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SkillForge 是一个仓库蒸馏工作台，将杂乱的项目材料转化为可重复使用的 AI 技能包。

它扫描本地文件夹，解析混合文档格式，提取工作流程证据，将这些证据聚集成能力，并通过内置的网页界面将结果编译为可预览和导出的技能输出。

为什么选择SkillForge？

团队已经拥有所需的原始知识：产品文档、设计笔记、研究报告、运行手册、标准操作程序（SOP）、会议笔记、API参考，以及散布在仓库和共享文件夹中的随机标记文件。

难点在于将这些支离破碎、隐含的知识转化为AI代理可重复利用的内容。

手动操作既慢又脆弱：

• 一次性脚本启动快速但维护困难
• 通用代理框架有助于编排，但它们不会自动将仓库知识提炼为技能包
• 手动复制粘贴到提示词中，无法跨团队或项目扩展

SkillForge专注于这个缺失的步骤：

证据提取→能力聚类→技能汇编

SkillForge

English | 简体中文

SkillForge is a repository distillation workbench that turns messy project materials into reusable AI skill packages.

It scans local folders, parses mixed document formats, extracts workflow evidence, clusters that evidence into capabilities, and compiles the result into previewable and exportable skill outputs through a built-in web UI.

Why SkillForge?

Teams already have the raw knowledge they need: product docs, design notes, research reports, runbooks, SOPs, meeting notes, API references, and random markdown files scattered across repositories and shared folders.

The hard part is turning that fragmented, implicit knowledge into something reusable by an AI agent.

Doing that manually is slow and brittle:

• one-off scripts are fast to start but hard to maintain
• generic agent frameworks help orchestration, but they do not automatically distill repository knowledge into skill packages
• manual copy/paste into prompts does not scale across teams or projects

SkillForge focuses on that missing step:

evidence extraction → capability clustering → skill compilation

So instead of just “running an agent,” you can systematically convert repository knowledge into reusable skill artifacts.

What makes it different?

Compared with ad-hoc scripts

Ad-hoc scripts are useful for a single extraction pass, but they usually stop at file parsing or keyword search.

SkillForge gives you:

• a repeatable end-to-end pipeline
• structured job tracking
• a visual workflow dashboard
• preview and export for generated skill outputs

Compared with LangChain / DSPy / custom agent stacks

Those tools are great for orchestration, prompting, and model workflows. SkillForge solves a narrower but very practical problem: turning a real-world repository into reusable skill packages with a local-first review workflow.

In short:

• LangChain / DSPy help you build agent systems
• SkillForge helps you distill repository knowledge into agent-usable skills

They are complementary, not mutually exclusive.

Core use cases

• Distill internal documentation into reusable team skills
• Convert research folders into structured analyst workflows
• Extract SOPs and process knowledge from operations repositories
• Turn API docs and implementation notes into integration-oriented skills
• Build a reviewable bridge between messy source materials and AI-ready skill packages

Product walkthrough

SkillForge is designed as a visual, local-first workflow.

Visual workflow