AB amazon-market-trend-scanner
Amazon category trend scanner. Scans Amazon category landscapes to discover trending subcategories, emerging niches, and market shifts. Tracks demand surges, brand consolidation, new entrant waves, price band migration, and margin changes across all subcategories under a parent category. Use when user asks about: market trends, category trends, trending categories, what's hot, emerging categories, trend scanner, which categories are growing, where the market is heading. Pick this to track how categories shift OVER TIME (trends, momentum, emerging niches). To evaluate one specific niche right now, use amazon-market-entry-analyzer; to discover products to sell, use amazon-opportunity-discoverer. Requires ZOODATA_API_KEY.
As a process B 72/100 · Nearly there — weak spots: when it triggers, running it twice
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
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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medium Exfiltration
net-redirectable-api-keyscripts/zoodata.py:74Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
Files scanned: 10. 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 31 steps
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2529 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 15 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)
- +4No input/output examples
- -238 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 728: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 31 items
- +3Output format is stated explicitly
- +4Reference files are cited in the instructions (2 of 2)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.