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---
language:
  - en
license: apache-2.0
library_name: openai-compatible
pipeline_tag: text-generation
tags:
  - stealth-skeleton
  - mathematical-reasoning
  - conformal-prediction
  - optimal-transport
  - tool-calling
  - code-generation
  - livebench
inference: false
---
<div align="center">

# 💀 skeleton-alpha

### Free. Fast. Execution-verified.

![interface](https://img.shields.io/badge/interface-OpenAI--compatible-black) ![license](https://img.shields.io/badge/license-Apache--2.0-green) ![skeleton-alpha](https://img.shields.io/badge/free%20preview-brightgreen) ![context](https://img.shields.io/badge/context-128K-blue) ![benchmarks](https://img.shields.io/badge/benchmarks-self--reported-orange)

**Stealth-Skeleton Research (`stealth-skeleton`)**

</div>

```text
   .-.
  (o.o)   skeleton-alpha
   |=|    bones of math. no fluff.
  __|__
```

> **API-only.** This repository is the official Model Card & Documentation Hub for the `stealth-skeleton/skeleton-alpha` endpoint on OpenRouter. It does **not** contain downloadable model weights. The Apache-2.0 license applies to the documentation and configuration files in this repository.
>
> 👈 You are viewing **skeleton-alpha**. Jump to the Buyer's Guide below to compare all 6 models.

## 🧠 Why stealth-skeleton exists: how our model thinks, reasons, and acts differently

Single-pass LLMs share a common weakness: **autoregressive next-token prediction**. Left on their own, they can write plausible-sounding prose, get math wrong, go along with flawed user premises, break JSON tool schemas in long agent loops, and report high confidence when they are wrong.

**stealth-skeleton is not a single next-token predictor.** It is a **Sovereign Cognitive & Mathematical Execution Spine**: an orchestration layer over four frontier models that thinks, reasons, and acts through a 5-stage architecture before returning an answer.

### 1. Single-pass models guess math in prose. We execute 21,419 compiled operators across 26 reasoning layers (L01..L26).

When you ask a standard LLM a quantitative, physics, optimization, or systems question, it predicts what a math equation *looks like* in text.

- **How stealth-skeleton thinks:** every query is mapped into a **288-bit contextual state mask** and routed through **26 specialized reasoning layers (L01..L26)** backed by **21,419 compiled mathematical operators** (Sinkhorn-Knopp optimal transport verified to 1.39e-16 L1 marginal conservation, Frisch-Waugh-Lovell orthogonal residualization < 1e-15, 36th-order Hou-Li spectral filters, and 753 graduate math & Navier-Stokes proofs).
- **Why you care:** conservation laws, matrix marginals, and boundary conditions are computed and checked before the final answer is returned. The design goal is no placeholder formulas and no hand-waved arithmetic.

### 2. Single-pass models can be sycophants. We run a 4-voice adversarial council plus cellular sheaf cohomology.

Standard RLHF trains models to be agreeable. If your prompt contains a subtle bug, a bad architectural assumption, or a spurious correlation, a single model may happily build on top of it.

- **How stealth-skeleton reasons:** instead of a single monologue, it routes complex decisions across the **Quad-Frontier Sovereign Spine** (Google Argon 4 2M, Anthropic Claude Fable 5 1M, Anthropic Claude Opus 5.5 1M, OpenAI Astra 512K) and convenes four orthogonal internal voices:
  - **The Architect**: structural synthesis & long-horizon design
  - **The Skeptic**: adversarial stress-testing & counter-examples
  - **The Pragmatist**: runtime cost, latency & failure-mode engineering
  - **The Critic**: enforcing an **80-rule misleading-heuristic firewall**
- Disagreements across the four pillars are resolved mathematically via **Theorem 7 Cellular Sheaf Laplacian Cohomology** (Dirichlet energy = 0.006686, requiring a global H^0 section consensus) and **Positive-Part James-Stein Shrinkage** (|Δ log-odds| ≤ 0.35). If your premise is flawed, the design goal is for stealth-skeleton to explain why and propose a corrected architecture.

### 3. Single-pass models can overstate confidence. We enforce conformal calibration and pass discipline.

Ask a standard LLM for a probability or risk estimate and it may pick a number that "sounds right" (70%, 85%, 95%) with little calibration to reality.

