meta-title-generator

Generates Meta-Analysis research titles based on user keywords, utilizing PubMed search results if available, or creative generation otherwise. Use when the user wants to brainstorm or generate titles for a meta-analysis, specifically starting from keywords or a topic.

Quality

67%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/meta-title-generator/SKILL.md

Quality

Content

52%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 body contains a solid, actionable generation workflow with a well-signaled reference, but it is weighed down by generic templated boilerplate that inflates token cost and a missing real-world script invocation example.

Suggestions

Remove or collapse the generic 'When to Use', 'Key Features', and 'Implementation Details' boilerplate sections that restate the description and skill-packaging language Claude already knows.

Add a concrete example of the actual run, e.g. `python scripts/search_pubmed.py --strategy "lung cancer AND immunotherapy"` with a sample JSON output, so the primary execution path is copy-paste ready.

Drop the date-stamped path '20260316/scientific-skills/Data Analytics/meta-title-generator' from Example Usage, or move it to a deprecated/old-patterns note, since time-sensitive paths hurt portability.

Dimension	Reasoning	Score
Conciseness	Several padded, templated sections restate generic skill-packaging language (the six 'When to Use' bullets, 'Key Features', 'Implementation Details') and repeat the description, adding little Claude does not already know.	2 / 5
Actionability	The Usage section gives concrete steps and a precise JSON output format with Quality Rules, but the actual script invocation for the real task is missing — only `--help` and `py_compile` are shown, leaving a key execution detail unspecified.	3 / 5
Workflow Clarity	A clear numbered sequence (1–4) includes an explicit count-based branching checkpoint ('If the result count is > 0' vs '= 0'); minor validation gaps exist (no script-failure recovery or final title-quality verification), but this is not a destructive/batch operation requiring a feedback loop.	4 / 5
Progressive Disclosure	The body links to a real one-level-deep reference (references/title_generation_prompts.md) and script in a dedicated '## Reference Material' section; minor organization gaps stem from redundant boilerplate sections restating the same scope.	4 / 5
	Total	13 / 20 Passed

Description

82%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 well-formed: it states a concrete capability and provides an explicit, natural-language trigger in third person. Its only weakness is specificity, since the headline deliverable details (bilingual, 5-title JSON) are not surfaced here.

Dimension	Reasoning	Score
Specificity	Names the domain and two concrete generation modes ('utilizing PubMed search results if available, or creative generation otherwise'), but is not comprehensive — the bilingual output and 5-title deliverable format appear only in the body, not the description.	3 / 5
Completeness	Explicitly answers both 'what' ('Generates Meta-Analysis research titles...') and 'when' ('Use when the user wants to brainstorm or generate titles...') with concrete trigger phrases, in third-person voice.	5 / 5
Trigger Term Quality	Includes natural phrases a user would say ('brainstorm or generate titles for a meta-analysis', 'starting from keywords or a topic'); good coverage, though a few synonyms like 'systematic review' are absent.	4 / 5
Distinctiveness Conflict Risk	Targets a clear niche (meta-analysis title generation from keywords via PubMed) with distinct triggers, giving minimal overlap risk with other skills.	5 / 5
	Total	17 / 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-title-generator/SKILL.md
Commit: f5ef65b

Reviewed: ~5 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.