/clawpathy-autoresearch

name: clawpathy-autoresearch
description: 'Eval-driven skill tuning. Given a task and an LLM-judge rubric, iteratively rewrites a SKILL.md until a downstream executor agent performs well against the judge. Low-code: all evaluation
  is LLM-as-judge, not deterministic Python.'
license: MIT
metadata:
  openclaw:
    requires:
      bins:
      - python3
      - claude
    always: false
    emoji: 🔁
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    trigger_keywords:
    - auto research
    - autoresearch
    - tune a skill
    - skill tuning
    - improve a skill
    - eval-driven
    - clawpathy
    - replicate paper
    - reproduce paper
  author: Jay Moore
  inputs:
  - name: paper_query_or_task
    type: string
    description: Paper title/URL/PMID/DOI, or a freeform task description
    required: true
  outputs:
  - name: workspace/
    type: directory
    description: Tuned skill/SKILL.md plus history.jsonl, snapshots, executor_runs
  tags:
  - meta
  - autoresearch
  - skill-tuning
  - llm-judge
  - eval-driven
  version: 1.0.0

clawpathy-autoresearch

Eval-driven skill development. The system iteratively rewrites a `SKILL.md` so a downstream executor agent performs better at a task class, as judged by an LLM against a paper/task-specific rubric.

Core idea

  propose (sonnet)  →  execute (sonnet, shell)  →  judge (opus, rubric)
       ↑                                                       │
       └──────── feedback: verdict + recommended edits ────────┘

• **Proposer** rewrites SKILL.md based on the last judge verdict.
• **Executor** runs the new SKILL.md end-to-end inside a workspace.
• **Judge** scores methodology (primary) and outputs (secondary) against

a per-task rubric. Lower is better; 0 = perfect.

• Keep the new SKILL.md only if it strictly beats the best score; else

revert. Stop on target_score or on `early_stop_n` consecutive regressions.

You are the orchestrator

You (the agent reading this) don't run the loop yourself. You dispatch subagents to build the workspace, then hand off to the Python loop.

Phase 1 — Scout

Dispatch a subagent with `prompts/scout.md` to research the paper/task. Report key findings to the user in a few lines.

Phase 2 — Scope (you + user)

Have a conversation. Ask ONE question at a time, multiple-choice where helpful. Agree on:

• what to reproduce / what success looks like
• which data sources are in-bounds
• what methodology expectations belong in the rubric
• iteration budget and target_score (if any)

Present a summary and get approval.

Phase 3 — Build

Dispatch a builder subagent with `prompts/builder.md` and the agreed scope. It writes:

• `task.json`
• `rubric.md` — **the authoritative scoring rubric for the LLM judge**
• `reference/` (optional; judge-only)
• `skill/SKILL.md` — seed

Validate:

from skills.clawpathy_autoresearch import validate_workspace
print(validate_workspace(Path("WORKSPACE")))  # [] means valid

Phase 4 — Loop

python -m skills.clawpathy_autoresearch WORKSPACE_DIR
# or with custom models:
python -m skills.clawpathy_autoresearch WORKSPACE_DIR \
  --proposer-model sonnet --executor-model sonnet --judge-model opus

The loop streams progress to `WORKSPACE/history.jsonl`, snapshots every iteration's skill to `WORKSPACE/snapshots/iter-NNN.md`, and writes the executor's full transcript to `WORKSPACE/executor_runs/iter-NNN.log`.

Workspace layout

workspace/
  task.json                  # task metadata + loop knobs
  rubric.md                  # LLM-judge rubric (the heart of the system)
  reference/                 # optional ground truth, judge-only
  skill/SKILL.md             # iterated by the loop
  output/                    # executor outputs (cleared each iter)
  executor_runs/iter-NNN.log # transcripts (judge reads these)
  snapshots/iter-NNN.md      # per-iter SKILL.md snapshots
  history.jsonl              # one row per iter: score, kept, verdict

Key principles

• **LLM judge only.** No deterministic Python scorers. All evaluation goes

through `judge.md` + opus. This keeps the system low-code and lets the rubric carry paper-specific nuance without adding code.

• **Methodology is primary.** The rubric weights "did the agent use sound

methods?" above "did the numbers match?". Ground-truth match is a signal, not the objective — the goal is better SKILL.md files.

• **Never leak ground truth.** `reference/` is judge-only. The executor

prompt says not to read it, and the judge penalises leakage.

• **No hardcoded answers in SKILL.md.** The proposer prompt and the judge

both enforce this. The executor must derive results by running methods.

• **Snapshots + strict-better revert.** Score on the first iter becomes the

floor. Later iters that tie or regress revert to the best.

Safety

• All processing is local except scout web fetches for public resources.
• ClawBio disclaimer: research/education tool, not a medical device.

Gotchas

• **Do not skip scoping.** The rubric is paper-specific; a generic rubric

tunes nothing. Get the user to agree on methodology expectations.

• **Do not write a Python scorer.** Earlier versions of this project did.

They rewarded API-fetching, not methodology. The judge is the scorer.

• **Do not hand-pick the "best" snapshot yourself.** Trust the loop. If

the judge is calibrated wrong, fix the rubric, not the history.