summarize-eval Skill
Summarize a CXR eval result directory (or compare two) as a Markdown table with metrics as columns and confidence bins as rows. Invoke when the user points at one or two result-dirs and asks for a summary, calibration analysis, or comparison. Published by microsoft in siim-workshop-2026.
What is summarize-eval Skill?
Summarize a CXR eval result directory (or compare two) as a Markdown table with metrics as columns and confidence bins as rows. Invoke when the user points at one or two result-dirs and asks for a summary, calibration analysis, or comparison. Published by microsoft in siim-workshop-2026. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Documentation
- Data analysis
- Documentation use cases
- Data analysis use cases
Technical details
- Install or run with Copy skill directory
When to use summarize-eval Skill
- Use it for documentation.
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/siim-workshop-2026
- Skill file: .github/skills/summarize-eval/SKILL.md
What it does
Summarize a CXR eval result directory (or compare two) as a Markdown table with metrics as columns and confidence bins as rows. Invoke when the user points at one or two result-dirs and asks for a summary, calibration analysis, or comparison.
Skill instructions
summarize-eval Single result-dir bash uv run python .github/skills/summarize-eval/evalsummary.py --result-dir <result-dir Required output, in this order: 1. The script's stdout, copied verbatim (the Markdown tables — do not edit, re-format, drop, or re-render them). 2. The analysis bullets below. Analysis. One bold headline sentence, then 2–5 free-form bullets. Classification leads when present. - Headline (bold, 1 sentence). What's true about this run? Lead with the classification verdict (or cxrmetric verdict if classification is absent). Cite the macro-F1 / sensitivity / specificity numbers (or the strongest cxrmetric movement) that support it. No hedging in the headline. - Bullets. Cover, in whatever order makes the story clearest: - Calibration — high vs medium (and low if n ≥ 10) on macro-F1 and sensitivity. Well-calibrated, flat, or inverted? Cite the gap. - Agreement — do cxrmetric numbers point the same direction as the classification verdict? Name the metric that agrees most,
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Frequently asked questions
What is summarize-eval?
summarize-eval is a open-source AI agent skill with Copy skill directory. Summarize a CXR eval result directory (or compare two) as a Markdown table with metrics as columns and confidence bins as rows.
Who is summarize-eval best for?
summarize-eval is best for reusing agent instructions, scripts, and references, documentation workflows, data analysis workflows.
How do I install summarize-eval?
Install or run summarize-eval using Copy skill directory. Check summarize-eval for the latest setup command.
Is summarize-eval actively maintained?
summarize-eval may need a closer maintenance check before production use.
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