rebelytics/one-skill-to-rule-them-all

# GitHub Repository: rebelytics/one-skill-to-rule-them-all

**URL:** https://github.com/rebelytics/one-skill-to-rule-them-all
**Author:** rebelytics
**Description:** The meta-skill that builds and improves all your skills, including itself. Watches your work sessions, captures corrections and judgement calls, and turns them into skill improvements automatically. Practical application of the Augmented Expertise methodology. Open-source, domain-agnostic, CC BY 4.0.
**Homepage:** https://www.rebelytics.com/task-observer/
**Language:** 

## Stats
- Stars: 703
- Forks: 77
- Open Issues: 3
- Commits: 21
- Created: 2026-02-15T19:18:12Z
- Updated: 2026-06-18T15:08:36Z
- Pushed: 2026-05-24T09:52:26Z

## README
# task-observer - One Skill to Rule Them All

## The meta-skill that builds and improves all your skills, including itself.

In the first three months of using this meta-skill, it **logged and applied over 600 improvements across my 40 skills**, most of which were themselves created based on observations by the meta-skill.

This meta-skill, called "task-observer", is a practical application of the [Augmented Expertise](https://www.rebelytics.com/augmented-expertise/) methodology, an AI framework for knowledge workers. However, users have reported successful integrations into their Hermes and Openclaw setups, so it works equally well with autonomous agents.

## Why you should use this meta-skill

Creating skills is powerful but time-consuming. But the skills that do get built stay frozen: they never learn from how you actually use them.

Task Observer fixes those problems. It's a meta-skill that runs alongside your work, watches what you do, and does two things:

1. **Creates new skills for you** — it spots repeating patterns in your work and drafts skill candidates automatically, so you get skills without the upfront effort of writing them from scratch
2. **Improves your existing skills** — it notices corrections you make, preferences you express, and gaps in your current skills, then suggests specific updates

You work normally. It watches. Your skill library grows and gets better over time.

## The self-improving part

This is the detail that makes the task observer truly beautiful in my opinion. Because it runs during every session and observes all active skills — including itself — it captures improvements to its own methodology over time.

If it misses something, or if its observation format could be clearer, or if it's triggering in contexts where it shouldn't — it notices, and it logs that too. The skill that improves all your skills also improves itself.

## What it does

Task Observer monitors your work sessions and looks for three things:

1. **Corrections and adjustments** — if you adjust the AI's output or steer it in a different direction, that's a signal that a skill could be clearer or more complete
2. **Gaps no skill covers yet** — if you're doing something manually that could be systematised, the observer flags it as a candidate for a new skill
3. **Its own blind spots** — the observer watches itself too, capturing improvements to its own methodology as you use it

During each session, it produces a structured observation log: what it noticed, which skills are affected, and specific suggested improvements. You review, approve, and your skills evolve.

Some observations reveal patterns that aren't specific to one skill. These get captured as **cross-cutting principles** in a separate log — and new skills are automatically checked against them whenever they're created or updated. The more you use the system, the higher the quality floor across your whole skill library.

The observer doesn't modify your skills directly. It produces recommendations that you review. You stay in control of what changes and when.

## Who it's for

You don't need to be a developer. If you use skills in any capacity and you want those skills to get better over time instead of staying frozen, this is for you.

If you're a builder, you can easily integrate this skill, or even just the methodology, into your existing setup. Just point your agent at the repo and let it guide you towards the ideal implementation for your specific setup.

The task observer is particularly valuable if you've built multiple skills and want a systematic way to maintain and improve them without manually auditing each one. But it's equally useful if you don't have any skills yet: the observer will start identifying and drafting them for you.

## How it works

**The best way to get started with this work setup in any environment is probably to grab the skill, readme and user guide, feed them to your AI and let it guide you towards the best setup for your particular environment** - No matter which AI system you use. As long as skills are supported, you should be able to use this approach with some adjustments. And even without skills, the methodology should work with any other type of knowledge base that your AI has access to.

## Claude environment notes

**In Claude Cowork (including Dispatch) or Claude Code in the desktop app:** Full experience. The observer writes observation logs to your filesystem, so improvements persist between sessions and can be actioned easily. Observations land in `[your shared folder]/skill-observations/`; proposed skill updates land in `[your shared folder]/skill-updates/`. You don't normally need to look at these directly — Claude handles them — but they're there if you want to inspect what's been captured.

