한국어 |

SkillForge는 재사용 가능한 AI 기술 패키지로 메시 프로젝트 자료를 회전시키는 저장소 증류 작업대입니다.

그것은 로컬 폴더를 스캔, 혼합 문서 형식을 파, 작업 흐름 증거를 추출, 기능에 증거 클러스터, 내장 웹 UI를 통해 미리보기 및 내보내기 가능한 기술 출력으로 결과를 컴파일.

왜 SkillForge?

팀은 이미 필요한 원시 지식을 가지고 있습니다. 제품 문서, 디자인 노트, 연구 보고서, runbooks, SOPs, 회의 노트, API 참조 및 임의 마크 다운 파일 저장소 및 공유 폴더에 걸쳐 분산.

하드 부분은 파편, AI 에이전트에 의해 재사용 할 수있는 무언가에 implicit 지식.

수동으로 느리고 브리틀을 수행 :

• 원오프 스크립트는 빠른 시작하지만 유지하기 어렵다
• 일반적인 대리인 기구는 관현을 돕습니다, 그러나 그들은 기술 포장에 자동적으로 증류 저장소 지식이 아닙니다
• 프롬프트에 수동 복사/약은 팀 또는 프로젝트의 맞지 않습니다

SkillForge는 그 누락 단계에 초점을 맞추고:

증거 추출 → 기능 클러스터링 → 기술 컴파일

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