AB analyzing-competitor-youtube-content-strategy
Analyzes a competitor's YouTube channel content strategy and performance using apidojo's YouTube scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on YouTube, see what video types perform best for a competitor, reverse-engineer a competitor's YouTube content calendar, benchmark your YouTube channel against a competitor, identify content gaps vs. a competitor on YouTube, understand what topics drive views for a competitor, or compare subscriber growth and video performance between two YouTube channels. Returns video cadence, format mix, top-performing topics, view benchmarks, and engagement analysis. Ideal for content marketing teams, YouTube strategists, and brand video teams.
Analyzes a competitor's YouTube channel content strategy and performance 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 · 2
✓ No critical or high findings
Medium and low: 2
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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)curl -X POST "https://api.apify.com/v2/acts/apidojo~youtube-scraper/runs?token=…" -H "Content-Type: application/json" -d '{"startUrls": [{"url": "https://www.youtube.com/@competitorhaplaceholder -
low Exfiltration
net-credential-useSKILL.md:95Credential 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~youtube-scraper/runs?token=…" -H "Content-Type: application/json" -d '{"startUrls": [{"url": "https://www.youtube.com/@competitorhavendor-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 75/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 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. 6 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1453 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 717: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 6 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.