AC keyword-research
When the user wants to research keywords, find target keywords, or analyze search intent. Also use when the user mentions "keyword research," "keyword tool," "target keywords," "search volume," "search intent," "keyword difficulty," "topical map," "keyword clustering," "People Also Ask," "Google autocomplete," "autocomplete keywords," or "alphabet method." For clusters, use content-strategy.
As a process C 59/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice
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: 2. 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 59/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (keyword-research) differs from the folder (keyword-research-seo)
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 100Steps. 49 steps
- 100Execution cost. Instruction body is 2540 tokens
- low 13 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
- +4Description does not say when NOT to use the skill (false activations)
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 12 example trigger phrases
- +3Description length 394: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 49 items
- +3Output format is stated explicitly
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.