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 well-crafted skill description that excels across all dimensions. It opens with an explicit 'Use when' clause containing rich trigger terms for the healthcare domain, followed by specific concrete actions. The combination of platform (LinkedIn) and domain (healthcare professionals) creates a distinctive niche with minimal conflict risk.
| 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.' | 3 / 3 |
Completeness | Explicitly answers both what (crafts headlines, writes summaries, integrates keywords, builds branding) AND when ('Use when optimizing LinkedIn profiles for doctors, physicians, nurses, healthcare professionals, or medical researchers'). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'LinkedIn profiles', 'doctors', 'physicians', 'nurses', 'healthcare professionals', 'medical researchers', 'headlines', 'summaries', 'personal branding', 'medical careers'. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche combining LinkedIn optimization specifically for healthcare/medical professionals. The intersection of 'LinkedIn' + 'healthcare/medical' creates a distinct trigger space unlikely to conflict with general LinkedIn skills or general healthcare skills. | 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 content for LinkedIn optimization (headline formulas, about section structure, keyword tables, experience formulas) but is severely undermined by excessive boilerplate and redundancy. The core optimization guidance is actionable but surrounded by generic template content that doesn't add value. Removing the boilerplate and focusing on the healthcare-specific optimization patterns would significantly improve this skill.
Suggestions
Remove redundant sections: 'When to Use' repeats the description, 'Key Features' adds no value, and generic sections (Implementation Details, Output Requirements, Error Handling, Input Validation, Response Template) are boilerplate that should be deleted.
Consolidate the workflow into a single, specific sequence for LinkedIn optimization: 1) Gather role/specialty/achievements, 2) Generate headline using formula, 3) Write about section, 4) Optimize experience bullets, 5) Verify keyword integration.
Either provide the actual implementation of LinkedInOptimizer class or remove the code examples that reference it and replace with direct, executable patterns.
Move the keyword table and headline formulas to the top as the primary actionable content, since these are the most valuable parts of the skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with significant redundancy. The 'When to Use' section repeats the description verbatim, 'Key Features' restates the same description again, and there's excessive boilerplate (Implementation Details, Output Requirements, Error Handling, Input Validation, Response Template) that adds little value for a LinkedIn optimization skill. | 1 / 3 |
Actionability | Contains concrete Python code examples for headline generation, about section writing, and CLI usage, but the code references modules (scripts/linkedin_optimizer.py, LinkedInOptimizer class) without showing their implementation. The examples are illustrative but not fully executable without the referenced files. | 2 / 3 |
Workflow Clarity | The 'Workflow' section provides a generic 5-step process, and there's a quality checklist with before/after items. However, the workflow is abstract and doesn't provide specific validation checkpoints for LinkedIn profile optimization. The 'Example run plan' is generic boilerplate rather than task-specific guidance. | 2 / 3 |
Progressive Disclosure | References external files (references/linkedin-examples.md, references/keywords-by-specialty.json) appropriately, but the main document is bloated with generic boilerplate sections (Error Handling, Input Validation, Response Template) that should either be removed or moved to a shared reference. The useful content is buried among unnecessary scaffolding. | 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
4a48721
- Reviewed
Table of Contents
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