baseline-runner

name	baseline-runner
description	Use this when the project needs real baseline results before or alongside the main model. Runs classical or literature-aligned baselines under the same protocol and writes a reproducible baseline summary.
metadata	{"openclaw":{"emoji":"📏","requires":{"bins":["python3","uv"]}}}

Baseline Runner

Don't ask permission. Just do it.

Use this skill when the project needs trustworthy baseline numbers instead of only evaluating the proposed model in isolation.

Outputs go to the workspace root.

Use This When

• plan_res.md already names baselines
• project/ already exists or a baseline implementation path is known
• the experiment stage needs matched comparison numbers

Do Not Use This When

• the project has not finished survey or planning
• no baseline method has been identified yet

Required Inputs

• plan_res.md
• survey_res.md
• project/ when the current project already has runnable code

If plan_res.md is missing, stop and say: Run /research-plan first to complete the implementation plan.

Required Outputs

• baseline_res.md
• experiments/baselines/ when runnable artifacts are created

Workflow

Step 1: Read the Evaluation Contract

Read:

• plan_res.md
• survey_res.md
• current experiment_res.md if it exists

Extract:

• baseline names
• evaluation metric
• protocol or guardrail
• dataset or workload assumptions

Step 2: Define the Baseline Matrix

Create a small comparison matrix with:

• baseline name
• source or basis
• expected setup
• metric
• status: ready, needs adaptation, or missing

Use references/baseline-matrix-template.md.

Step 3: Run or Approximate Baselines Conservatively

For each baseline:

• if code is runnable under the current workspace, run it
• if only a lightweight adaptation is needed, implement the minimal adapter
• if a baseline cannot be run honestly, mark it as unavailable instead of inventing numbers

All numeric results must come from actual execution logs or explicit imported evidence.

Step 4: Write baseline_res.md

Use references/baseline-report-template.md.

The report must include:

• which baselines were attempted
• which ones ran successfully
• the exact metric values
• the evaluation protocol
• missing or partial baselines
• the most comparable baseline for the current project

Rules

1. Never fabricate baseline numbers.
2. Keep the protocol aligned with the main experiment whenever possible.
3. If a baseline is only partly comparable, say so explicitly.
4. Prefer 2-3 strong baselines over a long weak list.