meta-forest-model-plot

Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts.

Quality

63%

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Securityby

Passed

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tessl review fix ./scientific-skills/Data Analysis/meta-forest-model-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 content is actionable with concrete R/Python commands and a clear validation-gated workflow, and its script references resolve to real bundle files. Its chief weakness is verbosity: large generic boilerplate sections dilute the genuinely useful, skill-specific guidance.

Suggestions

Trim or remove the generic boilerplate sections ('When to Use', 'Implementation Details', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling', 'Completion Checklist') that do not add forest-plot-specific value, and consolidate the duplicate 'Validation Shortcut' and 'Quick Validation' sections.

Fix the 'Quick Validation' expected-output block so it matches the actual script output (Survival_forest_{outcome}.png and .csv) rather than the aspirational 'meta_forest_model_plot_result.md' summary.

Make the validate->fix->retry feedback loop explicit in Step 1 (e.g., 'on invalid data, report the exact failing field/column, then re-read the corrected CSV before proceeding') to strengthen workflow clarity.

Dimension	Reasoning	Score
Conciseness	The body is padded with generic boilerplate sections ('When to Use', 'Key Features', 'Implementation Details', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling', 'Completion Checklist') that are not skill-specific, plus duplicated validation/quick-validation sections and a self-referential 'See ## Workflow above'.	2 / 5
Actionability	Provides concrete, executable commands ('Rscript scripts/forest_survival.R "<csv_path>" ...', 'python scripts/forest_survival.py ...'), a parameter table, a column-format table, and expected output; minor gap is that the 'Quick Validation' expected output references 'meta_forest_model_plot_result.md' which does not match the actual Survival_forest_{outcome}.png/.csv outputs.	4 / 5
Workflow Clarity	A clear three-step sequence (validate input, execute script, output results) with an explicit validation checkpoint ('If there are data issues, prompt the user to correct and resubmit'), though the validate->fix->retry feedback loop is only lightly stated.	4 / 5
Progressive Disclosure	Structure is sectioned and the referenced scripts (forest_survival.py, forest_survival.R, validate_skill.py) are real and clearly signaled at one level deep, but the SKILL.md is monolithic with no separate reference files and substantial generic content that could be trimmed or split.	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 distinctive, clearly stating inputs (CSV with study names, HR, CIs) and outputs (PNG plot, CSV table) for a well-scoped niche. Its main weakness is the absence of an explicit 'Use when...' trigger clause, which caps completeness.

Dimension	Reasoning	Score
Specificity	Names the domain ('meta-analysis of survival data') and several concrete actions ('Generate forest plots', 'output forest plot PNG and data table CSV', 'Supports both R and Python scripts'), with only minor coverage gaps versus the comprehensive multi-action anchor.	4 / 5
Completeness	Has a clear 'what' but no explicit 'Use when...' trigger clause; per the judging guideline a missing trigger clause caps completeness at 3, and the 'when' is only weakly implied.	3 / 5
Trigger Term Quality	Includes natural user-facing terms ('forest plots', 'meta-analysis', 'survival data', 'HR', '95% confidence intervals') that a researcher would say, though a few synonyms or file extensions (e.g. 'hazard ratio', '.csv') are absent.	4 / 5
Distinctiveness Conflict Risk	Targets a clear niche (forest plots for survival-data meta-analysis) with distinct, specific triggers and minimal overlap risk with other 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-model-plot/SKILL.md
Commit: f5ef65b

Reviewed: ~4 hours ago

Table of Contents

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