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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

86%

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SKILL.md
Quality
Evals
Security

Quality

Content

81%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 well-engineered CLI skill body: executable commands, a complete argument reference, explicit validation steps with an error-recovery table, and a clean external-file routing table. The main deductions are internal dev metadata inlined in SKILL.md and several referenced bundle files (installer, tests) missing from the actual bundle.

Suggestions

Remove the "Implementation Checklist" and the "Last updated | Version" footer from SKILL.md — they are development metadata, not runtime guidance, and dilute the lean overview.

Make every referenced path real: either ship scripts/install_dependencies.R and the tests/ directory (sample data, run_smoke_test.R, run_smoke_test.sh) or drop those commands from Prerequisites, Testing, and Examples.

Resolve the optparse "root fix" TODO note — apply the fix in scripts/main.R or move the note to references/troubleshooting.md so the body contains only actionable instructions.

DimensionReasoningScore

Conciseness

Mostly dense and useful — argument tables, executable commands, sample CSV, error tables — with little explanation of concepts Claude already knows. Not 5 because the "Implementation Checklist" (10 checked dev boxes), the "Last updated: 2026-04-27 | Version: 2.1.0" stamp, and the unresolved optparse "root fix" TODO note are internal metadata that could be trimmed.

4 / 5

Actionability

Copy-paste-ready bash commands with a complete argument table (defaults, types), four worked examples, sample input data, and an error-code-to-solution table. Not 5 because several executable examples reference bundle paths that do not exist ("scripts/install_dependencies.R", "tests/data/sample_clinical_survival_data.csv", "tests/run_smoke_test.R", "tests/run_smoke_test.sh"), so those commands fail as written.

4 / 5

Workflow Clarity

The four-step workflow is clearly sequenced with explicit validation in steps 1-2 (column presence, complete-case filtering, rejection of non-positive times, invalid coding, <30 samples, <10 events), and the error-handling table provides a feedback loop ("IF error persists, READ: references/troubleshooting.md"). Not below 5 since checkpoints and recovery paths are explicit.

5 / 5

Progressive Disclosure

The "When to Read External Files" table is a well-signaled, one-level-deep map and all four of its targets exist in the bundle (references/algorithm.md, scripts/main.R, references/troubleshooting.md, references/cli-guide.md). Not 5 because the body also references nonexistent paths (install_dependencies.R, tests/*) and inlines dev-metadata (checklist, version stamp) that belongs elsewhere.

4 / 5

Total

17

/

20

Passed

Description

87%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 strong description: it states concrete actions in third person, gives an explicit "Use when..." trigger, and carves out a distinct niche with a NOT-for exclusion list. Minor gaps in capability coverage and synonym/extension terms keep specificity and trigger quality at 4 rather than 5.

DimensionReasoningScore

Specificity

Names concrete actions — "fitting a Cox model", "generating bootstrap calibration curves at one or more prediction horizons", "from a clinical CSV file" — plus an explicit exclusion list. Not 5 because it omits capabilities the skill actually has (exporting statistics and a PDF visualization); not 3 because it lists several specific actions rather than only 1-2.

4 / 5

Completeness

Explicitly answers both: the "what" ("fitting a Cox model and generating bootstrap calibration curves... from a clinical CSV file") and the "when" ("Use when assessing how well a survival model's predicted probabilities agree with observed outcomes"), with concrete trigger phrasing and a NOT-for list. Matches the anchor-5 pattern.

5 / 5

Trigger Term Quality

Good natural-term coverage: "calibration curve", "Cox model", "survival model", "predicted probabilities", "clinical CSV file", plus boundary terms ("nomogram", "ROC", "decision-curve analysis"). Not 5 because common variations like "calibration plot", "bootstrap", or "predicted vs. observed survival" are missing.

4 / 5

Distinctiveness Conflict Risk

Clear niche — bootstrap calibration of survival/Cox models from clinical CSV data — with an explicit "NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis" boundary that cleanly separates it from the closest sibling skills.

5 / 5

Total

18

/

20

Passed

Validation

87%

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

CriteriaDescriptionResult

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

Reviewed

Table of Contents

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