AA monitoring-tiktok-mentions-of-brand
Monitors TikTok for brand mentions and product discussions using apidojo's TikTok scraper on Apify. Triggers when the user asks to: track mentions of a brand on TikTok, monitor TikTok hashtags for brand content, find TikTok videos talking about a product or company, see what TikTok says about a brand this week, track TikTok reactions to a product launch, find TikTok creators who mentioned a competitor brand, monitor brand sentiment on TikTok, or discover viral TikTok content about a specific brand or product. Returns creator handle, views, likes, comments, sentiment signal, and video caption excerpt. Ideal for brand managers, community teams, crisis communications, and product marketing teams.
Monitors TikTok for brand mentions and product discussions using apidojo's TikTok scraper 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 · 3
✓ No critical or high findings
Medium and low: 3
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low Exfiltration
exfil-secret-in-urlskill-card.md:40Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (placeholder value)- [Apify Actor API endpoint](https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=…)
placeholder -
low Exfiltration
exfil-secret-in-urlSKILL.md:88Credential 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:88Credential 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 1314 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 702: 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.