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.

Report security issues privately using the contact path in SECURITY.md instead of filing a public issue with sensitive details.

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