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Install in OpenClaw
/install necessity-pain-point-selection
Description
Helps merchants selling utility / problem-solution products (car storage, multi-use kitchen shears, storage boxes, cleaning tools, etc.) do assortment and pr...
Usage Guidance
This skill appears internally consistent and low-risk, but consider these practical points before using: 1) Legal/compliance: ensure you have the right to collect or reuse competitor reviews and follow platform scraping rules (SKILL.md already advises compliance). 2) Data privacy: review data may contain PII—de-identify or handle per policy before processing. 3) Accuracy: the provided pain_point_extractor.py is a simple keyword/rule classifier and will produce false positives/negatives; always manually review suggested labels and examples before acting. 4) Execution: the script runs locally with Python and reads CSV/TXT or stdin—inspect the file and run in a safe environment. 5) If you plan to integrate this into automation with network connectivity, audit any added code or dependencies for external endpoints or credential use.
Capability Analysis
Type: OpenClaw Skill
Name: necessity-pain-point-selection
Version: 0.1.2
The skill bundle is a legitimate tool designed for e-commerce merchants to analyze customer reviews and improve product selection. The included Python script (scripts/pain_point_extractor.py) performs simple keyword-based classification of text files without any network activity, shell execution, or sensitive data access. The instructions in SKILL.md are strictly aligned with the stated purpose of product strategy and do not contain any prompt injection attacks or malicious directives.
Capability Assessment
Purpose & Capability
Name/description (VOC-driven pain-point extraction for utility products) matches included artifacts: SKILL.md describes review-driven selection and improvement and the repo contains a simple keyword-based pain_point_extractor.py for bulk processing. No unrelated binaries or credentials are requested.
Instruction Scope
SKILL.md focuses on collecting contextual facts from the user, processing reviews into pain labels, and producing actionable output. It explicitly limits scope (not for marketing copy or unrelated categories) and references compliant review collection. Instructions do not ask the agent to read system files, secrets, or contact unknown endpoints.
Install Mechanism
No install spec is provided (instruction-only skill); the included Python script is self-contained and will run locally if invoked. There are no remote downloads, package installs, or archive extracts that would write arbitrary code to disk.
Credentials
The skill requests no environment variables, credentials, or config paths. The script only reads local input files or stdin and outputs JSON/table results; it does not access network services or other systems.
Persistence & Privilege
always is false and model invocation is not disabled (normal). The skill does not request permanent presence or modify other skills or global agent settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install necessity-pain-point-selection - After installation, invoke the skill by name or use
/necessity-pain-point-selection - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.2
**Expanded review mining workflow and output structure for merchants.**
- Added detailed workflow for extracting pain points from reviews, including first-step merchant questions and scope boundaries.
- Expanded explicit output sections: summary for leadership, structured pain summary table, selection spec list, and improvement backlog.
- Separated action layers (Product, Information, Usage) and clarified root-cause/action chains for every pain point.
- Included review mining/validation best practices and references to newly added frameworks and data guides.
- Clarified triggers and when not to use the skill (e.g., for marketing copy or non-utility products).
- Added concrete pain-type definitions and labeling principles to ensure actionable, supplier-ready output.
v0.1.1
- Skill documentation fully translated from Chinese to English.
- Description clarified for international (non-Chinese) merchants and users.
- All examples, process steps, and tone adapted to English-language e-commerce context.
- No changes to feature logic or core workflow; updates are purely in documentation and language.
v0.1.0
Initial release of necessity-pain-point-selection skill.
- Helps merchants selling necessity-driven, functional products identify user pain points from reviews to guide product selection and improvement.
- Provides clear workflows for extracting actionable pain points from comments and turning them into practical product or improvement suggestions.
- Covers categories such as automotive storage, kitchen tools, home cleaning, and small appliances.
- Includes structured output: pain point summary table, prioritized suggestions, and recommended next steps for deeper analysis.
- Example-driven guidance tailored for real merchant scenarios and common negative review patterns.
Metadata
Frequently Asked Questions
What is Necessity Pain Point Selection?
Helps merchants selling utility / problem-solution products (car storage, multi-use kitchen shears, storage boxes, cleaning tools, etc.) do assortment and pr... It is an AI Agent Skill for Claude Code / OpenClaw, with 354 downloads so far.
How do I install Necessity Pain Point Selection?
Run "/install necessity-pain-point-selection" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Necessity Pain Point Selection free?
Yes, Necessity Pain Point Selection is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Necessity Pain Point Selection support?
Necessity Pain Point Selection is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Necessity Pain Point Selection?
It is built and maintained by RIJOY-AI (@rijoyai); the current version is v0.1.2.
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