E2E suite. Use when working in the QSF playwright-tests directory (authFixtures, ___ping-* handles, data-test-label test IDs, .auth storage states) — not for generic Playwright advice.
Consolidate worktrees and clean up stale branches. Use when asked to reconcile, clean up worktrees, consolidate branches, or tidy up a project's git state.
Pre-commit workflow and commit guidelines. Use when asked to commit changes. Requires passing lint and tests, signed commits, semantic prefixes, imperative mood.
Playwright E2E testing best practices. Use when writing browser tests, visual regression, or accessibility tests in any project. Enforces user-facing locators, auto-waiting, web-first assertions, and Page Object Model.