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 correctly, 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.
The skill contains genuinely useful domain-specific content (headline formulas, keyword tables, about section structure, code examples) buried under layers of generic boilerplate that adds no value for LinkedIn profile optimization. Roughly half the content is template filler repeated across skills (Input Validation, Error Handling, Response Template, Output Requirements) that wastes tokens and obscures the actionable guidance. The core capabilities section is the strongest part but is undermined by inconsistent script references and redundant workflow sections.
Suggestions
Remove all generic boilerplate sections (Output Requirements, Error Handling, Input Validation, Response Template, Implementation Details) that don't contain LinkedIn-optimization-specific guidance—these waste tokens on things Claude already knows how to do.
Eliminate the duplicated description text that appears verbatim in 'When to Use' and 'Key Features' sections.
Consolidate the multiple overlapping workflow/process sections (Example Usage run plan, Workflow, Quality Checklist) into a single clear workflow with validation steps.
Resolve the inconsistency between `scripts/main.py` and `scripts/linkedin_optimizer.py` references, and clarify whether the LinkedInOptimizer class actually exists or if the code examples are illustrative patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with significant redundancy. The 'When to Use' section repeats the description verbatim. Generic boilerplate sections (Output Requirements, Error Handling, Input Validation, Response Template, Implementation Details) add substantial token cost without LinkedIn-optimization-specific value. The 'Key Features' section restates the description again. Multiple workflow/process sections overlap. | 1 / 3 |
Actionability | The Core Capabilities section provides concrete Python code examples with specific parameters and output formats, and the keyword table and headline formulas are genuinely useful. However, it's unclear whether the referenced scripts actually exist or work—the code references both `scripts/main.py` and `scripts/linkedin_optimizer.py` inconsistently, and the `LinkedInOptimizer` class may be fictional. The CLI examples and Quick Start are plausible but unverifiable. | 2 / 3 |
Workflow Clarity | There are multiple workflow-like sections (Example Usage run plan, Workflow section, Quality Checklist) but they overlap and none provides a single clear end-to-end sequence with validation checkpoints. The Quality Checklist is useful but disconnected from the execution workflow. No explicit validation/feedback loop for checking output quality. | 2 / 3 |
Progressive Disclosure | References to external files (references/linkedin-examples.md, keywords-by-specialty.json, headline-templates.md) are well-signaled and one level deep. However, the main file is monolithic with too much inline content—the generic boilerplate sections (Error Handling, Input Validation, Response Template, Output Requirements) bloat the file and should either be removed or separated. The 'Implementation Details' section says 'See Workflow above' which is a self-referential non-reference. | 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
8277276
- Reviewed
Table of Contents
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