swarm-plan

name	swarm-plan
owner	baseline
description	Decompose an approved spec into a dependency-ordered swarm plan — one task per component, each with an explicit write_set. Produces `.claude/state/swarm/<slug>.json` with tasks + waves. The wave scheduler guarantees pairwise-disjoint write_sets within each wave so parallel dispatch is provably conflict-free.

swarm-plan — Phase 5.5: decompose for parallel execution

Invoked after /approve-spec and before /tdd on any spec that has ≥3 independent components worth parallelizing (per project.json → swarm.min_tasks_worth_swarming). For smaller specs, skip swarm and go straight to /tdd solo.

Post-§18 amendment. Beyond the canonical .claude/state/swarm/<slug>.json plan, swarm-plan ALSO emits a runtime sub-track overlay at .claude/state/swarm/<slug>.jsonl (JSONL) when the chosen workflow track contains a selector node whose swarm-implementation alternate is chosen. The overlay's single line is a transient Track record with selectable: false, track_id: "swarm-runtime-<slug>", and nodes[] enumerating the swarm-worker dispatches as can_parallel: true peers. The harness reloads runtime overlays from .claude/state/swarm/*.jsonl at the same time it reads .claude/workflows.jsonl, so the runtime Track set is the union. The overlay is deleted by /archive along with the rest of the workflow's swarm state.

Prereqs

1. .claude/state/spec_approvals/<slug>.approval exists (spec is human-approved).
2. docs/specs/<slug>.md exists and passes /spec-lint.
3. docs/scout/<slug>.md exists (component → file mapping is its job).

If any prereq is missing, stop and surface what's needed.

Output contract

.claude/state/swarm/<slug>.json:

{
  "slug": "<slug>",
  "spec": "docs/specs/<slug>.md",
  "created_at": <epoch>,
  "status": "planned",
  "tasks": [
    {
      "id": "T-001",
      "title": "<what the task does — one line>",
      "component": "<component id from C4 Component diagram>",
      "acs": ["AC-001", "AC-002"],
      "write_set": ["src/foo/bar.py", "tests/foo/test_bar.py"],
      "read_set": ["src/common/http.py"],
      "depends_on": []
    }
  ],
  "waves": null
}

You produce tasks[]. The validator (validate.mjs) computes waves[] deterministically via Kahn-with-disjointness.

Steps

1. Read upstream: the spec, the scout report, the approval token. If an older .claude/state/swarm/<slug>.json exists, confirm with the user whether to overwrite (replan) or abort.

2. Extract inputs from the spec:

   • Components: every Component(id, …) in the C4 Component diagram (there may be multiple C4 Component diagrams, one per container).
   • ACs: every row in the Acceptance criteria table.
   • Dependency edges: every A --> B in the spec's dependency-graph fence.
   • Behavior ↔ component mapping: for each AC, the sequence diagram it references names participants; treat each non-actor, non-external participant as a component this AC touches.

3. Get file mapping from the scout report: for each component id, the files that back it. If the spec introduces greenfield components not in the scout, propose new file paths and flag them under a "new_paths" note in your plan summary — they will be accepted by the boundary guard when declared in write_set.

4. Construct tasks — one per component (per swarm.granularity: component):

   • id: T-001, T-002, … in stable order.
   • title: one-line imperative description.
   • component: the C4 component id.
   • acs: every AC whose sequence names this component.
   • write_set: union of (component files) + (test files covering those ACs). Every file must be explicit; no globs.
   • read_set: files this task will consult but not modify. Advisory; not enforced.
   • depends_on: for each component B such that this component depends on B (per the dependency graph), include the task id of the task owning B.

5. Merge overlapping tasks where forced:

   • If two tasks share any file AND have no depends_on relationship, either introduce a depends_on edge (making them sequential across waves) or merge them into one task. Merging is preferred when they're on the same component.

6. Validate the plan:

   node .claude/skills/swarm-plan/validate.mjs docs/specs/<slug>.md .claude/state/swarm/<slug>.json
   

   The validator checks: required fields, depends_on references resolve, DAG is acyclic, and assigns waves[]. If validation fails, it prints a precise error — fix the plan and re-run.

7. Surface the plan to the user as a table:

   Swarm plan for <slug> — <N> tasks across <M> waves
   
   wave 1:
     T-001  webhook-worker       [AC-001, AC-002]   3 files
     T-003  backoff-policy       [AC-004]           2 files
   wave 2:
     T-002  webhook-retry        [AC-003]           2 files  (needs T-001)
   

8. Tell the user: "Swarm planned at .claude/state/swarm/<slug>.json. Review it, then run /approve-swarm <slug>. After approval, run /swarm-dispatch <slug>."

Constraints

• Never dispatch from this skill. Planning and execution are separated by a human consent gate (/approve-swarm).
• Every file in a write_set must be a concrete path, not a glob. The boundary guard does string-level membership checks.
• The validate.mjs script is the source of truth for wave assignment. Do not hand-write waves[].
• Greenfield files are allowed in write_set even if they don't exist yet — the guard checks declared ownership, not disk presence.
• If validation keeps failing, the problem is usually that two tasks share a file with no dependency. Either merge or introduce a dependency edge.