CV Repro Lab Skills

Run CV experiments reproducibly across Colab, Kaggle, browser workflows, and GPU VMs, then decide whether a result is strong enough to promote.

CV Repro Lab Skills packages two public OpenClaw/Codex skills:

• data-science-cv-repro-lab: run experiments, capture browser and notebook evidence, and bundle results for review
• sota-agent: define the benchmark, rank candidates, and decide whether a claimed gain is real

Use both together when you want one planning lane and one execution lane:

• sota-agent freezes the benchmark, candidate list, rerun policy, and claim rules before more compute gets spent
• data-science-cv-repro-lab executes runs across Colab, Kaggle, browser-heavy workflows, or VMs and captures the evidence needed to promote or reject the result

Which Skill Should I Use?

data-science-cv-repro-lab

Use it when you need:

• Colab, Kaggle, or VM execution discipline
• browser evidence and validation scorecards
• dataset manifests, run cards, and promotion bundles
• reproducible artifact capture for a real training or export lane

sota-agent

Use it when you need:

• a fixed benchmark before spending more compute
• literature triage and candidate ranking
• ablation discipline and rerun policy for small deltas
• an honest claim decision instead of benchmark theater

Quick Start

Install from ClawHub or copy the skill folders into $CODEX_HOME/skills/.

mkdir -p "$CODEX_HOME/skills"
rsync -a skill/data-science-cv-repro-lab/ "$CODEX_HOME/skills/data-science-cv-repro-lab/"
rsync -a skill/sota-agent/ "$CODEX_HOME/skills/sota-agent/"

Live Packages

• CV Repro Lab on ClawHub (v1.9.1)
• SOTA Agent on ClawHub (v1.4.1)
• Portfolio entry

What Changed In This Release

• added an explicit improvement harness for plateau recovery and score-improvement work
• added a review-dashboard manifest for synced QA runs, benchmark panels, runtime sweeps, and audit surfaces
• expanded run cards and candidate/program records with reruns, slices, agent threads, and auth policy
• added explicit dashboard, source-audit, and leakage-audit references to the SOTA claim surface
• added redacted public summary rendering for the richer machine-readable records
• made OAuth-backed ChatGPT/Codex paths the default public story instead of API-key-first tooling

Good Public Fit

These skills are strongest when the user already has a real CV or DS workflow and wants a drop-in research harness around it. Good fits include:

• derm or segmentation plateau recovery
• browser-heavy notebook workflows
• benchmark campaigns that need stronger promotion gates
• public or reusable experiment-management patterns across repos

Public Safety

ClawHub is public. Keep the published skill bundles free of:

• absolute local paths
• browser profile names
• private notebook URLs
• secrets, tokens, and customer identifiers
• internal hostnames or VM labels

Private specializations should stay in local override skills, not in the public package.

Local Validation

python3 -m py_compile \
  skill/data-science-cv-repro-lab/scripts/*.py \
  skill/sota-agent/scripts/*.py

python3 skill/data-science-cv-repro-lab/scripts/init_cv_improvement_harness.py \
  --out /tmp/cv-harness.json \
  --task-id demo \
  --candidate-family baseline-recovery

python3 skill/data-science-cv-repro-lab/scripts/init_cv_review_dashboard_manifest.py \
  --out /tmp/cv-dashboard.json \
  --dashboard-id demo-dashboard \
  --title "Demo review dashboard"

python3 skill/sota-agent/scripts/init_sota_program.py \
  --out /tmp/sota-program.json \
  --campaign-id demo \
  --task demo \
  --metric score

References

The public bundle is informed by:

• karpathy/autoresearch
• algorithmicsuperintelligence/openevolve
• algorithmicsuperintelligence/optillm
• openai/symphony
• OpenAI harness engineering
• OpenClaw skills docs