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.
58
67%
Does it follow best practices?
Impact
—
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 roles. 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 domain-specific content (headline formulas, keyword tables, about section structure, code examples) buried under layers of generic boilerplate that appears templated and not tailored to LinkedIn optimization. The redundancy is severe—the skill description is repeated at least three times, and generic sections like Error Handling, Input Validation, and Response Template consume significant tokens without adding LinkedIn-specific value. Stripping the boilerplate and focusing on the Core Capabilities content would dramatically improve this skill.
Suggestions
Remove all generic boilerplate sections (Input Validation, Error Handling, Response Template, Output Requirements, Implementation Details) that are not specific to LinkedIn profile optimization—these waste tokens on guidance Claude already knows.
Eliminate redundancy: the skill description is repeated verbatim in 'When to Use', 'Key Features', and the opening line. Consolidate into a single concise purpose statement.
Merge the overlapping workflow sections (Example Usage run plan, Workflow, Quality Checklist) into one clear sequence with explicit validation steps, e.g., 'After generating headline, verify it's under 220 characters'.
Verify that referenced files (scripts/linkedin_optimizer.py, references/*.md) actually exist in the bundle, or remove code examples that import from non-existent modules and replace with inline executable logic.
| 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 LinkedIn-optimization-specific value. The 'Key Features' section restates the description again. Multiple workflow 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, the code references modules (scripts/linkedin_optimizer.py, LinkedInOptimizer class) that may not exist given no bundle files are provided, making executability uncertain. The generic workflow steps (confirm, validate, run) are too abstract to be actionable. | 2 / 3 |
Workflow Clarity | There are multiple workflow-like sections (Example Usage run plan, Workflow section, Quality Checklist) but they conflict and overlap. The Quality Checklist provides useful validation checkpoints, but the main Workflow section is entirely generic and not specific to LinkedIn optimization. No clear feedback loop for validating output quality against LinkedIn constraints (character limits, keyword density). | 2 / 3 |
Progressive Disclosure | References to external files (references/linkedin-examples.md, references/keywords-by-specialty.json, references/headline-templates.md) are clearly signaled and one level deep, which is good. However, no bundle files are provided to verify these exist. The main file itself is monolithic with too much inline content that could be split, and the 'Implementation Details' section says 'See Workflow above' which is a same-file circular reference adding no value. | 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
d3c2c0f
- Reviewed
Table of Contents
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.