# SkillOS Autonomous RSI Revenue Experiment Factory Market-Readiness Proof

**Status:** `PASSED_AUTONOMOUS_RSI_REVENUE_EXPERIMENT_FACTORY_MARKET_PROOF`

## Workflow

Revenue experiment design, causal validation, portfolio allocation, and scalable growth-plan selection.

## Why this matters

This is a highly scalable business-domain proof for an AI-native experiment factory. It is not an email example, invoice example, CloudOps example, cyber defense example, silicon example, metamaterials example, generic corporate OS example, unit-economics profit-engine example, or marketplace-flywheel example.

The system must select the right growth experiment, intervention, guardrail, and priority under realistic business failure modes: saturation, cannibalization, underpowered tests, novelty effects, segment heterogeneity, margin risk, lagged retention, and scalable growth opportunities.

## Recursive Self-Improvement

SkillOS runs recursive self-improvement:

training failures → lessons → candidate causal-experiment rules → validation → released skill versions → holdout proof

## Holdout results

| Metric | Baseline | Final SkillOS RSI |
|---|---:|---:|
| Fully correct decisions | 6.1% | 100.0% |
| Growth-state accuracy | 6.1% | 100.0% |
| Experiment-design accuracy | 6.1% | 100.0% |
| Intervention accuracy | 6.1% | 100.0% |
| Guardrail accuracy | 6.1% | 100.0% |
| Value capture rate | 1.4% | 82.4% |
| Causal confidence score | 11.6 | 96 |
| Material miss rate | 60.5% | 0.0% |
| False positive rate | 27.9% | 0.0% |
| Avg experiment cycle | 31.674 days | 0.386 days |

## Improvements

- Fully correct gain: +93.9 pts
- Growth-state accuracy gain: +93.9 pts
- Value capture gain: +81.0 pts
- Causal confidence gain: +84.4 pts
- Material miss reduction: 100.0%
- False-positive reduction: 100.0%
- Experiment-cycle reduction: 98.8%
- Synthetic annual value captured on holdout: $17,063,901,894.23

## RSI release history

- Gen 0: `baseline` — fully correct 6.7%, value capture 1.9%, causal confidence 12.1, false positive 27.1% — released
- Gen 1: `skillos-revenue-experiment-factory-rsi-v1` — fully correct 17.9%, value capture 15.2%, causal confidence 22.3, false positive 27.1% — released
- Gen 2: `skillos-revenue-experiment-factory-rsi-v2` — fully correct 29.2%, value capture 29.3%, causal confidence 32.4, false positive 27.1% — released
- Gen 3: `skillos-revenue-experiment-factory-rsi-v3` — fully correct 40.4%, value capture 39.5%, causal confidence 42.8, false positive 21.7% — released
- Gen 4: `skillos-revenue-experiment-factory-rsi-v4` — fully correct 51.7%, value capture 54.1%, causal confidence 53.3, false positive 16.2% — released
- Gen 5: `skillos-revenue-experiment-factory-rsi-v5` — fully correct 62.5%, value capture 70.4%, causal confidence 63.4, false positive 10.8% — released
- Gen 6: `skillos-revenue-experiment-factory-rsi-v6` — fully correct 72.5%, value capture 76.6%, causal confidence 72.7, false positive 5.4% — released
- Gen 7: `skillos-revenue-experiment-factory-rsi-v7` — fully correct 83.3%, value capture 79.1%, causal confidence 82.3, false positive 0.0% — released
- Gen 8: `skillos-revenue-experiment-factory-rsi-v8` — fully correct 94.6%, value capture 81.4%, causal confidence 91.6, false positive 0.0% — released
- Gen 9: `skillos-revenue-experiment-factory-rsi-v9` — fully correct 100.0%, value capture 82.3%, causal confidence 96, false positive 0.0% — released
- Gen 10: `skillos-revenue-experiment-factory-rsi-v10` — fully correct 100.0%, value capture 82.3%, causal confidence 96, false positive 0.0% — released

## Final learned revenue-experiment skills

- **skill_price_elasticity** — Detect pricing upside and run a segmented elasticity test before a broad price change.
- **skill_activation_bottleneck** — Detect onboarding activation bottlenecks and fix the first-value moment before scaling acquisition.
- **skill_sales_cycle_compression** — Detect enterprise sales-cycle friction and compress the cycle with reusable evidence packs.
- **skill_lifecycle_retention** — Detect retention lift from lifecycle nudges and validate with randomized holdouts.
- **skill_cannibalization_guard** — Detect cannibalization risk and pause gross-booking campaigns until net-new incrementality is proven.
- **skill_segment_heterogeneity** — Detect hidden segment winners and avoid losing gains in averaged results.
- **skill_lagged_retention** — Detect lagging retention metrics and validate proxy metrics before scaling.
- **skill_cross_sell_sequence** — Detect cross-sell sequence opportunities and trigger only after activation milestones.
- **skill_paid_saturation** — Detect paid-channel saturation and stop spending past the marginal return frontier.
- **skill_margin_guardrail** — Detect growth that destroys margin and block rollout until profit guardrails pass.
- **skill_high_intent_channel** — Detect underallocated high-intent demand and scale it only with incrementality and CAC-payback guardrails.
- **skill_power_analysis** — Detect underpowered tests and redesign sample size before deciding.
- **skill_novelty_effect** — Detect novelty-effect false positives and require delayed readout before rollout.
- **skill_referral_network** — Detect referral network potential and seed it in high-trust cohorts with fraud guardrails.
- **skill_seasonal_window** — Detect seasonal demand windows and validate lift with geo holdouts.
- **skill_trial_abuse** — Detect free-trial abuse and add verification without harming legitimate conversion.
- **skill_seo_compounding** — Detect compounding SEO opportunities and fund topic clusters tied to qualified pipeline.
- **skill_clean_hold** — Recognize clean growth states and avoid unnecessary experiments.

## Proof gates

- ✅ business domain scalable revenue experiment factory workflow
- ✅ not email workflow
- ✅ not invoice workflow
- ✅ not cloudops workflow
- ✅ not cyberdefense workflow
- ✅ not silicon workflow
- ✅ not metamaterials workflow
- ✅ not generic corporate os workflow
- ✅ not unit economics profit engine workflow
- ✅ not marketplace flywheel workflow
- ✅ no human review required
- ✅ no customers contacted
- ✅ no private data used
- ✅ no api keys required
- ✅ deterministic reproducible benchmark
- ✅ recursive self improvement releases at least 10
- ✅ rsi validation improves monotonically
- ✅ train cases at least 480
- ✅ validation cases at least 240
- ✅ holdout cases at least 960
- ✅ final rules at least 18
- ✅ fully correct gain at least 85 points
- ✅ growth state accuracy at least 99 percent
- ✅ experiment design accuracy at least 99 percent
- ✅ intervention accuracy at least 99 percent
- ✅ guardrail accuracy at least 99 percent
- ✅ value capture rate at least 75 percent
- ✅ causal confidence score at least 90
- ✅ material miss rate zero
- ✅ false positive rate zero
- ✅ experiment cycle reduction at least 90 percent
- ✅ synthetic value captured positive

## Boundary

This is a 100% autonomous reference workflow proof using deterministic synthetic/redacted-style business data and benchmark assumptions. It is not audited customer ROI, live customer market proof, financial advice, investment advice, or a guarantee of future outcomes.
