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leeshunee

Kinema's Concept Re-Search

by Kinema. · GitHub ↗ · v1.0.4 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install kinema-concept-research
Description
Research whether a concept has been implemented and its current state. Use multi-language keywords, multi-engine cross-validation, and multi-dimensional sear...
README (SKILL.md)

Concept Research - Concept Status Research | 概念现状调研

Research whether a concept has been implemented and what it looks like. Search thoroughly and output a summary list.

调查一个概念是否已被实现、做成什么样子。彻底搜索,输出摘要清单。

Workflow | 工作流

Phase 1: Concept Clarification (Dialogue) | 阶段 1: 概念澄清(对话)

Through multi-turn dialogue clarify: | 通过多轮对话明确:

  • What user wants to do | 用户想做什么
  • Core functionality | 核心功能是什么
  • Target users | 目标用户是谁
  • Differentiation expectations | 差异化期望是什么

Output: One-sentence concept definition | 产出: 一句话概念定义

Phase 2: Keyword Breakdown | 阶段 2: 关键词拆解

Based on concept definition, break down into multiple keyword groups: | 基于概念定义,拆解为多组关键词:

  1. Core word variants - synonyms, near-synonyms, different expressions | 核心词变体 - 同义词、近义词、不同表述
  2. Tech stack words - involved technologies, frameworks, protocols | 技术栈词 - 涉及的技术、框架、协议
  3. Scenario words - use cases, problems to solve | 场景词 - 使用场景、解决的问题
  4. Combined words - core word + tech/scenario | 组合词 - 核心词 + 技术/场景

Generate Chinese and English versions for each group. | 每组生成中英文版本。

Phase 3: Broad Search | 阶段 3: 广度搜索

Search using searxng-search batch by batch: | 使用 searxng-search 逐批搜索:

  1. Search 1-2 pages for each keyword group | 每组关键词搜索 1-2 页结果
  2. Record all relevant links | 记录所有相关链接
  3. Preliminary relevance marking based on title/abstract | 根据标题/摘要初步标记相关性

Time filter (if search engine supports): | 时间过滤(如搜索引擎支持):

  • Five years ago, two years ago, one year ago, three months ago, recent three months | 五年之前、两年之前、一年之前、三个月之前、最近三个月

Phase 4: Deep Exploration | 阶段 4: 深度探索

From high-relevance results, select 3-5 for deep exploration: | 从高相关性结果中挑选 3-5 个进行深度探索:

  1. Web pages: Use web_fetch to grab content | Web 页面: 使用 web_fetch 抓取内容
  2. GitHub Repo: Clone locally, read README | GitHub Repo: 克隆到本地,阅读 README
  3. PDF/Papers: Download and read | PDF/论文: 下载阅读

Record exploration content: | 探索内容记录:

  • Core functionality | 核心功能
  • Technical implementation | 技术实现
  • Pros and cons | 优缺点
  • Similarities/differences with user concept | 与用户概念的异同

Phase 5: Output Report | 阶段 5: 输出报告

Generate summary list: | 生成摘要清单:

Field Description 说明
Link Original URL 链接
Overview What it is, what it does 概述
Basic Approach Core technical solution 基本思路
Similarities Common points with user concept 相同点
Differences Differences from user concept 不同点
Analysis Opportunities, improvement space 分析

Project Directory | 项目目录

All files saved in: projects/research-{uuid}/ | 所有文件保存在:projects/research-{uuid}/

projects/research-{uuid}/
├── concepts/
│   └── definition.md
├── keywords/
│   └── keywords.md
├── search/
│   ├── broad/
│   └── deep/
├── repos/
├── papers/
└── report.md

Search Tools | 搜索工具

Priority: searxng-search. If SearXNG not deployed, can use ddg-search. | 优先使用 searxng-search。如 SearXNG 未部署,可使用 ddg-search。


Related Documentation | 相关文档

  • searxng-search skill
  • skill-creator skill
Usage Guidance
This skill appears coherent for concept research: it will run searches, fetch web pages, clone public GitHub repos, download PDFs, and save results to projects/research-{uuid}/. Before installing/using: ensure the agent environment provides the expected tools (web_fetch/search skills and git or repo access); be aware it will download arbitrary external content — do not execute downloaded code without inspection; store results in an isolated workspace if you worry about malicious files; consider limiting the agent's network or filesystem privileges if you want tighter control. No credentials are requested and there are no hidden install scripts according to the package contents.
Capability Analysis
Type: OpenClaw Skill Name: kinema-concept-research Version: 1.0.4 The kinema-concept-research skill is a well-documented tool designed for systematic market and technical research. It follows a structured workflow involving keyword generation, multi-engine searching (SearXNG/DuckDuckGo), and deep exploration of web content and GitHub repositories. All actions, including fetching web pages and cloning repositories for README analysis, are directly aligned with the stated purpose of concept validation, and there are no indicators of malicious intent, data exfiltration, or unauthorized system modification.
Capability Assessment
Purpose & Capability
The name/description (concept research) aligns with the instructions: clarify the concept, generate keywords, run broad/deep searches, fetch pages, clone repos, download PDFs, and produce a report. The requested actions are expected for a research workflow.
Instruction Scope
SKILL.md instructs the agent to use searxng-search (or ddg-search), web_fetch, and to clone GitHub repositories locally and save all artifacts under projects/research-{uuid}/. It does not instruct the agent to execute any downloaded code, nor does it attempt to read unrelated local files or environment variables. However, it assumes availability of network-fetch and git-like tooling which are not declared in the skill metadata.
Install Mechanism
No install spec or code files present (instruction-only). Nothing will be written by an installer; all actions happen at runtime via the agent's normal capabilities.
Credentials
The skill requests no environment variables, credentials, or config paths. There are no unrelated secret requests.
Persistence & Privilege
The workflow writes search results and downloaded artifacts to a project directory (projects/research-{uuid}/). This is proportional to the task, but users should be aware the skill will persist external content to the agent's filesystem.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install kinema-concept-research
  3. After installation, invoke the skill by name or use /kinema-concept-research
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.4
Add GitHub repository link to SKILL.md and README.md
v1.0.3
README 添加 ClawHub 安装链接
v1.0.2
version sync
v1.0.1
Standardize SKILL.md
v1.0.0
- Initial release of kinema-concept-research skill. - Provides a structured workflow for researching whether a concept has existing implementations and summarizing findings. - Supports multi-language keywords, multi-engine searches, and multi-dimensional exploration (web, GitHub, papers). - Outputs organized reports with links, overviews, technical approaches, similarities, differences, and analysis.
Metadata
Slug kinema-concept-research
Version 1.0.4
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 5
Frequently Asked Questions

What is Kinema's Concept Re-Search?

Research whether a concept has been implemented and its current state. Use multi-language keywords, multi-engine cross-validation, and multi-dimensional sear... It is an AI Agent Skill for Claude Code / OpenClaw, with 184 downloads so far.

How do I install Kinema's Concept Re-Search?

Run "/install kinema-concept-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Kinema's Concept Re-Search free?

Yes, Kinema's Concept Re-Search is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Kinema's Concept Re-Search support?

Kinema's Concept Re-Search is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Kinema's Concept Re-Search?

It is built and maintained by Kinema. (@leeshunee); the current version is v1.0.4.

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