AC linkedin-skills
Use when someone wants to grow an organic LinkedIn presence — a content strategy for a career change or consulting or thought leadership, a rewritten profile or headline, post drafts and hooks, a posting cadence or newsletter plan, connection notes and outreach, a commenting strategy, repurposing an article or talk into posts, or a read on why their reach dropped. Triggers on "grow my LinkedIn", "fix my headline", "write a LinkedIn post", "what should I post about", "LinkedIn strategy", "connection request", "my reach dropped". Forks context to route to one of five sub-skills, and refuses automation, scraping, pods, and bulk DMs before any drafting starts.
Use when someone wants to grow an organic LinkedIn presence — a content strategy for a career change or consulting or thought leadership, a rewritten profile…
As a process C 60/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting
How to improve
- 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: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1112 tokens
- 100Running it twice. Mutating operations check current state
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +3Description length 664: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.