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MontrealAI / skillos

# the work-compounding layer for self-improving AI agents.

SkillOS now proves one concrete reference workflow gets cheaper, faster, and better as agents do the work and repeated corrections become tested skill releases.

See the proof View GitHub Actions
Target live site
`https://montrealai.github.io/skillos/`

GitHub Actions runs tests, generates `data/wealth_proof.json`, and publishes this dashboard automatically.

Cheaper
Faster
Better

Workflow proof

Loading…Sales follow-up from call notes

Autonomous no-send proof

## Shadow Pilot Proof

GitHub Actions now runs a no-send reference evaluation that shows SkillOS can turn call-note examples into traces, lessons, tested skill rules, and improved holdout performance.

No emails sent. No customers contacted. No private data. No API keys. Anyone can rerun the proof in GitHub Actions.

View visual proof Run on GitHub
+29.8 pts quality gain
64.1%edit-time reduction
+80.0 pts accepted-rate lift
0%hallucination rate after learned skill
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cost per job reduction

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time per job reduction

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quality gain

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projected annual savings under demo assumptions

reference workflow proof

## One reference workflow improves across tested skill releases.

The repository generates this proof during the GitHub Actions build. It runs a sales follow-up workflow, captures traces, converts repeated corrections into candidate skills, evaluates each candidate, releases approved versions, and checks that unit economics improve monotonically.

### Before → After

Checking…

### Plain-English result

Loading generated proof…

### Skill versions: cheaper, faster, better

Version | Quality | Minutes/job | Cost/job | Accepted | Learned rules
--- | --- | --- | --- | --- | ---

How the proof works

## Work becomes traces. Traces become skills. Skills improve future work.

1. Work

   Agent drafts sales follow-up emails.

2. Trace

   SkillOS records outputs, quality, time, cost, and human corrections.

3. Learn

   Repeated corrections become lessons.

4. Skill

   Each lesson becomes a bounded skill update.

5. Test

   The candidate must improve quality while reducing time and cost.

6. Release

   Only approved versions become current.

### Approved releases

View generated proof JSON summary

    Loading generated proof snapshot…

For GitHub web users

## Upload it, click Pages, wait for green checks.

No terminal is required.

1

### Create

Create a public repository named skillos under MontrealAI.

2

### Upload

Upload everything inside UPLOAD_THE_CONTENTS_OF_THIS_FOLDER_TO_GITHUB.

3

### Enable Pages

Go to Settings → Pages and choose GitHub Actions.

4

### Open

Visit montrealai.github.io/skillos after the deploy check turns green.

What green means:

The Action verified the repo, ran unit tests, ran the reference workflow proof, generated `data/wealth_proof.json`, and deployed the dashboard.

Reference implementation

## The proof is code, not just copy.

### Agent Runtime

Runs jobs and creates traces.

### Trace Store

Stores outputs, feedback, scores, tools, and skill usage.

### Learning Engine

Finds reusable lessons.

### Skill Trainer

Creates bounded skill edits.

### reference workflow proof

Checks quality rises while time and cost fall.

### Release Center

Publishes approved skill upgrades.

> “Work stops being disposable and becomes capital.”