dynamic-workflow

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Plan-in-code fan-outs, adversarial verification, waves.

AI & Automation 249,097 stars 52845 forks Updated today MIT

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Skill Content

# Dynamic Workflow Skill Runs large fan-out work as a workflow: the plan, the loop and every intermediate result live in a script and on disk, so the parent's context holds only verified results. Covers one-shot fan-outs, adversarial convergence (attempts + refuters), and multi-wave campaigns that integrate dozens of worker branches. It does not make `delegate_task` durable across restarts; that is the kanban swarm's job. ## When to Use Reach for it when the unit of work is clear (a file, an endpoint, a record) and there are more units than one context can hold. Skip it for under ~10 units or for serial chains. For a refactor or fix campaign on hermes-agent itself, load `hermes-agent` (the dev workflow) alongside; this skill owns the fan-out shape. ## Prerequisites - `delegate_task` available and `delegation.max_concurrent_children` sized for the wave (default 10; the runtime rejects a `tasks=[]` larger than that with a clear error rather than queueing). `delegation.max_spawn_depth >= 2` only if children must fan out themselves. <!-- no-tmp: ok — explains why a literal /tmp is wrong --> - A writable run directory resolved from the terminal environment's temp dir (`$TMPDIR`, else the platform temp dir). Never a literal `/tmp`: Termux has no such directory and native Windows breaks on it. Use `<tmp>/wf_<name>_<uuid>/`, unique per run, so an interrupted earlier run cannot leave stale outputs to be misread. - `execute_code` for the deterministic layer (only `web_se...

Details

Author
NousResearch
Repository
NousResearch/hermes-agent
Created
1 years ago
Last Updated
today
Language
Python
License
MIT

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