crap-analyzer
Use to produce a risk-based refactor + test plan for recently-changed code on a diff/branch/PR by computing CRAP (complexity × untested) on changed methods.…
Use when a new feature folder must be created for the DAE pipeline, or when discuss promotes/parks an idea. Triggers — "/engineer.feature-init", "create a feature", "start a new feature", "init a feature".
$ npx -y skills add swingerman/disciplined-agentic-engineering --skill feature-init --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/feature-initContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when a new feature folder must be created for the DAE pipeline, or when discuss promotes/parks an idea. Triggers — "/engineer.feature-init", "create a feature", "start a new feature", "init a feature".
name: feature-init description: Use when a new feature folder must be created for the DAE pipeline, or when discuss promotes/parks an idea. Triggers — "/engineer.feature-init", "create a feature", "start a new feature", "init a feature".
Create a feature folder for the DAE pipeline — `feature.md` (the Ready contract), the folder, a branch, and a tracker entry. Checkpoint 1.5.
Five paths:
Detect from-discuss vs standalone by whether a `feature_intake` payload is present; a tracker capture is signalled by a slug-less `tracker_ref` passed in (typically from `next`); a roadmap promotion is signalled by a `roadmap_ref` (item id) passed in; onboarding intake is signalled by `onboard` (or an explicit "formalize existing feature" request).
**Not for:** brand-new ideas worth exploring first (`discuss`), or editing an existing feature (`feature-edit`).
Before the steps below, create one TodoWrite todo per workflow step (the full list up front, as a roadmap). After Step 6 creates the feature folder, show the **pipeline breadcrumb**: run `${CLAUDE_PLUGIN_ROOT}/scripts/dae_progress.py <feature-dir>` and present its output — for a just-initialized feature (no `progress.md` yet) it renders the pipeline ahead. The breadcrumb is advisory and never blocks. See `${CLAUDE_PLUGIN_ROOT}/references/progress-indicator.md`.
1. **Resolve** — resolve the methodology root + manifest via `${CLAUDE_PLUGIN_ROOT}/scripts/dae_resolve.py` (see `references/resolving.md`). Exit 2 (no manifest) → point to `/engineer.onboard`. 2. **Gather intake** — from-discuss: read the payload. From an `intent.md` (async originator, `${CLAUDE_PLUGIN_ROOT}/references/intent.md`): read it as the seed, synthesize the required fields from it, and confirm with the human before creating. Standalone: **first, offer the path** — if the feature's shape is unclear, fuzzy, or UX-heavy (better discovered by building than specced up front), offer the **prototype-first path** (`${CLAUDE_PLUGIN_ROOT}/references/two-paths.md`): hand to `/engineer.prototype "<idea>"` instead, which iterates fast and converts to spec on convergence. Only when the human takes the spec-first route, interview — required fields one per turn (`title`, `slug`, `outcome`, `source_links`, `status`, `autonomy_level` if `status` is `ready`/`in-progress`/`done`, `scope`), optional fields (`target`, `owner`, `area`, `relevant_adrs`, `tags`, `size`, `validation_method`, `assignee`) bundled at the end. **`validation_method`** is a one-line description of how this feature will be validated beyond the default (passing acceptance + unit + mutation); examples: "manual smoke in staging", "canary 5% prod for 24h, watch dashboard X", "feature flag `new_checkout`, internal users for 1 week". A high `autonomy_level` should be matched by an explicit non-default `validation_method`. **`assignee`** names who executes the next checkpoint — `human` | `local` | `cloud` (default `local`); it is orthogonal to `owner` (who is accountable). `cloud` requests cloud delegation, which the dispatch router still gates per-checkpoint via `dae_delegable.py` (see `references/handoff-dispatch.md`). Onboarding intake: reverse-engineer the fields from the existing spec / branch / commits; set `status` to `in-progress` or `done` per how complete the work is. 3. **Validate** — slug format (kebab-case, lowercase, ASCII, ≤50, leading letter); `autonomy_level` ∈ `manifest.autonomy.allowed_levels` and within path overrides; any `status` other than `parked` requires `autonomy_level`; `relevant_adrs` exist; `assignee` ∈ {`human`, `local`, `cloud`} if present. Slug collision: if existing feature is `parked` → offer promote-from-parked (flip status, keep handoffs); if `ready`/`in-progress` → redirect to `feature-edit`; if `done` → reject. 4. **Decomposition check** — if scope spans multiple competencies or sounds like several PRs, surface it; user proceeds as one feature or re-invokes per sub-feature with `parent_feature` set. 5. **Allocate number** — scan `features/` for max `NNN`, +1, 3-digit zero-padded. (Parallel runs may race — solo work won't hit it.) **Onboarding intake exception:** inherit the existing number — a feature migrated from a Speckit `specs/NNN-slug/` keeps its `NNN`; do not renumber. 6. **Create** — `features/NNN-<slug>/` with `feature.md` (per the Foundation Design feature.md schema), empty `handoffs/`, empty `.build/`. Add `.build/` to `.gitignore`. Include `branch: <name>` in `feature.md` frontmatter — the slug for greenfield; the adopted branch for onboarding intake. This is read by `${CLAUDE_PLUGIN_ROOT}/scripts/dae_branch.py` at every later checkpoint's Step 0 to enforce branch hygiene. If a `validation_method` was provided in the intake, include it in the frontmatter too — downstream skills (`plan`, `consistency-check`
A methodology kit for engineering-led AI development — spec-driven, test-driven, charter-bound. ATDD + mutation testing + deterministic guardrails. AI agents do the typing. Engineers stay in charge of architecture, behavior contracts, and verification.
Repo: swingerman/disciplined-agentic-engineering
Use to produce a risk-based refactor + test plan for recently-changed code on a diff/branch/PR by computing CRAP (complexity × untested) on changed methods.…
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