meta-forest-binary-plot

Generate meta-analysis forest plots for binary classification data. Input is a CSV file containing study names, event counts and sample sizes for experimental and control groups. Output includes forest plot PNG and data table CSV.

Quality

63%

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Passed

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tessl review fix ./scientific-skills/Data Analysis/meta-forest-binary-plot/SKILL.md

Quality

Content

56%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.

The skill has a genuinely strong core workflow with executable R/Python commands and a defined output contract, but it is weighed down by heavy generic boilerplate and a serious accuracy problem: multiple validation and example sections reference the wrong packaged script (extract_criteria.py instead of the forest plot scripts).

Suggestions

Correct every reference to `scripts/extract_criteria.py` (Example Usage, Validation Shortcut, Quick Validation, Key Features) to point at `scripts/forest_binary.R` / `scripts/forest_binary.py`, the actual forest-plot entry points.

Remove the generic templated boilerplate sections (When to Use, Key Features, Implementation Details, Deterministic Output Rules, Completion Checklist, Required Inputs, Output Contract) that restate what Claude already knows; keep only the data format, workflow, dependencies, and failure-handling guidance.

Consolidate the three redundant validation sections (Validation Shortcut, Quick Validation, Validation and Safety Rules) into a single concise verification step tied to the real script.

Dimension	Reasoning	Score
Conciseness	Roughly half the body is generic templated boilerplate ("When to Use", "Key Features", "Implementation Details", repeated validation/checklist sections) that restates what Claude already knows and pads the token budget without adding domain value.	2 / 5
Actionability	The core workflow gives copy-paste-ready commands (`Rscript scripts/forest_binary.R ...` and `python scripts/forest_binary.py ...`) plus a concrete output template, but the Example Usage and Quick Validation sections repeatedly point to `scripts/extract_criteria.py`, a script unrelated to forest plotting.	4 / 5
Workflow Clarity	A clear three-step sequence (validate input → execute R with Python fallback → output results) with a validation checkpoint and an error-recovery fallback, though the input validation is light and the surrounding checklist is generic boilerplate.	4 / 5
Progressive Disclosure	The body is a monolithic 215-line SKILL.md with section headers and clear inline script paths, but there are no separate reference files and substantial generic content is inlined rather than offloaded.	3 / 5
	Total	13 / 20 Passed

Description

70%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.

The description is specific and distinct with good keyword coverage, clearly stating what the skill does and its I/O contract. Its main weakness is the absence of any explicit "when to use" trigger guidance.

Suggestions

Add an explicit "Use when..." clause naming natural triggers (e.g., "Use when a user asks for a forest plot or meta-analysis of binary/classification outcome data from a CSV of study event counts").

Include the ".csv" file extension and a synonym or two (e.g., "odds ratio plot", "binary outcome meta-analysis") to broaden trigger coverage.

Optionally mention the combined effect size / heterogeneity statistics the plot conveys to round out the capability description.

Dimension	Reasoning	Score
Specificity	Names the domain and concrete actions ("Generate meta-analysis forest plots") plus specific I/O ("CSV file containing study names, event counts and sample sizes", "forest plot PNG and data table CSV"), with only minor gaps in coverage such as the underlying statistical computations.	4 / 5
Completeness	Has a clear "what" but no "Use when..." clause or equivalent trigger guidance, which per the rubric caps completeness at 3; the "when" is entirely absent.	3 / 5
Trigger Term Quality	Good natural keyword coverage ("forest plots", "meta-analysis", "binary classification data", "CSV", "experimental and control groups"), but missing common synonyms and file extensions like ".csv".	4 / 5
Distinctiveness Conflict Risk	"meta-analysis forest plots for binary classification data" is a clear, narrow niche with distinct triggers and minimal overlap risk with unrelated skills.	5 / 5
	Total	16 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Data Analysis/meta-forest-binary-plot/SKILL.md
Commit: f5ef65b

Reviewed: ~4 hours ago

Table of Contents

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