**In Claude.ai web or Claude Chat in the desktop app / mobile app:** Handoff doc mode. Since there's no filesystem access, the observer produces a structured handoff document at the end of your session that you can use to update your skills in a dedicated session.

## Compatibility

**Tested and designed for:**
- Claude Cowork (full experience with filesystem access)
- Claude Dispatch
- Claude.ai web interface (handoff doc mode)
- Claude mobile app (handoff doc mode)
- Claude Code in the desktop app

**Expected to work but untested:**
- Claude Code without desktop app — the methodology and format should translate directly, but I haven't verified it in practice - users have reported seamless experiences with this.

**Versions for other environments created by users:**
- Codex version by AllstarGER: [https://github.com/AllstarGER/one-skill-to-rule-them-all](https://github.com/AllstarGER/one-skill-to-rule-them-all)
- Please get in touch if you've open-sourced an adaptation of the meta-skill for another system or environment. I'm happy to include it here.

**Potentially compatible with caveats:**
- Other skills-compatible platforms (ChatGPT, Gemini CLI, Cursor, etc.) — the skill uses Claude-centric concepts like `<available_skills>` and skill-creator references that other systems would need to interpret or adapt. The SKILL.md format is cross-platform, but the content assumes Claude's architecture.
- Users have reported successful integrations into Openclaw and Hermes setups.

If you try it in another environment, please let me know how it goes. Issues and pull requests welcome.

## Quick start

1. Read the user guide at [https://github.com/rebelytics/one-skill-to-rule-them-all/blob/main/USER-GUIDE.md](https://github.com/rebelytics/one-skill-to-rule-them-all/blob/main/USER-GUIDE.md)
2. Give the content of this repo (skill, readme and user guide) to the AI system of your choice and let it guide you towards the ideal configuration for your individual setup.
3. Make sure that the skill loads in all sessions where it's needed (I solved this via an instruction in my CLAUDE.md file)
4. Try to remember to ask "Any observations logged?" when you finish a session (I do this every time I archive a session). Often, the skill then finds additional improvement potential that it didn't log before.
5. Schedule a recurring review session that applies all open observations. Mine runs Monday, Wednesday and Friday morning, but you should adapt this to your needs.

## Contributing

This is an open-source project for the community. If you use it, I would love to hear from you:

- **Bug reports and feature requests:** Open an issue
- **Platform compatibility reports:** Tried it somewhere other than Claude? Tell me what happened
- **Interesting use cases:** Have you come up with a creative way of using or improving the task observer?
- **Integrations with other systems:** One user told me that they connected task observer to Obsidian. Do you have a similar story?

## License

This work is licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

You're free to use, adapt, and redistribute — even commercially — as long as you give appropriate credit: Link to the original repo (https://github.com/rebelytics/one-skill-to-rule-them-all/) and name the author (Eoghan Henn / rebelytics.com).

## Further reading

If you want to learn more about the methodology behind this skill, please read the [Augmented Expertise manifesto](https://www.rebelytics.com/augmented-expertise/).

## Security audit

[![Oathe Security](https://img.shields.io/endpoint?url=https%3A%2F%2Faudit-engine.oathe.ai%2Fapi%2Fbadge%2Frebelytics%2Fone-skill-to-rule-them-all&style=for-the-badge&logo=data:image/svg%2Bxml;base64,PHN2ZyB4bWxucz0naHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmcnIHZpZXdCb3g9JzAgMCAyNCAyNCcgZmlsbD0nd2hpdGUnPjxwYXRoIGQ9J00xMiAyQzkuMjQgMiA3IDQuMjQgNyA3djNINmMtMS4xIDAtMiAuOS0yIDJ2OGMwIDEuMS45IDIgMiAyaDEyYzEuMSAwIDItLjkgMi0ydi04YzAtMS4xLS45LTItMi0yaC0xVjdjMC0yLjc2LTIuMjQtNS01LTV6bTMgMTBIOVY3YzAtMS42NiAxLjM0LTMgMy0zczMgMS4zNCAzIDN2M3onLz48L3N2Zz4=&labelColor=000000&cacheSeconds=3600)](https://oathe.ai/report/rebelytics/one-skill-to-rule-them-all)

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**Created by [Eoghan Henn](https://rebelytics.com)**