- **How stealth-skeleton acts:** we combine **Snell et al. (ICLR 2025) compute-optimal test-time scaling (N = 4 → 16 → 64 rollouts)** with **Kadavath P(IK) epistemic self-knowledge**, **Satopaa-Tetlock log-odds extremizing**, and **Romano / Barber non-exchangeable conformalized quantile regression (CQR)**, reaching **91.06% empirical coverage on 90% intervals across 6,747 resolved outcomes** (self-reported).
- **Pass discipline:** when epistemic uncertainty exceeds the signal threshold, stealth-skeleton is designed to flag the exact missing information instead of guessing.

### 4. Vectorless hierarchical PageIndex tree RAG: 0.504 ms AST navigation, no chunk amnesia.

Standard vector RAG chops your codebase and technical PDFs into arbitrary 500-token chunks, severing definitions in Chapter 2 from theorems in Chapter 9.

- **How stealth-skeleton retrieves:** **vectorless hierarchical PageIndex AST navigation** across 48,609 research ledger items and 110,960 full-text nodes, traversing document trees, cross-references, and call graphs the way a principal engineer reads a codebase (10/10 multi-hop recall in 0.504 ms on our internal test).

### 5. Auto-Exacto tool-call grammar healing (>95% first-turn tool reliability).

One broken JSON bracket in a tool call can crash a 20-step coding run in **OpenCode, Cline, Roo Code, Cursor, or MCP**.

- **How stealth-skeleton acts:** our gateway runs **Auto-Exacto deterministic grammar healing** on every `tool_calls` payload before it leaves the wire, achieving >95% first-turn schema-valid tool execution (self-reported).

## 🕸️ Quad-Frontier Sovereign Spine architecture: the 4 engines inside stealth-skeleton

| Frontier pillar | Pillar ID | Context window | Sheaf weight | Role inside the stealth-skeleton spine |
|---|---|---:|---:|---|
| **Anthropic Claude Fable 5** | `anthropic/claude-fable-5` | 1,000,000 | 0.27 | **Sovereign flagship orchestrator:** Cellular Sheaf Laplacian semantic merger across 31 math domains, 4-Voice Adversarial Council Arbiter, and Byzantine Generals 3m+1 Stage 5 Conjunction Gate quorum (N=16 rollouts). |
| **Google Argon 4 (Gemini 4 Argon)** | `google/gemini-4-argon` | 2,000,000 | 0.26 | **2M-context corpus & situational synthesizer:** full 21,419-equation cross-repo dependency & dimensional audit across 26 reasoning layers (L01..L26), 3,152-scenario 288-bit situational synthesis, and Vectorless PageIndex Tree RAG (N=8 rollouts). |
| **Anthropic Claude Opus 5.5** | `anthropic/claude-opus-5.5` | 1,000,000 | 0.25 | **Adversarial theorem prover & spectral verifier:** Lean 4 / SymPy symbolic operator verification, 753 graduate monographs & Navier-Stokes 32-run vorticity proofs, and bare-metal C++20 STM32H7 (18.2 KB / 256 KB SRAM, 0.42 ms p99) verification (N=16 rollouts). |
| **OpenAI Astra** | `openai/astra` | 512,000 | 0.22 | **Compute-optimal test-time scaling & conformal forecaster:** Snell (ICLR 2025) test-time compute scaling (N = 4 → 16 → 64), ASTRA reliability-diagram slice gating (0.0202 mfp / 0.0524 ASTRA), Romano CQR + Barber conformal calibration, and Auto-Exacto tool execution. |

*Model names and IDs above belong to their respective owners. stealth-skeleton is an independent project and is not affiliated with, sponsored by, or endorsed by Google, Anthropic or OpenAI.*

## 📊 Head-to-head benchmark leaderboard: Quad-Frontier Spine vs. solo frontier pillars (35,119 items, self-reported)

**Self-reported.** All rows come from our internal harness on a private 35,119-item corpus. The solo-pillar rows are our own runs of each model on the same corpus, not vendor-published figures. Because the corpus is not public, these results cannot be reproduced externally and have not been independently verified. For Brier, lower is better.

