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Install in OpenClaw
/install ecommerce-dropshipping-product-research
Description
Evaluates dropshipping products by scoring demand, competition, margin, creative fit, and risk to recommend go, test, or reject decisions.
README (SKILL.md)
Dropshipping Product Research
Overview
Dropshipping Product Research helps beginners and small operators evaluate whether a product is worth testing. It is a descriptive, non-API MVP focused on structured scoring, risk filtering, and clear go / test / reject decisions.
Trigger
Use this skill when the user wants to:
- evaluate a product idea for dropshipping
- compare multiple product candidates
- estimate risk, margin, and creative fit
- build a weekly shortlist for testing
Example prompts
- "Evaluate this product for dropshipping in the US"
- "Should I test a galaxy projector or pet grooming glove?"
- "Give me a go / test / reject recommendation"
- "Help me score 3 product ideas for my store"
Workflow
- Capture candidate, market, and positioning constraints.
- Infer demand, competition, margin, creative angle, and risk.
- Produce a viability score and recommendation.
- Summarize why it may win, why it may fail, and what to test next.
Inputs
- product name or keyword
- optional product link or niche
- target market
- price target or cost hints
- mode: single product / batch / trend scouting
Outputs
- viability score
- sub-scores: demand, competition, margin, creative fit, risk
- recommendation: Go / Test / Reject
- memo with hypotheses and next steps
Safety
- No marketplace scraping or real-time trend API access
- No guarantee of profit or compliance clearance
- Recommendations are heuristic and should be validated with real tests
Acceptance Criteria
- Must output markdown
- Must include all five scoring dimensions
- Must include Go / Test / Reject recommendation
- Must include at least three risk or execution notes
Usage Guidance
This skill is a simple, local heuristic tool — it does not call external services or request credentials. Before relying on its recommendations: (1) treat outputs as heuristics only and validate with supplier quotes and ad tests, (2) be aware the scoring is keyword-based and may miss context or nuance (e.g., large prices, multi-word markets), and (3) review the included handler.py if you need stronger guarantees or to adapt scoring rules. If you plan to extend it (add real marketplaces, ad data, or automation), expect to need API keys and to reassess privacy and security implications.
Capability Analysis
Type: OpenClaw Skill
Name: ecommerce-dropshipping-product-research
Version: 1.0.0
The skill is a straightforward heuristic tool for evaluating dropshipping product ideas. The logic in `handler.py` uses basic keyword matching and arithmetic to generate scores, and the instructions in `SKILL.md` are strictly aligned with the stated purpose without any signs of prompt injection or unauthorized data access.
Capability Assessment
Purpose & Capability
The name and description describe heuristic scoring for dropshipping product ideas; the included handler.py implements exactly that with keyword lists and simple scoring. No unexpected credentials, binaries, or services are required.
Instruction Scope
SKILL.md limits the skill to heuristic scoring and explicitly states no marketplace scraping or real-time API access. The runtime code only parses the provided text, computes scores, and returns markdown; it does not read other files, environment variables, or send data externally.
Install Mechanism
There is no install specification (instruction-only behavior) and no downloads or package installs. Code files are included but they are local Python scripts executed by the agent; nothing is fetched from external URLs.
Credentials
The skill requires no environment variables, credentials, or config paths. The code does not reference any secrets or external tokens.
Persistence & Privilege
always is false (not force-included). disable-model-invocation is default (agent can invoke autonomously), which is expected. The skill does not modify other skills or system-wide settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install ecommerce-dropshipping-product-research - After installation, invoke the skill by name or use
/ecommerce-dropshipping-product-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Dropshipping Product Research skill.
- Provides structured evaluation and scoring for dropshipping product ideas.
- Outputs a viability score with demand, competition, margin, creative fit, and risk sub-scores.
- Delivers clear Go / Test / Reject recommendations with supporting memo.
- Designed for beginners and small operators to shortlist and compare products.
- No marketplace scraping or live trend data; all recommendations are heuristic.
Metadata
Frequently Asked Questions
What is Dropshipping Product Research?
Evaluates dropshipping products by scoring demand, competition, margin, creative fit, and risk to recommend go, test, or reject decisions. It is an AI Agent Skill for Claude Code / OpenClaw, with 95 downloads so far.
How do I install Dropshipping Product Research?
Run "/install ecommerce-dropshipping-product-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Dropshipping Product Research free?
Yes, Dropshipping Product Research is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Dropshipping Product Research support?
Dropshipping Product Research is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Dropshipping Product Research?
It is built and maintained by haidong (@harrylabsj); the current version is v1.0.0.
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