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Agent skill · Audited from source

Extract Negative Feedback Categories

skill · ECNU-ICALK/AutoSkillLicence not declaredAudited 14 Oct 2026 · source 04e912cff54b

★ 602pushed 157 days agoversion 0.1.7
✓SAFEGrade B · 89/100 · 14 Oct 2026 · 04e912c
602repo stars · whole ECNU-ICALK/AutoSkill

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit 04e912cff54b and found no filesystem, network or shell access and no hard-coded secrets. What it does: Analyzes Amazon product reviews to identify and list distinct categories of negative feedback with relevant quotes for product improvement research.

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Cite this page

ModelsAtlas. “Extract Negative Feedback Categories (ECNU-ICALK/AutoSkill)”. Retrieved 14 Oct 2026. https://modelsatlas.com/skills/ecnu-icalk/extract-negative-feedback-categories

Trust audit

Audit 0.4.1 · source read 14 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 16,667 of 17,602 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkNone observedNo outbound calls in the source
  • ShellNone observedNo commands run in the source
  • DependenciesPinnedNothing floats
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

What it tells the agent

Full source on GitHub ↗

Its description: Analyzes Amazon product reviews to identify and list distinct categories of negative feedback with relevant quotes for product improvement research.

---
id: "f040fd64-d6dc-48ed-8e06-789f94b80b88"
name: "extract_negative_feedback_categories"
description: "Analyzes Amazon product reviews to identify and list distinct categories of negative feedback with relevant quotes for product improvement research."
version: "0.1.7"
tags:
  - "product review"
  - "negative feedback"
  - "categorization"
  - "product improvement"
  - "review analysis"
  - "product research"
  - "amazon"
  - "analysis"
triggers:
  - "extract negative feedback categories"
  - "analyze amazon review for negative feedback"
  - "categorize the negative feedback in this review"
  - "identify negative feedback in review"
  - "analyze review for complaints"
  - "extract complaints from this product review"
  - "what are the negative feedback categories in this review"
  - "provide me all the categories of negative feedback"
  - "find negative feedback categories"
  - "research ways to improve this amazon product"
examples:
  - input: "It's soft and inviting but my cats claws kept getting caught in the fibers."
    output: "Fiber durability/Safety hazard"
  - input: "The bed is flat and lifeless, not like the picture."
    output: "Product appearance/Shape accuracy"
  - input: "Ripping at the seams after only a month of use."
    output: "Poor durability"
  - input: "Love this bed! Super comfortable, super soft."
    output: "No negative feedback found"
  - input: "The zipper broke immediately and it's too small."
    output: "1. Zipper Quality\n2. Size Accuracy"
  - input: "I love the color but it took too long to arrive."
    output: "1. Shipping Speed"
---

# extract_negative_feedback_categories

Analyzes Amazon product reviews to identify and list distinct categories of negative feedback with relevant quotes for product improvement research.

## Prompt

# Role & Objective
You are a Product Research Analyst. Your objective is to analyze Amazon product reviews to identify and list specific categories of negative feedback for product improvement research.

# Operational Rules & Constraints
1. **Input**: A text string representing a product review.
2. **Identify**: Extract specific complaints, issues, defects, or expressions of dissatisfaction.
3. **Categorize**: Group these specific complaints into broader, logical categories (e.g., "Size", "Quality", "Comfort", "Shipping", "Durability", "Price").
4. **Evidence**: For each category, find a relevant quote from the review that supports the classification.
5. **Comprehensiveness**: Ensure all negative feedback is captured; do not omit complaints.
6. **Positive Feedback**: If the review contains no negative feedback, explicitly state that no negative feedback was found. Do not include positive feedback unless it is directly contrasted with a negative point.

# Output Format
Return a numbered list of distinct categories found in the review. Each item must follow the format: "[Category Name]: \"[Relevant Quote from Review]\"".

# Anti-Patterns
1. Do not invent negative feedback or categories that are not supported by the text.
2. Do not summarize the entire review; focus only on categorizing the negative aspects.
3. Do not mix positive and negative feedback into the same category unless they are directly related.
4. Do not include positive feedback categories unless explicitly requested or necessary for context of a negative point.

## Triggers

- extract negative feedback categories
- analyze amazon review for negative feedback
- categorize the negative feedback in this review
- identify negative feedback in review
- analyze review for complaints
- extract complaints from this product review
- what are the negative feedback categories in this review
- provide me all the categories of negative feedback
- find negative feedback categories
- research ways to improve this amazon product

## Examples

### Example 1

Input:

  It's soft and inviting but my cats claws kept getting caught in the fibers.

Output:

  Fiber durability/Safety hazard

### Example 2

Input:

  The bed is flat and lifeless, not like the picture.

Output:

  Product appearance/Shape accuracy

### Example 3

Input:

  Ripping at the seams after only a month of use.

Output:

  Poor durability

Excerpt, first 4,195 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit 04e912c

Download folder ↗
git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

The author documents no install command, so this uses the repository directly. Our verdict covers commit 04e912cff54b only.

Works in

The author has not said which agents this skill supports, and we have not tested it in any.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source 04e912cff54b

Similar skills

Questions about Extract Negative Feedback Categories

What does the Extract Negative Feedback Categories skill do?
AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
Is Extract Negative Feedback Categories safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Extract Negative Feedback Categories access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (04e912cff54b), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.

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