AF 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.
As a process F 70/100 · Will not run — References files that are not bundled: references/linkedin-examples.md, references/keywords-by-specialty.json, references/headline-templates.md
How to improve
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: references/linkedin-examples.md - warning
missing-refreference to a missing file: references/keywords-by-specialty.json - warning
missing-refreference to a missing file: references/headline-templates.md - note
frontmatter-keyunknown frontmatter key "skill-author"
Process rating: all ten parameters 70/100
- 0Tools and files. 3 referenced file(s) missing: references/linkedin-examples.md, references/keywords-by-specialty.json, references/headline-templates.md
- 30Running it twice. 1 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 65 steps
- 100Result and completion. Output format and completion criterion are stated
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2640 tokens
- 100Progress reporting. Reports progress
- low 18 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +3Description length 262: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 65 items
- +3Output format is stated explicitly
- +4Has examples (9 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.