burtenshaw/multiautoresearch/autolab-hermes-delegation

Claude Code and agent skill by burtenshaw/multiautoresearch

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Use Hermes delegate_task cleanly in this repo for planner, reviewer, researcher, reporter, experiment-worker, and memory-keeper roles.

Install

npx vskill@latest install burtenshaw/multiautoresearch/autolab-hermes-delegation

SKILL.md

Use this when Hermes is the parent control plane for this repo.

Hermes children get a fresh context, cannot ask the user for clarification, cannot delegate again, and only return their final summary to the parent. Pass the full role contract in every delegate_task(...) call.

Parent Rules

  • Keep AGENTS.md as the only checked-in rulebook. Do not add .hermes.md.
  • Launch the parent with toolsets: terminal,file,web,skills,delegation,clarify
  • Leave Hermes memory out of the default toolsets so repo markdown stays the durable record.
  • Keep Hermes child concurrency at min(gpu_slots, 3) per parent session.
  • If you need more than 3 parallel workers, use multiple top-level Hermes sessions or stay on OpenCode.

Role Defaults

  • planner
  • toolsets: ["file"]
  • focus: fresh, non-duplicate single-change ideas only
  • reviewer
  • toolsets: ["file"]
  • focus: hard-rule checks, stale-master risk, duplicates, multi-change patches
  • researcher
  • toolsets: ["web", "file", "skills", "terminal"]
  • focus: paper-derived single-change hypotheses only
  • reporter
  • toolsets: ["terminal", "file", "skills"]
  • focus: HF Jobs and Trackio status, duplicate active jobs, anomalies
  • memory-keeper
  • toolsets: ["file"]
  • focus: durable markdown updates in the main checkout only
  • experiment-worker
  • toolsets: ["terminal", "file", "skills"]
  • focus: one isolated worktree, one hypothesis, one managed benchmark run

Planner Template

python
delegate_task(
goal="Propose up to 3 fresh Autolab experiments against the current local promoted master.",
context="""Read AGENTS.md, README.md, research/notes.md, research/do-not-repeat.md,
research/campaigns/, research/experiments/, research/results.tsv, research/live/master.json,
and research/live/dag.json.
Return a ranked queue of 1-3 fresh experiments. Each must include:
- short title
- one-sentence hypothesis
- parent master hash
- exact single variable being changed
- expected upside
- reason it is not a duplicate
Do not run commands that mutate the repo. Do not propose multi-change ideas.""",
toolsets=["file"],
max_iterations=20,
)

Reviewer Template

python
delegate_task(
goal="Review this Autolab plan or result for rule violations and comparability risk.",
context="""Read AGENTS.md and the provided experiment details.
Prioritize:
- hard-rule violations
- stale-master risk
- duplicate experiments
- multi-change patches
- missing benchmark evidence
- incorrect submit or no-submit decisions
Return concise findings with exact file or evidence references.""",
toolsets=["file"],
max_iterations=20,
)

Researcher Template

python
delegate_task(
goal="Find up to 3 paper-derived single-change Autolab ideas that map cleanly to train.py.",
context="""Read AGENTS.md, research/notes.md, research/do-not-repeat.md,
research/paper-ideas.md, research/results.tsv, research/live/master.json, and research/live/dag.json.
Use the repo's Hugging Face skills when useful. Reject ideas already present in code or already ruled out.
Return the smallest credible change to test for each idea and the main risk if it fails.""",
toolsets=["web", "file", "skills", "terminal"],
max_iterations=30,
)

Reporter Template

python
delegate_task(
goal="Summarize current Autolab fleet status and call out duplicate or stale active jobs.",
context="""Use the repo reporter workflow:
- . ~/.autolab/credentials
- uv run scripts/trackio_reporter.py summary --max-jobs 25
- uv run scripts/trackio_reporter.py sync --project ${AUTOLAB_TRACKIO_PROJECT:-autolab} when needed
Treat Trackio plus HF Jobs metadata as the source of truth.
Do not edit repo markdown or code.""",
toolsets=["terminal", "file", "skills"],
max_iterations=25,
)

Experiment Worker Flow

  1. Create the reserved state:
  • uv run scripts/hermes_worker.py create <experiment-id> --campaign ... --hypothesis ...
  1. Print the delegate payload:
  • uv run scripts/hermes_worker.py delegate <experiment-id>
  1. Paste the emitted delegate_task(...) block into the parent session.
  2. The child must:
  • cd into the reserved worktree
  • export AUTOLAB_CAMPAIGN, AUTOLAB_EXPERIMENT_ID, AUTOLAB_WORKER_ID, AUTOLAB_HYPOTHESIS, AUTOLAB_LOG_PATH, and AUTOLAB_EXPERIMENT_NOTE
  • refresh local master
  • edit train.py only
  • run one managed experiment
  • parse the metric
  • run submit_patch.py

Memory-Keeper Template

python
delegate_task(
goal="Update the durable Autolab markdown after a worker completed.",
context="""Read AGENTS.md plus research/notes.md, research/do-not-repeat.md,
research/campaigns/, research/experiments/, and the worker's final summary.
Update only the durable markdown in the main checkout.
Preserve:
- hypothesis tested
- parent master hash
- local val_bpb or failure state
- submit decision
- one short interpretation""",
toolsets=["file"],
max_iterations=25,
)

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
283
Repository forks
39
7-day trend score
28

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 20 May 2026, 5 months ago.

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