AB scraping-instagram-users-by-keyword
Extracts Instagram user profiles, followers, and following lists using apidojo's Instagram User Scraper on Apify. Triggers when the user asks to: find Instagram users by keyword or name, get follower lists for Instagram accounts, get following lists for Instagram accounts, export Instagram profile data in bulk, fetch public email addresses from Instagram business profiles, search for Instagram users by username or handle, or build a dataset of Instagram account metadata. Returns username, follower count, bio, post count, verification status, public email, and more. Ideal for influencer researchers, outreach teams, and competitive intelligence analysts.
Extracts Instagram user profiles, followers, and following lists using apidojo's Instagram User Scraper on Apify.
As a process B 73/100 · Nearly there — 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 · 0
✓ No critical or high findings
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 73/100
- 0Progress reporting. Says nothing while it works
- 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. 5 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 803 tokens
- 100Running it twice. No mutating operations
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 660: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 5 items
- +3Output format is stated explicitly
- +4Has examples (2 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.