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在 OpenClaw 中安装
/install ecommerce-dropshipping-product-research
功能描述
Evaluates dropshipping products by scoring demand, competition, margin, creative fit, and risk to recommend go, test, or reject decisions.
使用说明 (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
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install ecommerce-dropshipping-product-research - 安装完成后，直接呼叫该 Skill 的名称或使用
/ecommerce-dropshipping-product-research触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
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.
元数据
常见问题
Dropshipping Product Research 是什么？
Evaluates dropshipping products by scoring demand, competition, margin, creative fit, and risk to recommend go, test, or reject decisions. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 95 次。
如何安装 Dropshipping Product Research？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install ecommerce-dropshipping-product-research」即可一键安装，无需额外配置。
Dropshipping Product Research 是免费的吗？
是的，Dropshipping Product Research 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Dropshipping Product Research 支持哪些平台？
Dropshipping Product Research 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Dropshipping Product Research？
由 haidong（@harrylabsj）开发并维护，当前版本 v1.0.0。
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