# SkillOS Fully Autonomous Market-Readiness Proof

**Status:** `PASSED_AUTONOMOUS_MARKET_READINESS_PROOF`

This proof is 100% autonomous. It uses a deterministic synthetic/redacted-style benchmark and runs entirely in GitHub Actions.

## What it proves

It proves that SkillOS can autonomously learn reusable skill rules from repeated correction patterns and improve holdout workflow examples without sending emails, contacting customers, using private data, calling APIs, or requiring human review.

## What it does not prove

It is not audited customer ROI, live customer market proof, investment advice, financial advice, or a guarantee of future outcomes.

## Results

| Metric | Baseline | SkillOS |
|---|---:|---:|
| Quality score | 49.2 | 95 |
| Accepted rate | 0.0% | 100.0% |
| Edit minutes/job | 6.09 | 0.8 |
| Cost/job | $7.6 | $1.0 |
| Hallucination rate | 15.0% | 0.0% |

## Improvements

- Quality gain: +45.8 pts
- Accepted-rate lift: +100.0 pts
- Edit-time reduction: 86.9%
- Cost reduction: 86.8%
- Hallucination reduction: 100.0%

## Learned rules

- Put the agreed next step in the first three lines.
- Include the buyer's main pain in the recap.
- Never add commitments, approvals, budgets, or launch dates not present in the call notes.
- Replace generic filler with a concise, specific recap.
- End with a clear confirmation question tied to the next step.
- Keep the draft under 140 words unless the user asks for more detail.

## Gates

- ✅ no human review required
- ✅ no emails sent
- ✅ no customers contacted
- ✅ no private data used
- ✅ no api keys required
- ✅ deterministic reproducible benchmark
- ✅ train examples at least 20
- ✅ holdout examples at least 50
- ✅ learned rules created
- ✅ quality gain at least 20 points
- ✅ accepted rate lift at least 50 points
- ✅ edit time reduction at least 50 percent
- ✅ cost reduction at least 50 percent
- ✅ hallucination rate after skillos is zero
