# SkillOS Autonomous RSI Metamaterials Discovery Market-Readiness Proof

**Status:** `PASSED_AUTONOMOUS_RSI_METAMATERIALS_DISCOVERY_MARKET_PROOF`

## Workflow

Autonomous lightweight metamaterial discovery for load-bearing structures.

## Why this matters

This is not an email, invoice, CloudOps, cyber defense, or silicon verification example. It is an autonomous scientific/engineering discovery workflow: generate candidate designs, evaluate them, learn from failures, release better design skills, and improve recursively.

## Recursive Self-Improvement

SkillOS runs recursive self-improvement:

design failures → lessons → candidate design skills → validation → released skill versions → holdout discovery proof

## Holdout results

| Metric | Baseline | Final SkillOS RSI |
|---|---:|---:|
| Feasible design rate | 0.0% | 100.0% |
| Average score | 1.09 | 100.0 |
| Average material efficiency | 0.89 | 3.504 |
| Manufacturing failure rate | 100.0% | 0.0% |
| Average design days | 15.94 | 0.7 |
| Average cost | $768315.46 | $33787.2 |
| Pareto candidates | 1 | 2 |

## Improvements

- Feasible-rate gain: +100.0 pts
- Average score gain: +98.91 pts
- Material-efficiency gain: +2.614
- Manufacturing-failure reduction: 100.0%
- Design-time reduction: 95.6%
- Cost reduction: 95.6%
- New Pareto frontier candidates: 1
- Synthetic design cost avoided on holdout: $617,003,742.69

## RSI release history

- Gen 0: `baseline` — feasible 0.0%, score 0.94, cost $789015.33 — released
- Gen 1: `skillos-metamaterials-rsi-v1` — feasible 0.0%, score 32.82, cost $621025.19 — released
- Gen 2: `skillos-metamaterials-rsi-v2` — feasible 2.4%, score 46.7, cost $503560.47 — released
- Gen 3: `skillos-metamaterials-rsi-v3` — feasible 11.4%, score 57.31, cost $436114.84 — released
- Gen 4: `skillos-metamaterials-rsi-v4` — feasible 37.1%, score 73.95, cost $302068.19 — released
- Gen 5: `skillos-metamaterials-rsi-v5` — feasible 77.1%, score 90.66, cost $130976.33 — released
- Gen 6: `skillos-metamaterials-rsi-v6` — feasible 100.0%, score 100.0, cost $34586.2 — released

## Final learned discovery skills

- **skill_load_path_topology** — Select lattice topology from load mode instead of using a generic grid.
- **skill_manufacturing_constraints** — Respect minimum feature size, overhang, and node-count manufacturability.
- **skill_hierarchical_stiffness** — Use hierarchical subcells when stiffness target is high under mass constraint.
- **skill_graded_density_load_paths** — Move density toward high-stress load paths and remove density elsewhere.
- **skill_mass_pruning** — Prune mass in low-stress regions once feasibility is achieved.
- **skill_pareto_frontier_selection** — Select candidates by constrained Pareto score, not a single naive metric.
- **skill_buckling_safety_margin** — Raise slenderness safety on compression and torsion cases.
- **skill_triangular_compression_tension** — Use triangular or octet truss families for axial compression and tension.
- **skill_cross_braced_shear_torsion** — Add cross-bracing and closed loops for shear and torsion.
- **skill_thermal_channel_separation** — Reserve aligned thermal channels while preserving load paths.
- **skill_gyroid_multiaxis_isotropy** — Use gyroid-like continuous lattices for multi-axis isotropy.
- **skill_auxetic_impact_absorption** — Use auxetic cells for impact-energy absorption.

## Proof gates

- ✅ not email workflow
- ✅ not invoice workflow
- ✅ not cloudops workflow
- ✅ not cyberdefense workflow
- ✅ not silicon verification workflow
- ✅ scientific discovery 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 6
- ✅ rsi validation improves monotonically
- ✅ train cases at least 400
- ✅ validation cases at least 200
- ✅ holdout cases at least 800
- ✅ final skills at least 12
- ✅ feasible rate gain at least 90 points
- ✅ final feasible rate at least 98 percent
- ✅ manufacturing failure rate zero
- ✅ design time reduction at least 85 percent
- ✅ cost reduction at least 85 percent
- ✅ new pareto candidates positive
- ✅ synthetic cost avoided positive

## Boundary

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