AB extracting-youtube-comments-for-research
Extracts and analyzes YouTube comments for audience research using apidojo's YouTube scraper on Apify. Triggers when the user asks to: extract YouTube comments for research, analyze what viewers say in YouTube comments, scrape comments from a YouTube video for sentiment analysis, find common questions in YouTube comments, research audience feedback from YouTube video comments, extract top comments from a YouTube channel for audience insights, or analyze viewer reactions from YouTube comment sections. Returns comment text, likes on comment, reply count, commenter username, and timestamp. Ideal for content creators, brand researchers, product teams, and audience insight analysts.
Extracts and analyzes YouTube comments for audience research using apidojo's YouTube scraper on Apify.
As a process B 75/100 · 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:95Credential 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~youtube-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 75/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 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
- 85Steps. 10 steps, 2 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1174 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 686: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 10 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.