meta-analysis-methods-generator

Generates the Methods section for a meta-analysis paper, including search strategy, screening, quality assessment, data extraction, and statistical analysis.

Quality

66%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Academic Writing/meta-analysis-methods-generator/SKILL.md

Quality

Content

65%

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

The skill delivers concrete, actionable prompt templates within a clearly sequenced workflow, but it is padded with generic boilerplate and contains contradictory validation guidance plus a stub validation script. Tightening the boilerplate and resolving the validation inconsistency would most improve it.

Suggestions

Remove or condense the generic process boilerplate (When to Use, Key Features, Required Inputs, Recommended Workflow, Output Contract, Validation and Safety Rules, Failure Handling, Completion Checklist) that restates widely applicable process discipline Claude already follows.

Resolve the validation contradiction: either make validate_skill.py perform a real check and point both 'Validation Shortcut' and 'Quick Validation' at it, or drop the script reference entirely and keep a single consistent verification path.

Consider moving the six detailed prompt templates into a references/ file (e.g. prompts.md) with one-level-deep links from SKILL.md to improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The body surrounds genuinely useful prompt templates with substantial generic process boilerplate ('Use this skill when the request matches its documented task boundary', 'Prefer this skill for repeatable, checklist-driven execution'), which could be tightened considerably without loss.	2 / 3
Actionability	Six concrete prompt templates (Search Strategy, Quality Assessment with per-scale rules, Statistical Analysis with I-squared thresholds and fixed/random-effects guidance) plus a per-step IO Contract provide specific, copy-paste-ready guidance for a prompt-generation skill.	3 / 3
Workflow Clarity	The Workflow section sequences six well-defined steps with explicit per-step inputs and an upfront input-validation step, but the guidance contradicts itself ('Validation Shortcut' says run validate_skill.py --help while 'Quick Validation' says no local script validation is required) and validate_skill.py is a hollow stub, leaving validation checkpoints inconsistent.	2 / 3
Progressive Disclosure	Sections are well organized with clear headings and the one bundle file (scripts/validate_skill.py) is real and referenced, but all six detailed prompt templates are inline rather than split into one-level-deep reference files, so content that could be separate remains in SKILL.md.	2 / 3
	Total	9 / 12 Passed

Description

67%

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 occupies a clear, distinctive niche, but it lacks an explicit 'Use when...' trigger clause and offers only one phrasing per concept. Adding trigger guidance and common term variations would raise completeness and trigger-term quality.

Suggestions

Append a 'Use when...' clause naming concrete triggers, e.g. 'Use when writing the Methods section of a meta-analysis or systematic review, including search strategy, screening, quality assessment, data extraction, and statistical analysis.'

Add common user phrasings and variations such as 'systematic review', 'PRISMA', 'risk of bias', or 'meta-analysis protocol' to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	The description enumerates multiple concrete actions: 'Generates the Methods section', 'search strategy, screening, quality assessment, data extraction, and statistical analysis', matching the anchor for listing several specific concrete actions.	3 / 3
Completeness	It clearly states what the skill does but offers no 'Use when...' clause or equivalent explicit trigger guidance, so per the judging guideline completeness is capped at 2.	2 / 3
Trigger Term Quality	Domain terms like 'meta-analysis paper', 'Methods section', 'search strategy', 'quality assessment' are natural, but each concept has only one canonical phrasing with no common variations (e.g. 'systematic review', 'PRISMA'), fitting the 'some relevant keywords but missing common variations' anchor.	2 / 3
Distinctiveness Conflict Risk	The meta-analysis Methods-section niche with its specific methodology triggers is clearly distinct and unlikely to fire for unrelated skills, matching the clear-niche anchor.	3 / 3
	Total	10 / 12 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
Commit: 7cc5680

Reviewed: 3 days ago

Table of Contents

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