AB reddit-market-insights
Research ecommerce categories on Reddit to find opportunity areas (pain points) and trending products using semantic AI search via reddit-insights.com MCP server. Use when you need to: (1) Find ecommerce buyer pain points and complaints tied to a category, (2) Identify underserved use cases and product gaps, (3) Discover trending products and “what people are buying/recommending”, (4) Validate category/product ideas with real user feedback, (5) Extract verbatim quotes as evidence. Triggers: ecommerce market research, category opportunities, trending products, reddit ecommerce research, pain points for buyers, product gap, DTC research, Amazon alternatives, what to buy, gift ideas, product recommendations.
As a process B 65/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, running it twice
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: 3. 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 65/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 86 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2491 tokens
- low 12 top-level sections: this looks like several domains in one skill
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)
- -232 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 714: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 86 items
- +3Output format is stated explicitly
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.