AB extracting-tiktok-comments-for-research
Extracts and analyzes TikTok comments from any video or creator using apidojo's TikTok Comments scraper on Apify. Triggers when the user asks to: scrape TikTok comments from a video, analyze what viewers say about a TikTok post, extract comment data for sentiment analysis, find top comments on a viral TikTok video, collect TikTok user feedback from comments, build a dataset of TikTok community reactions, study audience sentiment on TikTok content, or research what a target audience cares about from TikTok comments. Returns commenter username, comment text, likes on comment, reply count, and timestamp. Ideal for market researchers, brand managers, content creators, and academic researchers.
Extracts and analyzes TikTok comments from any video or creator using apidojo's TikTok Comments scraper on Apify.
As a process B 71/100 · 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 · 1
✓ No critical or high findings
Medium and low: 1
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low Exfiltration
exfil-secret-in-urlSKILL.md:101Credential 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-comments-scraper/runs?token=…" \
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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 71/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 5 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (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. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1455 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 698: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 20 items
- +3Output format is stated explicitly
- +4Has examples (5 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.