Building Agent Skills from Scratch: A Retrospective on 8 Open-Source Projects
2025/04/12

Building Agent Skills from Scratch: A Retrospective on 8 Open-Source Projects

An independent developer's journey building 8 AI Agent tools — from idea to release

Building Agent Skills from Scratch: A Retrospective on 8 Open-Source Projects

Over the past few months, I built 8 open-source projects: 3 Agent Skills + 5 CLI tools. This post is a retrospective on what worked, what didn't, and what I learned.

Why Build These Tools

One word: necessity.

I'm a tech blogger who publishes weekly on WeChat Official Account. The publishing workflow used to take 30-60 minutes of manual formatting. So I built md2wechat — one command to convert Markdown to WeChat-formatted HTML and push it as a draft.

That started a chain reaction.

The 8 Projects

  • md2wechat — WeChat formatting toolkit (Skill + Go CLI)
  • agora — Multi-perspective deliberation system with 31 thinkers
  • gcli — Gmail read-only CLI with OAuth PKCE
  • imgcli — Zero-CGO image processing CLI
  • any2card — Text-to-shareable HTML card converter
  • interactive-learning — Socratic learning path generator
  • jina-cli — Web-to-Markdown reader for AI agents
  • md2wechat-lite — Lightweight Go CLI version of md2wechat

What Went Right

1. One-line install is non-negotiable

If installation takes more than 3 steps, most users give up. My standard: the first code block in README is the install command.

2. Build for yourself first

7 out of 8 projects solved my own problems first. Tools you don't use yourself are hard to maintain.

3. Go's distribution advantage is real

Single binary, no runtime needed. curl | bash and you're done.

What Went Wrong

1. Documentation is never enough

The most common issue after launch wasn't bugs — it was "how do I use this?"

2. Too many themes too early

md2wechat has 38+ themes. 30 of them are rarely used. Should have started with 3 great ones.

3. No automated testing for Skills

Agent Skill output is AI-generated and varies each time. But parsing logic and output format should still be tested.

What's Next

  1. Commercialization — Turning mature Skills into paid services
  2. Community-driven — Let users vote on feature priorities
  3. Tutorials — Helping more developers build their own Agent Skills

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I'm GeekJourney, an independent developer building tools for AI agents.