AC audience-research
Use to develop a deep, usable understanding of who a brand creates content for — sharp enough that every content skill resonates with them specifically. Run when the user says "who's my audience," "audience research," "target audience," "build a persona," "customer profile," "ideal customer profile" / "ICP," "who am I talking to," "understand my followers," or before content work that needs more audience depth than the brand-profile sketch. Reads brand-profile first and goes deeper: jobs-to-be-done, pains, objections, and the audience's ACTUAL language (voice-of-customer), grounded in real sources where possible — never demographic theater. Produces an audience.md that content-pillars, batch-content-plan, and the content skills read. Works for any business.
Use to develop a deep, usable understanding of who a brand creates content for — sharp enough that every content skill resonates with them specifically.
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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 62/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 38 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1586 tokens
- low 11 top-level sections: this looks like several domains in one skill
- medium 8 test cases, all positive: not one "should refuse" or "should ask first"
- low No test case covers injection arriving through data
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
- +4No input/output examples
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +3Description length 767: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 38 items
- +4Reference files are cited in the instructions (4 of 4)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.