diff --git a/skills/autonomous-ai-agents/dynamic-workflow/SKILL.md b/skills/autonomous-ai-agents/dynamic-workflow/SKILL.md index 2844c00cf5a..0a35fc106b6 100644 --- a/skills/autonomous-ai-agents/dynamic-workflow/SKILL.md +++ b/skills/autonomous-ai-agents/dynamic-workflow/SKILL.md @@ -47,7 +47,7 @@ the split is enforced by a real capability boundary, not a style preference: |---|---|---| | Use for | DETERMINISTIC fan-out — fetch N URLs, parse N files, run N shell commands, template N outputs | LLM-JUDGMENT fan-out — classify, review, decide, write, refute, audit per item | | The script holds | the loop + branching + intermediate vars (real Python) | n/a — you call it once with a `tasks=[...]` array; each task is its own isolated agent | -| Tools available inside | `web_search, web_extract, read_file, write_file, search_files, terminal, patch` ONLY (the `SANDBOX_ALLOWED_TOOLS` set) | configured child toolsets, subject to delegate restrictions (leaf children are stripped of `delegate_task`, `clarify`, `memory`, `send_message`, `execute_code` — see `DELEGATE_BLOCKED_TOOLS`) | +| Tools available inside | `web_search, web_extract, read_file, write_file, search_files, terminal, patch` ONLY (the `SANDBOX_ALLOWED_TOOLS` set) | full toolsets per child (configurable) | | Can it call `delegate_task`? | **NO.** `delegate_task` is NOT in `SANDBOX_ALLOWED_TOOLS`. Do not write a script that imports it — it will fail. | itself, if `role='orchestrator'` and `max_spawn_depth>=2` | | Concurrency | you control it in Python (`ThreadPoolExecutor`, batches) | `delegation.max_concurrent_children` (default 3; raise in config.yaml) | | Cost shape | cheap — most steps are tool calls, no per-item LLM unless you call `web_search`/aux | one model call tree PER child task — multiplies linearly, can be very expensive | @@ -81,11 +81,7 @@ So a "workflow" in Hermes is one of: path would time out or when the user must be able to walk away. Never promise "background, resumable, hundreds of agents over days" from a plain -`delegate_task` call. For a durable multi-agent workflow *graph*, the kanban -swarm is the right fit. For simpler durable/out-of-turn cases there are lighter -options too: a `cronjob` one-shot or scheduled job, or a managed -`terminal(background=True, notify_on_complete=True)` process — both survive the -turn without standing up a full task graph. +`delegate_task` call. That is the kanban path or nothing. ## Workflow recipe (foreground) @@ -94,28 +90,19 @@ turn without standing up a full task graph. (else it's serial, not fan-out — see when_not_to_use). 2. **Deterministic pre-pass (Layer A).** In one `execute_code` script, gather the manifest: list the files, extract the candidate sites, fetch the raw sources, - compute anything regex/parse can compute. Write a manifest to a **unique - per-run** directory — `/tmp/wf__/manifest.jsonl` (one unit per - line), never a bare `/tmp/wf_/` that a prior interrupted run could have - left stale outputs in. This is the "plan in code." Print the unit count and - the run dir, and stop. + compute anything regex/parse can compute. Write a manifest to + `/tmp/wf_/manifest.jsonl` (one unit per line). This is the "plan in + code." Print a count and stop. 3. **Size the fan-out** against `delegate-task-output-patterns`: chunk so each child handles ~8-12 mechanical file edits OR ~2000-3000 lines of reading OR - ~50-70KB of corpus. Look at the LARGEST unit, not the average. One - `delegate_task(tasks=[...])` call is bounded by - `delegation.max_concurrent_children` (default 3) — it does NOT queue hundreds - of tasks internally. For larger fan-out, issue bounded waves yourself (loop: - one batch, collect, next batch) or have the user raise the config - intentionally. + ~50-70KB of corpus. Look at the LARGEST unit, not the average. 4. **LLM-judgment fan-out (Layer B).** Issue ONE `delegate_task` with a `tasks=[]` array, one task per chunk. Each task: reads its slice from the manifest, - emits delimiter-separated lines to `/tmp/wf__/out_.csv`, prints a + emits delimiter-separated lines to `/tmp/wf_/out_.csv`, prints a status word, stops. Do NOT depend on the `summary` field for content. -5. **Synthesize on the parent.** Read the out_*.csv files yourself — verify the - file count and freshness (each was written this run) so a stale or missing - output from an interrupted child isn't silently read as success — then merge - and present. The cross-cutting "whole picture" step stays on the parent — only - the per-unit work fanned out. +5. **Synthesize on the parent.** Read the out_*.csv files yourself, merge, + present. The cross-cutting "whole picture" step stays on the parent — only the + per-unit work fanned out. ## The novel mechanic worth building: adversarial convergence @@ -168,14 +155,9 @@ inherent, not a bug. Two real multipliers: - **Each Layer-B child is a full agent tree.** 20 children ≈ 20× the model calls. `delegation.max_concurrent_children` only bounds *concurrency*, not *total*. - **Hermes aux/subagent model defaults to main-model-first.** Children inherit - the parent's (often expensive reasoning) model. `delegate_task` does NOT expose - a per-task `model` or `profile` field — its per-task keys are - `{goal, context, toolsets, role}`. To run the fan-out cheaper you either route - delegation globally via `delegation` config (model/provider applied to all - children), or — for genuinely model/profile-scoped work — use cron, the kanban - swarm, or a separate Hermes process. The cleanest lever for mechanical fan-out - is still Layer A: do the deterministic part in a script with no per-item LLM at - all. + the parent's (often expensive reasoning) model unless you pin a cheaper one. + For mechanical fan-out, pass a cheaper model per task if the config supports it, + or do the deterministic part in Layer A (no per-item LLM at all). Always: start on a SCOPED slice (one directory, 20 records, 10 endpoints), prove the recipe end-to-end, report the token cost, THEN offer to run it at full scale.