linkedin-optimizer

Use when optimizing LinkedIn profiles for doctors, physicians, nurses, healthcare professionals, or medical researchers. Crafts compelling headlines, writes professional summaries, integrates healthcare keywords, and builds personal branding for medical careers.

Quality

75%

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/linkedin-optimizer/SKILL.md

Quality

Content

50%

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

The body contains genuinely useful concrete guidance for LinkedIn profile optimization, but it is padded with redundant template boilerplate and its code/CLI examples do not match the actual bundled script. Referenced reference files are missing, so progressive disclosure and actionability both fall short of fully executable.

Suggestions

Make the documented code and CLI match the actual bundle: either implement generate_headline/write_about_section/suggest_keywords/optimize_experiences and the CLI in scripts/main.py, or replace the examples with the real optimize(role, specialty, achievements) API.

Remove the duplicated description restatements and generic boilerplate ('Implementation Details', 'Output Requirements', 'Error Handling', 'Response Template') that Claude already knows, keeping only the concrete formulas, keyword table, and worked examples.

Create the referenced files (references/linkedin-examples.md, keywords-by-specialty.json, headline-templates.md) and move the large keyword table and about-section example into them, or delete the broken references and keep the content inline with accurate paths.

Dimension	Reasoning	Score
Conciseness	Useful concrete examples (headline formulas, keyword table, about-section structure) are interspersed with large amounts of generic templated boilerplate ('Implementation Details', 'Output Requirements', 'Error Handling', 'Response Template') and verbatim re-statements of the description, so it is mostly efficient but padded rather than lean.	2 / 3
Actionability	Concrete code and CLI examples are provided, but they reference a LinkedInOptimizer API (generate_headline, suggest_keywords, optimize_experiences) and a scripts/linkedin_optimizer.py CLI that do not exist in the actual bundle, whose main.py only exposes optimize(); guidance is therefore incomplete and not executable as written.	2 / 3
Workflow Clarity	A sequenced 5-step Workflow plus a Quality Checklist and Quick Check exist, but the steps are abstract ('Confirm the user objective', 'Validate the request') without concrete validation checkpoints tied to the real script, leaving checkpoints implicit rather than an explicit validate-fix-retry loop.	2 / 3
Progressive Disclosure	The body signals references (references/linkedin-examples.md, keywords-by-specialty.json, headline-templates.md) but none of these files exist in the bundle (only guidelines.md is present), and content that should be split out (keyword table, about example) is inlined, matching the score-2 anchor of structure present but references not resolving and inline content that should be separate.	2 / 3
	Total	8 / 12 Passed

Description

100%

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 concise, third-person, and clearly states both the specific capabilities and the explicit 'Use when' trigger with natural, healthcare-specific keywords. It is well-targeted to its niche with low conflict risk.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Crafts compelling headlines, writes professional summaries, integrates healthcare keywords, and builds personal branding for medical careers' — matching the score-3 anchor for specific actions rather than the partial listing expected at score 2.	3 / 3
Completeness	Explicitly answers both what (four named actions) and when (a 'Use when...' clause with concrete trigger audiences), satisfying the score-3 anchor; not score 2 where the 'when' is missing or only implied.	3 / 3
Trigger Term Quality	'Use when optimizing LinkedIn profiles for doctors, physicians, nurses, healthcare professionals, or medical researchers' covers natural terms a user would say, with multiple role variants; above score 2 which expects missing common variations.	3 / 3
Distinctiveness Conflict Risk	The healthcare/medical-professional LinkedIn niche with role-specific triggers is clearly distinguishable and unlikely to fire for unrelated skills, matching the score-3 clear-niche anchor.	3 / 3
	Total	12 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 6 missing	Warning

	Total	14 / 16 Passed

Repository: aipoch/medical-research-skills
Commit: 7cc5680

Reviewed: 3 days ago

Table of Contents

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