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.
73
67%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Academic Writing/linkedin-optimizer/SKILL.mdQuality
Discovery
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.
This is a strong skill description that clearly defines its niche at the intersection of LinkedIn profile optimization and healthcare professionals. It provides explicit trigger guidance with a 'Use when' clause, lists concrete actions, and includes a rich set of natural trigger terms covering multiple healthcare role variations. The description is concise, uses third-person voice, and would be easily distinguishable from other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Crafts compelling headlines, writes professional summaries, integrates healthcare keywords, and builds personal branding for medical careers.' These are clear, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what ('Crafts compelling headlines, writes professional summaries, integrates healthcare keywords, and builds personal branding') and when ('Use when optimizing LinkedIn profiles for doctors, physicians, nurses, healthcare professionals, or medical researchers') with an explicit 'Use when' clause. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'LinkedIn profiles', 'doctors', 'physicians', 'nurses', 'healthcare professionals', 'medical researchers', 'headlines', 'summaries', 'healthcare keywords', 'personal branding', 'medical careers'. These are terms users would naturally use when seeking this kind of help. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive by combining two specific niches: LinkedIn profile optimization AND healthcare professionals. This intersection creates a clear niche that is unlikely to conflict with a general LinkedIn skill or a general healthcare skill. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill contains genuinely useful healthcare LinkedIn optimization content (headline formulas, about section structure, keyword tables, experience formulas) buried under layers of generic boilerplate. Roughly half the content is template filler that adds no domain-specific value—repeated descriptions, generic workflow steps, input validation boilerplate, and response templates that Claude doesn't need. Stripping the boilerplate and focusing on the healthcare-specific guidance would dramatically improve this skill.
Suggestions
Remove all generic boilerplate sections (Input Validation, Error Handling, Response Template, Output Requirements, Implementation Details, 'When to Use' repeated description) that add no healthcare-LinkedIn-specific value—this would cut token count roughly in half.
Consolidate the duplicate workflow sections into a single, LinkedIn-specific workflow with concrete validation steps (e.g., 'Verify headline is under 220 characters', 'Check keyword density in about section').
Remove or verify the Python API examples—if LinkedInOptimizer is a real class in scripts/, keep them; if not, replace with direct instructional content showing Claude how to generate each profile section without pretending a library exists.
Move the keyword table to the referenced keywords-by-specialty.json file and keep only 2-3 inline examples to improve conciseness while maintaining actionability.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with significant redundancy. The 'When to Use' section repeats the description verbatim. Generic boilerplate sections (Input Validation, Error Handling, Response Template, Output Requirements, Implementation Details) add substantial token cost without healthcare-specific value. The 'Key Features' section restates the description again. Multiple workflow/process sections overlap. | 1 / 3 |
Actionability | The core capabilities section provides good Python code examples with concrete parameters (headline generation, about section writing, keyword tables). However, the code references modules (scripts/linkedin_optimizer.py, LinkedInOptimizer class) that may not actually exist or be executable. The CLI examples and API calls look plausible but are unverifiable, and much of the surrounding content is generic process boilerplate rather than actionable guidance. | 2 / 3 |
Workflow Clarity | The quality checklist provides a useful before/after validation structure, and the about section has a clear 4-step structure. However, the main 'Workflow' section is entirely generic (confirm objective, validate scope, use script, return result) with no healthcare-LinkedIn-specific steps. There's no validation checkpoint for verifying output quality against LinkedIn character limits or keyword density targets. | 2 / 3 |
Progressive Disclosure | References to external files (references/linkedin-examples.md, references/keywords-by-specialty.json, references/headline-templates.md) are well-signaled and one level deep. However, the main file is bloated with generic boilerplate that should either be removed or extracted. The 'Implementation Details' section says 'See Workflow above' creating circular references. Content organization mixes domain-specific value with generic template filler. | 2 / 3 |
Total | 7 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- aipoch/medical-research-skills
- Commit
0b96148
- Reviewed
Table of Contents
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