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Agent Skill · 研究检索

knowledge-forge(知识锻造)

knowledge-forge 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

knowledge-forge 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 11,441。

总安装 11,268周安装 484GitHub Stars 1下载 3,949核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:knowledge-forge(知识锻造)
来源仓库:https://github.com/angjustinl/knowledge-forge
安装命令:
openclaw skills install knowledge-forge
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install knowledge-forge

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

知识锻造工具将原始经验或文档转化为结构化、可复用的认知资产,便于跨场景迁移。适合经验沉淀、案例转化或知识资产化需求,常用于商业分析、项目复盘等场景。通过输入文本或文档触发处理流程,输出标准化知识条目供后续检索与使用。需确认输入数据来源的合法性,避免将敏感或未授权内容纳入结构化知识库。

维护时注意版本控制,确保知识资产的更新与原始材料变更保持同步。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install knowledge-forge;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

knowledge-forge 怎么安装?

在终端执行 openclaw skills install knowledge-forge。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

knowledge-forge 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

knowledge-forge 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

knowledge-forge 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

knowledge-forge 用的人多吗?

公开数据是累计安装 11,268 次,近一周安装 484 次,下载 3,949 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

knowledge-forge 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
knowledge-forge

知识锻造 · 本页

11,2681只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
knowledge-forge
description
>-

Knowledge Forge

Forge raw experience into transferable cognitive assets using a 4-step conversion engine.

Core Concept

Experience is abundant. Answers are scarce.

Most experts are strong inside their own world. But when they open a document or step on stage, others can't follow. The problem is not lack of experience -- it's that experience has not been modeled.

A modeled experience is one that has been abstracted into a structure that transfers across contexts. This skill performs that transformation.

Conversion Engine

When the user provides raw material (a case study, personal summary, business document, draft speech, or any form of experience narrative), execute these 4 steps sequentially:

Step 1: Perspective Flip -- "My experience" -> "Your challenge"

Identify what the user accomplished, then reframe it as a universal challenge the audience faces.

  • Extract the core problem the user solved
  • Abstract it away from domain-specific details
  • Restate it as a challenge the target audience recognizes in their own work
  • The audience should think "yes, I face this too" -- not "interesting, but that's your job"

Key question to answer: "What struggle does the audience already have that this experience speaks to?"

For modeling patterns and examples, see modeling-patterns.md.

Step 2: Experience Modeling -- Specific story -> Transferable structure

The raw experience is a story. Transform it into a model -- an abstraction that works across scenarios.

  • Find the structural pattern hidden in the specific case
  • Name it with a memorable, compact label (e.g., "The 100->10->1 Funnel")
  • Validate: does the model apply to at least 2-3 other domains the audience cares about?

Key question to answer: "What is the underlying structure that makes this experience work -- independent of the specific domain?"

For modeling archetypes and before/after examples, see modeling-patterns.md.

Step 3: Narrative Reconstruction

Rebuild the narrative using this sequence:

  1. Challenge alignment -- Present the universal challenge so the audience enters the tension. Spend substantial space here. Make old/obvious answers visibly insufficient.
  2. Model reveal -- Introduce the abstracted model as the new lens. Emphasize the shift in thinking (role change, mental model upgrade), NOT tool details or step-by-step procedures.
  3. Evidence from experience -- Use the original story as proof that the model works, not as the centerpiece.

Principle: Present the "Dao" (the judgment behind decisions), not the "Shu" (the operational steps). Tools and procedures are forgettable; the cognitive shift is what transfers.

For techniques on designing cognitive gaps, see challenge-design.md.

Step 4: Anchor Design -- The one sentence they carry away

Design a single, specific, portable judgment -- the anchor.

Requirements for a good anchor:

  • Specific -- not a vague platitude ("work smarter") but a concrete reframing ("AI doesn't save you time -- it changes which game you're playing")
  • Sticky -- compact enough to remember and repeat
  • Generative -- triggers new thinking when applied to the audience's own context

Key question to answer: "If the audience forgets everything else, what is the ONE sentence that, by itself, changes how they think?"

Place the anchor at the structural climax of the output. It must feel earned -- a culmination of the challenge and model, not a disconnected slogan.

Output

After completing the 4 steps internally, produce the final output.

Determining Output Format

If the user specifies a format, use it. Otherwise, infer from context:

SignalFormat
"presentation", "talk", "speech", "share"Presentation Script
"course", "training", "teach", "workshop"Course Outline
"article", "post", "essay", "document"Article / Document
"summary", "card", "one-pager", "memo"Knowledge Card
Ambiguous or unspecifiedKnowledge Card (default)

For output templates and structural guidance, see output-formats.md.

Output Structure

Every output, regardless of format, must contain these elements:

  1. The Challenge -- The universal problem, stated in the audience's language
  2. The Model -- The transferable structure, with a memorable label
  3. The Evidence -- The original experience, reframed as proof of the model
  4. The Anchor -- The one sentence to carry away

Transformation Log

After the main output, append a brief ## Transformation Log showing the key decisions made during conversion:

## Transformation Log

- **Perspective Flip**: [Original framing] -> [Audience-facing challenge]
- **Model Extracted**: [Model name and one-line description]
- **Narrative Shift**: [What was de-emphasized vs. elevated]
- **Anchor**: "[The one sentence]"

This log helps the user understand and iterate on the transformation.

平台分布

OpenClaw

94.06%
按下载量换算3,714

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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