AB finding-restaurant-brands-on-instagram
Discovers restaurant brands, food businesses, and hospitality accounts on Instagram using apidojo's Instagram Scraper on Apify. Triggers when the user asks to: find restaurant brands on Instagram, discover food businesses for outreach on Instagram, build a list of restaurant Instagram accounts, find cafes or food chains active on Instagram, identify local restaurant brands by hashtag or location on Instagram, or prospect food and beverage businesses via their Instagram presence. Returns account handle, follower count, bio, post count, and engagement data. Ideal for food tech SaaS vendors, beverage distributors, and B2B service providers targeting restaurants.
Discovers restaurant brands, food businesses, and hospitality accounts on Instagram using apidojo's Instagram Scraper on Apify.
As a process B 72/100 · Nearly there — weak spots: result and completion, 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 72/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 50Failures and branches. 0 branches, has a failure section
- 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. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 664 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)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 667: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (3 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.