Run an evaluation harness over AI output to catch quality regressions when the model or harness changes. Reads golden tasks (input + a known expected outcome) from an evals/ directory, runs each against its named target (a skill, prompt, agent, or product LLM feature), scores the output against the expected oracle with a rubric/assert/exact grader, compares to a git-tracked baseline, and reports pass/fail per task plus a regression diff. A run fails on any drop vs baseline, not just on missing a threshold. Concrete, not aspirational: golden tasks, a baseline, acceptance thresholds, and a model/harness-stamped regression check. Does NOT grade against the implementation's own output (the oracle is the golden expected outcome). Trigger phrases: "run the evals", "eval the prompt", "did the model upgrade regress anything", "score against golden tasks", "set up an eval harness".
Execute atomic task files (T-NNN-<slug>.md) or a standalone plan.md with disciplined pre/per/post gates and write status back to frontmatter, closing the loop with /atomize so reconciliation works without manual edits. Detects the project's test runner from package.json / pyproject.toml / go.mod / pom.xml / Cargo.toml / composer.json. Respects depends_on between tasks, picks the next unblocked task on resume, and offers to capture lessons when a task is abandoned mid-flight. Modes: single task (T-NNN), whole initiative folder (docs/work/<NNN>-<slug>/), or a plan.md that was never atomized. Trigger phrases: "implement T-001", "implement this initiative", "execute the plan", "work on docs/work/NNN-slug", "resume implementation", "continue the initiative".
Author end-to-end test scenarios (optimistic + pessimistic paths) from a task, initiative, product-spec, or freeform description, and generate committed Playwright test files into tests/e2e/. Each scenario is a two-part artifact: a human-reviewable plan (tests/e2e/scenarios/<slug>.md, carrying depends_on links to docs/work/<NNN>-<slug>/ initiatives and T-NNN tasks) plus generated .spec.ts files — web via the browser, backend via Playwright's request fixture — tagged @optimistic / @pessimistic so /e2e-run can scope by path type. The plan is a mandatory human-review gate: no test code is written until the user confirms the paths. Optionally grounds web selectors against a live app via the Playwright MCP for honest role/test-id locators. Does NOT run the tests (that's /e2e-run) and never auto-heals. Trigger phrases: "write e2e scenarios", "create test scenarios", "scenario for T-NNN", "test the happy and edge paths", "author e2e tests".
Decompose a plan.md into atomic task files (T-NNN-<slug>.md) inside an initiative folder under docs/work/. Auto-detects mode: INITIAL (no T-*.md present — propose tasks from plan, write T-*.md + index.md) or RECONCILIATION (T-*.md exist — diff plan against current tasks, propose new/revised/obsoleted tasks; verify status:done claims against git log + file existence; never edit already-done tasks — write follow-up tasks instead). Trigger phrases: "atomize the plan", "decompose into tasks", "break down the plan", "create tasks from plan", "reconcile tasks", "the plan changed", "what tasks need updating".
Build an evidence-based map of an unfamiliar or large/legacy repository before you change it. Gathers cheap, deterministic CLI signals (git-history territory, a dependency graph, contributor history) OUTSIDE the model's context window, then has the agent interpret the evidence into one short, decision-oriented artifact — docs/architecture/repo-map.md (modules, entry points, dependency directions + cycles, coupling hotspots, sensitive areas / blast radius, who-to-ask, and explicit unknowns). Read-only: never modifies source. Detects the stack (JS/TS, Python) to pick the right dependency tool. Trigger phrases: "map this repo", "understand this codebase", "onboard me to this legacy project", "where is it risky to change", "/repo-map".
Per-decision research skill: gather facts and reasoning around a specific technology choice, vendor selection, or technical investigation, and write the findings to docs/analyzes/<slug>.md as a point-in-time research snapshot. Two modes (auto-selected via user choice): INTERVIEW — facilitator asks structured questions, user provides facts; INVESTIGATION — spawns research subagents (web + code) and synthesizes. Reads docs/reference/ for project- specific context — any file type (PDFs, DOCs, link collections, curated bibliographies), with sibling .md indexes for binaries. Closes with anti-bias cross-check (devil's advocate + pre-mortem). Use BEFORE making a significant technical choice, NOT for product-level discovery (use /discover for that). Trigger phrases: "should we adopt X", "research <topic>", "evaluate <option>", "vendor selection", "<lib A> vs <lib B>", "investigate <technology>", "what should I know about <topic>".
Initialize the /docs directory in this project — scaffold docs/{architecture,analyzes,reference,work}/ plus the tests/e2e/ home for /scenario, with README.md files if absent. Idempotent. For brownfield projects, also offers to run Claude Code's built-in /init to generate CLAUDE.md from codebase analysis.
Generate a CI/CD pipeline for a project: a GitHub Actions workflow that lints, tests, and enforces a coverage gate (default 80%), publishes coverage to an external dashboard (Codecov), and optionally adds an AI code-review step on PRs (kukuvaia on a self-hosted runner, or the official Claude Code Action). Detects the stack (Python/uv, Node/npm) to emit the right commands and reuses /implement's runner-detection. Writes nothing until you confirm the file plan. Idempotent. Trigger phrases: "set up CI", "add a CI/CD pipeline", "add GitHub Actions", "add a coverage gate", "add AI code review to CI", "/ci-setup".