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v1.9.3

data-science-cv-repro-lab

@zack-dev-cm⭐ 0 stars

Data Science CV Repro Lab is a public ClawHub CV repro-lab skill. Use it when the user says "cv repro lab", "computer vision reproducibility", "CV experiment evidence", or wants execution-grade CV experiments and evidence capture across Colab, Kaggle, browser automation, and GPU VMs.

Choose how to use this skill

You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.

Adopt or update this skill from https://proskills.md/skills/other/zack-dev-cm-agentic-cv-repro-lab-skill-skill-data-science-cv-repro-lab. First identify the installation path supported by this platform, detect whether it is already installed, compare the installed version with the latest stable release, show me what will change, and only then install or update it. If no native installer is available, use the complete package or source fallback described on the page.

1. Native installer

This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.

Do not guess an installer command or replace an existing version without reviewing the diff.

2. Complete package recommended

Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.

No complete ProSkills package is published for this listing yet.

3. Prompt-only

Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.

Need only the instruction file?

Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.

No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.

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// RATINGS

⭐GitHub Stars

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Not yet listed on ClawHub or SkillsMP

// README

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

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 local-first planning and public artifact review 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:

// HOW IT'S BUILT

KEY FILES

skill/data-science-cv-repro-lab/SKILL.mdREADME.md

// REPO STATS

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