AA finding-brand-collaborations-on-tiktok
Discovers brand collaboration patterns and sponsored content on TikTok using apidojo's TikTok scrapers on Apify. Triggers when the user asks to: find which brands are running TikTok influencer campaigns, discover creator-brand partnerships in a product category, identify which influencers are working with competitor brands on TikTok, see what sponsorship deals are active in a niche, find brands that sponsor TikTok creators, analyze competitor influencer marketing strategy on TikTok, or track paid partnership posts by category. Returns creator handle, brand name, post performance, estimated reach, and collaboration frequency. Ideal for influencer marketing teams, competitive intelligence analysts, and brand partnership managers.
Discovers brand collaboration patterns and sponsored content on TikTok using apidojo's TikTok scrapers on Apify.
As a process A 81/100 · 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 · 2
✓ No critical or high findings
Medium and low: 2
-
low Exfiltration
exfil-secret-in-urlSKILL.md:89Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)curl -X POST "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=…" -H "Content-Type: application/json" -d '{placeholder -
low Exfiltration
net-credential-useSKILL.md:89Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)curl -X POST "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=…" -H "Content-Type: application/json" -d '{vendor-host
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 81/100
- 0Progress reporting. Says nothing while it works
- 50Failures and branches. 0 branches, has a failure section
- 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. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1284 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +2Single-language instructions
- +3Description length 737: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 10 items
- +3Output format is stated explicitly
- +4Has examples (7 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.