model-calibration-curve

Use when assessing how well a survival model's predicted probabilities agree with observed outcomes by fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file. NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis.

Quality

90%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

88%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong, actionable skill body with clear workflow validation and well-organized references. Main gaps are minor padding (checklist, bibliography) and a few broken file references in the testing/prerequisites sections.

Suggestions

Remove or relocate the 'Implementation Checklist' and bibliographic 'References' sections — they describe meta-status and citations rather than executable guidance.

Fix or remove references to non-existent bundle files (scripts/install_dependencies.R, tests/data/sample_clinical_survival_data.csv, tests/run_smoke_test.R, tests/run_smoke_test.sh).

Verify the optparse/--help note reflects actual behavior or move it into troubleshooting.md to keep the body lean.

Dimension	Reasoning	Score
Conciseness	Mostly efficient actionable reference (args table, CLI, error table), but the 'Implementation Checklist' and bibliographic 'References' sections add tokens Claude does not need and could be trimmed.	4 / 5
Actionability	Fully executable, copy-paste-ready CLI commands, a complete arguments table, concrete CSV input examples, and an error-code table cover the common cases.	5 / 5
Workflow Clarity	A clearly sequenced 4-step workflow with explicit validation checkpoints (column/sample/event counts) in Steps 1–2 and a feedback loop directing to troubleshooting.md on persistent errors.	5 / 5
Progressive Disclosure	Good one-level-deep structure with a 'When to Read External Files' table pointing to real references, but several referenced paths (scripts/install_dependencies.R, tests/data/..., tests/run_smoke_test.*) do not exist in the bundle.	4 / 5
	Total	18 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A highly specific, well-triggered description that cleanly states both capability and use conditions with strong boundary exclusions. Only minor room for additional natural-language synonyms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file' — with comprehensive coverage rather than just a few.	5 / 5
Completeness	Explicitly answers both what (fit Cox + bootstrap calibration curves at horizons from a CSV) and when ('Use when assessing how well a survival model's predicted probabilities agree with observed outcomes'), with concrete trigger phrasing and an explicit NOT-for boundary.	5 / 5
Trigger Term Quality	Strong domain keywords ('calibration curves', 'Cox model', 'prediction horizons', 'clinical CSV file', 'survival model') but missing some natural synonyms and file-extension variations users might say.	4 / 5
Distinctiveness Conflict Risk	A clear niche (survival model calibration via bootstrap Cox) with explicit exclusions for nomogram construction, Cox screening, ROC, and decision-curve analysis, giving minimal conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 1 missing	Warning

	Total	14 / 16 Passed

Repository: aipoch/medical-research-skills
Path: awesome-med-research-skills/Data Analysis/model-calibration-curve/SKILL.md
Commit: f5ef65b

Reviewed: ~11 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.