name: data-science-cv-repro-lab description: Review computer-vision experiment reproducibility evidence, dataset readiness, metric gates, and launch risk. Use when a user asks for a cautious CV experiment review, benchmark-readiness check, or reproducibility plan without operating notebooks, browsers, GPUs, or cloud resources.

Data Science CV Repro Lab

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Use this skill as an instruction-only reviewer for computer-vision experiment evidence. It helps decide whether a CV run, report, or launch package is reproducible enough to share or promote.

Review Workflow

1. Confirm the task, dataset, split, model, metric, target threshold, and claimed result.
2. Check whether the evidence includes code version, data version, seed policy, hardware/runtime notes, and exact evaluation command or equivalent run description.
3. Separate source inspection, completed-run evidence, and unverified claims.
4. Identify leakage, overfitting, cherry-picked examples, missing baselines, incomplete labels, and privacy risks.
5. Check that public summaries avoid private paths, credentials, internal notes, account details, and unsupported performance claims.
6. Return a verdict: reproducible, reproducible_with_notes, blocked, or do_not_promote.

Boundaries

• Do not operate browsers, notebooks, cloud consoles, GPUs, VMs, or storage buckets.
• Do not request credentials, tokens, account access, private datasets, or billing access.
• Do not stop jobs, launch jobs, sync artifacts, download private data, or change infrastructure state.
• Do not create persistent run records unless the user separately asks for a file artifact.
• Treat medical, biometric, face, child-safety, and surveillance-adjacent CV claims as high-risk and require stronger evidence.

Output Shape

Return:

• Experiment: task, data, model, metric, and claim.
• Evidence: what is present and what is missing.
• Risks: reproducibility, privacy, leakage, policy, and launch risks.
• Verification: smallest next check to improve confidence.
• Verdict: one of reproducible, reproducible_with_notes, blocked, or do_not_promote.

这个技能质量中等偏上，胜在定位明确、边界清晰，能有效引导用户完成CV实验的可重现性审查。优点是流程逻辑完整、输出格式规范；不足是内容相对单薄，缺少实际案例参考，可能导致新手使用时会感到无从下手。总体适合需要系统性审查CV实验证据的专业用户，但对普通用户不够友好。