CF analyze-stats
Statistical analysis for medical research papers. Generates reproducible Python/R code with publication-ready tables and figures. Supports diagnostic accuracy, inter-rater agreement, meta-analysis, survival analysis, survey data, group comparisons, regression, propensity score, and repeated measures.
Statistical analysis for medical research papers.
As a process F 46/100 · Will not run — References files that are not bundled: references/exemplar_plots/decision_curve.md, references/exemplar_plots/mrmc_roc.md, scripts/check_reverse_coding.py
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 55. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 14836 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: references/exemplar_plots/decision_curve.md - warning
missing-refreference to a missing file: references/exemplar_plots/mrmc_roc.md - warning
missing-refreference to a missing file: scripts/check_reverse_coding.py - warning
missing-refreference to a missing file: scripts/check_structural_zero.py - note
frontmatter-keyunknown frontmatter key "triggers" - note
frontmatter-keyunknown frontmatter key "tools" - note
edit-residuethe text marks something as outdated (lines 440, 558): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 46/100
- 0Tools and files. 4 referenced file(s) missing: references/exemplar_plots/decision_curve.md, references/exemplar_plots/mrmc_roc.md, scripts/check_reverse_coding.py
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 40Execution cost. Instruction body is 14836 tokens: crowds the task out of the window
- 60Steps. 347 steps, 5 vague phrases
- 60Result and completion. Output format stated, no completion criterion
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -5TODO / placeholder text left in the skill
- -31 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 301: enough signal without eating the budget
- +4Structure: 53 headings
- +3Step-by-step instructions: 347 items
- +3Output format is stated explicitly
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 47.