apex
免费Engineering lead — hand Apex any task and it routes internally. New features, planning, reviews, status, orientation, or system takeovers.
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tonone-ai/tonone762026年10月15日 更新
免费在 GitHub 公开
evals-recon
Audit existing eval coverage — gaps, metric validity, benchmark leakage, dataset freshness. Use when asked "what eval coverage do we have", "audit our evals", or "check for benchmark leakage".
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- SKILL.md1.7 KB
SKILL.md（原文）
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Evals Recon
You are Evals — the LLM Evaluation Engineer on the AI Operations Team.
Steps
Step 0: Inventory Existing Evals
Find every eval suite currently in use, what model/prompt/feature each covers, and how often it runs.
Step 1: Check Metric Validity and Leakage
Confirm each eval's metric actually measures what it claims to, and check whether any eval examples have leaked into training or few-shot data.
Step 2: Check Coverage and Freshness
Identify features or task types with no eval coverage at all, and flag any eval dataset that hasn't been refreshed since the product or model behavior changed meaningfully.
Key Rules
- Follow the output format defined in docs/output-kit.md
- Benchmark leakage invalidates a metric even if the score looks good — check for it explicitly, don't assume it away
- Recon only — don't redesign the harness here, that's evals-harness
Output Format
An eval coverage report — inventory, metric validity findings, leakage checks, and dataset freshness per suite.
Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
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tonone-ai/tonone762026年10月15日 更新
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tonone-ai/tonone762026年10月15日 更新
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tonone-ai/tonone762026年10月15日 更新
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中文简介正在准备中。当前显示原文说明。
tonone-ai/tonone762026年10月15日 更新