Swarm

Contract

Field	Bound contract
Trigger	Run partitioned parallel coverage or races.
Authority	Reversible local: writes only named local artifacts; rollback is version control or undo. No remote mutation.
Side effect	Spawns isolated cloud workers. Workers run session-scoped; nothing published.
Done	Consolidated evidence table with gaps and dropouts returned.

Inputs

Must supply:

• Partition strategy or count. A partition count (integer, minimum 2) or a custom strategy that assigns disjoint work slices to each worker.
• Test target or scope. The exact test suite, file set, coverage mode, or race condition to run in each partition.

Optional:

• Concurrency cap. Defaults to 3. Cap concurrency even when more partitions are independent. Workers do not automatically scale up unbounded.
• Session label. A string to prefix each worker session name for traceability.

Refusal

• No concrete target or partition count: stop. Do not infer or extend the target.
• Partial failure (some workers failed, at least one produced evidence): return the consolidated partial evidence with the non-converged result. List the failed partitions by ID.
• Complete failure (all workers failed): return a blank table with the non-converged result. Name the failure class and the request that caused it.
• Non-converged: use non-converged when all workers fail, a batch produces broad unplanned edits, or the evidence cannot be consolidated.

Procedure

1. Bound the scope. Accept only the partition strategy and the test target. Done when: a concrete target and partition count are confirmed.
2. Derive partitions. Convert the partition strategy into N disjoint work slices, where N equals the partition count or the number of slices the strategy produces. Each slice must be independently runnable with no shared state. Done when: N disjoint slices are enumerated.
3. Cap concurrency. If concurrency cap is not supplied, default to 3. Do not spawn more than the cap simultaneously even if more partitions are available. Done when: the concurrency cap is set.
4. Spawn isolated workers. Spawn each worker in a fresh, isolated session. Each worker receives exactly one slice and runs the test target against that slice only. Workers do not coordinate; no shared state is assumed. Done when: every worker is dispatched.
5. Run to completion. Wait for each worker to finish. Each worker produces a local evidence artifact scoped to its slice: pass/fail per test, coverage percentage, and any dropouts (skipped, timed out, panicked). Done when: every worker has returned or failed.
6. Consolidate evidence. Collect every worker artifact. Merge into one table with one row per partition: partition ID, tests run, tests passed, coverage delta, dropouts. Add a summary row: combined tests, combined coverage, total dropouts. Done when: the consolidated table is assembled.
7. Report gaps and dropouts. Name every partition that produced a dropout or coverage below the expected threshold. For each gap, give the partition ID, symptom, and affected slice. Done when: every gap is named.
8. Stop, do not widen. Return the consolidated table and the gap report. Do not trigger additional workers, re-run failed partitions, or mutate the original source. Done when: the table and gap report are returned.

Output

Consolidated evidence table (partition ID, tests run, tests passed, coverage delta, dropouts; one summary row) plus a gap report naming every partition with a dropout or sub-threshold coverage by partition ID, symptom, and affected slice.