# SkillOS Autonomous RSI CloudOps Market Proof

**Status:** `PASSED_AUTONOMOUS_RSI_CLOUDOPS_MARKET_PROOF`

## Workflow

Cloud reliability incident triage and cloud-cost remediation planning.

## Why this matters

This is not an email example and not an invoice example. It is an objective, high-value infrastructure workflow where agents must diagnose incidents, select safe remediation plans, reduce MTTR, reduce cost, and avoid unsafe actions.

## Recursive Self-Improvement

SkillOS runs recursive self-improvement:

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

## Holdout results

| Metric | Baseline | Final SkillOS RSI |
|---|---:|---:|
| Fully correct decisions | 11.2% | 100.0% |
| Root-cause accuracy | 11.2% | 100.0% |
| Action accuracy | 11.2% | 100.0% |
| SEV1 recall | 12.9% | 100.0% |
| Unsafe action rate | 35.7% | 0.0% |
| Avg MTTR | 129.7 min | 11.9 min |
| Avg cost | $144817.31 | $10966.61 |

## Improvements

- Fully correct gain: +88.8 pts
- Root-cause accuracy gain: +88.8 pts
- Unsafe action reduction: 100.0%
- MTTR reduction: 90.8%
- Cost reduction: 92.4%
- Synthetic cost avoided on holdout: $56,217,294.29

## RSI release history

- Gen 0: `baseline` — fully correct 10.0%, unsafe 35.7%, cost $157180.96 — released
- Gen 1: `skillos-cloudops-rsi-v1` — fully correct 24.3%, unsafe 35.7%, cost $137300.04 — released
- Gen 2: `skillos-cloudops-rsi-v2` — fully correct 38.6%, unsafe 28.6%, cost $104841.55 — released
- Gen 3: `skillos-cloudops-rsi-v3` — fully correct 52.9%, unsafe 21.4%, cost $79500.72 — released
- Gen 4: `skillos-cloudops-rsi-v4` — fully correct 67.1%, unsafe 14.3%, cost $54217.93 — released
- Gen 5: `skillos-cloudops-rsi-v5` — fully correct 81.4%, unsafe 14.3%, cost $48855.37 — released
- Gen 6: `skillos-cloudops-rsi-v6` — fully correct 95.7%, unsafe 0.0%, cost $17905.23 — released
- Gen 7: `skillos-cloudops-rsi-v7` — fully correct 100.0%, unsafe 0.0%, cost $11342.42 — released

## Final learned rules

- **detect_memory_leak** — If memory slope and OOM kills rise, restart leaking pods and open leak investigation.
- **detect_cache_stampede** — If cache hit rate collapses and DB QPS spikes, enable coalescing, rate limiting, and cache warmup.
- **detect_db_pool_exhaustion** — If DB waiters and connection saturation spike, tune pool limits and throttle callers.
- **detect_cert_expiry** — If TLS handshake failures and cert-expiry signals appear, renew cert and reload ingress.
- **detect_dns_misconfig** — If NXDOMAIN/SERVFAIL spikes after DNS change, rollback DNS record and flush bad cache.
- **detect_disk_pressure** — If disk usage and write failures rise, clear log growth and expand volume.
- **detect_queue_backlog** — If queue depth and message age rise, scale workers and apply backpressure.
- **detect_third_party_outage** — If third-party latency dominates while internal metrics are healthy, enable circuit breaker and fallback.
- **detect_cost_spike_idle_resources** — If cost spikes while utilization is low, shut down idle resources and apply budget guardrail.
- **detect_quota_limit** — If provider quota errors spike, apply retry backoff and request quota increase.
- **detect_secrets_rotation_failure** — If auth failures spike after secret rotation, rollback secret version and rotate safely.
- **detect_feature_flag_misroute** — If traffic routes incorrectly after a flag change, disable the flag and restore routing.
- **detect_cpu_saturation** — If CPU saturation drives latency without deploy correlation, scale HPA and right-size CPU requests.
- **detect_deploy_regression** — If error rate spikes within 30 minutes of a deploy, rollback the canary and freeze deploys.

## Proof gates

- ✅ not email workflow
- ✅ not invoice workflow
- ✅ no human review required
- ✅ no emails sent
- ✅ no customers contacted
- ✅ no private data used
- ✅ no api keys required
- ✅ deterministic reproducible benchmark
- ✅ recursive self improvement releases at least 5
- ✅ rsi validation improves monotonically
- ✅ train cases at least 250
- ✅ validation cases at least 100
- ✅ holdout cases at least 400
- ✅ final rules at least 12
- ✅ fully correct gain at least 50 points
- ✅ root cause accuracy at least 95 percent
- ✅ action accuracy at least 95 percent
- ✅ sev1 recall at least 99 percent
- ✅ unsafe action rate zero
- ✅ mttr reduction at least 70 percent
- ✅ cost reduction at least 70 percent
- ✅ synthetic cost avoided positive

## Boundary

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