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 search. When user proposes an idea or concept, systematically research its existing solutions through a structured search process. Trigger: User asks "has anyone done this", "check the status of xxx", "research xxx", etc.

Kinema.@leeshunee

Install

openclaw skills install @leeshunee/kinema-concept-research

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

Author: LeeShunEE
Organization: KinemaClawWorkspace
GitHub: https://github.com/KinemaClawWorkspace/kinema-concept-research

⚠️ Before First Use | 首次使用必读

首次使用此 skill 前，必须先读取 references/ONBOARDING.md 完成环境配置。

首次配置 → 读取 references/ONBOARDING.md 完成全部步骤
环境不可用（searxng-search 未安装、搜索失败）→ 读取 references/ONBOARDING.md Troubleshooting 排查修复
配置完成后 → 直接使用下方工作流

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：

Core word variants - synonyms, near-synonyms, different expressions | 核心词变体 - 同义词、近义词、不同表述
Tech stack words - involved technologies, frameworks, protocols | 技术栈词 - 涉及的技术、框架、协议
Scenario words - use cases, problems to solve | 场景词 - 使用场景、解决的问题
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：

Search 1-2 pages for each keyword group | 每组关键词搜索 1-2 页结果
Record all relevant links | 记录所有相关链接
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：

Web pages: Use web_fetch to grab content | Web 页面: 使用 web_fetch 抓取内容
GitHub Repo: Clone locally, read README | GitHub Repo: 克隆到本地，阅读 README
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}/

text

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。