About swarm
Swarm is an optional dispatch skill for callers that provide complete, disjoint work packets and select an executor. It validates the full batch, runs each packet exactly once, and returns per-packet results or errors without retrying, scheduling work, or issuing verdicts.
Key features
- 01One-time dispatch per packet
- 02Whole-batch validation before execution
- 03Per-packet candidates, evidence, or errors
- 04Preserves packet and context identities
- 05Rejects unsupported write-scope exclusions
Use cases
- 01Run independent, explicitly scoped packets in parallel
- 02Collect separate results from selected executor calls
- 03Prevent partial launches caused by invalid packets
- 04Return executor failures without automatic retries
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/swarmin Claude Code to confirm it loaded.
Bundled filesscripts/dispatch_once.pytests/test_dispatch_once.py
Details
From the project READMESwarm
Swarm exposes one optional factory port:
dispatch_once(explicit_disjoint_packets, executor)
-> per-packet candidate | evidence | error
The caller supplies every complete packet, proves their write scopes disjoint, and chooses the executor. Swarm dispatches each packet once, preserves packet and context identities, collects results, and stops.
Write scopes must be workspace-relative and canonical — symlink-resolved and
already normalized. The disjointness check is lexical: it case-folds prefixes so
scopes differing only by case are treated as a collision (safe on
case-insensitive filesystems), but it cannot see a symlink that aliases two
scopes onto one target. Supplying non-canonical or symlinked scopes forfeits the
disjointness guarantee; a non-empty write_scope.exclude is rejected, not
silently ignored, because the proof cannot honor it.
Exactly-once dispatch over proven-disjoint scopes is why parallel failures stay independent: no packet can observe, block, or corrupt another, so N packets yield N factual results about N experiments rather than one tangle. Those results are not semantic verdicts.
Named failure mode — partial-batch launch: dispatching valid packets before discovering an invalid one, leaving the batch half-run; validate the entire batch before the first call.
Anti-pattern: re-dispatching a packet whose executor returned an error. Corrective: return the error as that packet's factual result; retry is the caller's decision, not the dispatcher's.
The developer-only reference implementation is scripts/dispatch_once.py.
Repository tests exercise this module; the supported native installed path
follows this skill through the caller-selected executor and does not invoke Python.
It validates the entire explicit batch before the first call, invokes the supplied
executor exactly once for each packet, and returns executor exceptions as factual
per-packet errors.
Swarm's own effect is invoking the selected executor once per packet; the real blast radius rides on the packets. Each packet's transitive effects — whatever its executor writes, runs, or reaches — are the caller's to declare on the packet, not Swarm's to bound.
Swarm does not select work, create packets, schedule from a backlog, persist a queue, claim ownership, retry, validate, integrate, close, use Git, or deliver. Executor failures remain executor evidence and cannot become core phase or verdict state. The adapter cannot select AgentOps semantics, issue a binding verdict, or turn factory completion into delivery or validation proof.