AB amazon-review-intelligence-extractor
Deep consumer insights from 1B+ pre-analyzed Amazon reviews. Extracts pain points, buying factors, user profiles, usage patterns, and differentiation opportunities across 11 analysis dimensions. Compares review sentiment across competitors and generates listing copy suggestions. Uses all 11 ZooData API endpoints with cross-validation. Use when user asks about: review analysis, customer feedback, pain points, what customers say, review insights, sentiment analysis, consumer insights, product improvements, voice of customer, review comparison, negative reviews, customer complaints, buying factors, user profile. Requires ZOODATA_API_KEY.
As a process B 73/100 · Nearly there — weak spots: 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
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5586 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 73/100
- 30Running it twice. 6 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 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
- 70Execution cost. Instruction body is 5586 tokens
- 100Steps. 52 steps
- 100Failures and branches. 6 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 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)
- -283 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 642: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 52 items
- +3Output format is stated explicitly
- +4Has examples (12 code blocks)
- +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: 82.