AA brand-prospect-researcher
Find brands actively spending on influencer and creator campaigns. Scans trade press, Instagram, job boards, and news for active-spender signals, then outputs a scored, prioritized prospect list with contacts. Use when you need to find brands to pitch, identify active advertisers, build a prospect list, research which brands are hiring influencer marketers, or find companies launching creator campaigns. Triggers on: 'find brands to pitch,' 'prospect list,' 'who's spending on influencers,' 'brand prospects,' 'active advertisers,' 'campaign activity,' or any request to identify brands actively investing in creator/influencer marketing.
As a process A 86/100 · Runs to the end — weak spots: 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: 3. 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 86/100
- 0Progress reporting. Says nothing while it works
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 14 steps
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1476 tokens
- 100Running it twice. No mutating operations
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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)
- +1No license
- +2Single-language instructions
- +3Description length 641: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 14 items
- +3Output format is stated explicitly
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.