AB discovering-tiktok-creators-by-niche
Discovers and ranks TikTok creators in any niche or topic using apidojo's TikTok scrapers on Apify. Triggers when the user asks to: find TikTok influencers in a specific niche, discover creators for a campaign, identify who's growing fastest on TikTok in a category, find micro-influencers on TikTok under a certain follower count, build a list of TikTok creators for brand partnerships, compare TikTok creator engagement rates in a vertical, or identify rising TikTok stars. Returns creator username, follower count, avg views, engagement rate, bio, and profile URL. Ideal for influencer marketers, brand partnership teams, and talent managers.
Discovers and ranks TikTok creators in any niche or topic using apidojo's TikTok scrapers on Apify.
As a process B 76/100 · Nearly there — weak spots: running it twice, 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 · 3
✓ No critical or high findings
Medium and low: 3
-
low Exfiltration
exfil-secret-in-urlSKILL.md:104Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)"https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=…" \
placeholder -
low Exfiltration
exfil-secret-in-urlSKILL.md:117Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=…"
placeholder -
low Exfiltration
net-credential-useSKILL.md:117Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=…"
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 76/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 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
- 100Steps. 10 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1303 tokens
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 645: enough signal without eating the budget
- +4Structure: 11 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.