february2015/dsh-taskswarm

DSH port of TaskPlane: dependency-ordered waves run in parallel git-worktree lanes, with task packets, cross-model review, and crash recovery.

dsh-taskswarm (TaskSwarm 蜂群) is multi-agent task orchestration: it arranges a batch of tasks into dependency-ordered waves from the task DAG, runs multiple AI workers in parallel lanes isolated by git worktrees, then automatically reviews and merges their output. Each task is a packet of PROMPT.md (mission/steps/constraints) + STATUS.md (progress) for durable memory across context resets; checkpoint discipline auto-commits at step boundaries so a crashed worker never loses committed work; an independent reviewer scores each task against its Review Level (PASS merges, REVISE sends back); a file mailbox lets workers and the supervisor communicate asynchronously; and a conversational supervisor shares your session with start/pause/abort/integrate commands, bilingual 中文/English notifications auto-detected from session language.

Workflow & Automation ★ 0 updated 2026-08-16
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Install

dsh plugin --profile web add https://github.com/february2015/dsh-taskswarm.git

Git-URL install per README (bilingual EN/zh; EN primary). npm dsh-taskswarm 404 verified 2026-09-03 — the README's dated 'back on Aug 19' notice is stale, so the documented git route is primary: dsh plugin --profile web add https://github.com/february2015/dsh-taskswarm.git (or clone + npm install && npm run build && dsh plugin --profile web add $(pwd)).

Compatibility

DSH web profile; git worktrees for lane isolation; Node per DSH requirements; upstream TaskPlane (Pi ecosystem) native port.

Details

Recent updates

README carries a dated install notice (Aug 17–18, 2026) about an npm unpublish cool-down; npm remains unpublished at enrichment time.

FAQ

How are parallel tasks isolated?
Every task runs in its own git worktree lane, with results merged into the taskswarm/orch integration branch after review.
What survives a crashed worker?
Checkpoint discipline auto-commits at step boundaries and each task keeps a PROMPT.md + STATUS.md packet, so committed work is never lost across context resets or crashes.
How is quality controlled?
An independent reviewer scores each task against its Review Level: PASS merges into the integration branch, REVISE sends it back for revision.

Alternatives

apheli0os/deepseek-harness-orchestrate · wellorbetter/dsh-product-delivery-workflow

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