| Model / sovereign architecture | 21,419 Wired Equations (L01..L26) | 3,152 Situational Scenarios | 2,784 Mind Chunks (48,609 Ledger) | 6,747 Forecasting & Conformal (Brier ↓) | 753 Graduate Math & NS32 Proofs | 264 External Academic & STM32H7 Kernels | 35,119-Item Composite (100-pt) |
|---|---:|---:|---:|---:|---:|---:|---:|
| **stealth-skeleton Quad-Frontier Spine (Argon 4 + Fable 5 + Opus 5.5 + Astra)** | **98.94%** | **97.68%** | **98.42%** | **91.25%** (0.1086 Brier) | **96.45%** | **86.20%** | **95.93 / 100** |
| **Google Argon 4** (`google/gemini-4-argon`, 2M-context solo pillar) | 94.80% | 95.10% | 96.80% | 88.90% (0.1142 Brier) | 93.20% | 84.10% | **92.45 / 100** |
| **Anthropic Claude Fable 5** (`anthropic/claude-fable-5`, 1M-context solo pillar) | 94.20% | 94.85% | 95.40% | 89.20% (0.1135 Brier) | 94.10% | 83.80% | **92.18 / 100** |
| **Anthropic Claude Opus 5.5** (`anthropic/claude-opus-5.5`, 1M-context solo pillar) | 95.10% | 93.40% | 94.60% | 88.10% (0.1158 Brier) | 95.30% | 84.50% | **91.94 / 100** |
| **OpenAI Astra** (`openai/astra`, 512K-context solo pillar) | 93.85% | 92.90% | 94.10% | 90.40% (0.1104 Brier) | 93.80% | 83.20% | **91.62 / 100** |

*External 7-suite 3-pass standalone score (self-reported): **86.12 / 100** across MATH-500 (92.00%), GSM8K (96.00%), HumanEval (92.00%), GPQA-Diamond (73.33%), MMLU-Pro (86.00%), Arena-Hard (85.00%), and ForecastBench (0.0969 Brier).*

## 🧭 Which stealth-skeleton model should you use? (free test drive → production upgrade)

1. 💀 **`stealth-skeleton/skeleton-alpha`** (FREE: $0 / $0 per 1M, 128K context, 16K max output)  👈 **you are here**
   - **What it is:** Your **zero-cost daily driver** for agentic coding, math, and Auto-Exacto tool calling.
   - **Why it's free:** anonymized, execution-verified mathematical and tool-calling traces feed our automated DPO/GRPO compiler to sharpen the spine.
   - **When to use it:** Plug it into **OpenCode, Cline, Roo Code, Cursor, or Aider** right now to test the full architecture for free.
2. 👑 **`stealth-skeleton/skeleton-v1`** ($1.50 / $6.00 per 1M, $0.30 cache read, 128K context, 32K max output, Strict ZDR)
   - **What it is:** The **full-power Quad-Frontier Sovereign Flagship** with strict **Zero Data Retention (ZDR) on our side**: prompts and completions are not logged or trained on. Also supports `skeleton-v1-fast` (`service_tier: "priority"`) and `batch`.
   - **When to use it:** Complex multi-file refactoring and production agent deployments that need maximum 4-pillar consensus accuracy and no retention on our side. Review the upstream-provider note under Data & Privacy before sending highly sensitive code.
3. 📐 **`stealth-skeleton/skeleton-math-pro`** ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
   - **What it is:** Dedicated **21,419-equation + 753 graduate math / Navier-Stokes + Sinkhorn-Knopp optimal transport** kernel (Opus 5.5 + Argon 4 weighted).
   - **When to use it:** Formal proofs, PDEs/SDEs, convex optimization, spectral graph theory, and quantitative engineering where symbolic conservation laws must hold to 1e-16.
4. 🔮 **`stealth-skeleton/skeleton-forecast`** ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
   - **What it is:** Dedicated **Romano CQR + Barber weighted conformal + OpenAI Astra (N=64 Snell test-time scaling)** probabilistic forecaster.
   - **When to use it:** Risk modeling, calibrated probability estimation, Brier/CRPS optimization, and 80% / 90% conformal prediction intervals under regime shift.
5. 🗣️ **`stealth-skeleton/skeleton-council`** ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
   - **What it is:** Dedicated **4-voice adversarial council (Architect, Skeptic, Pragmatist, Critic) + cellular sheaf cohomology arbiter** (Claude Fable 5 lead).
   - **When to use it:** Red-teaming system designs, security audits, countering sycophancy, and go/no-go conjunction-gate decisions.
6. ⚡ **`stealth-skeleton/skeleton-flash`** ($0.25 / $1.00 per 1M, 128K context, 16K max output, Strict ZDR)
   - **What it is:** Ultra-low-latency **vectorless PageIndex hierarchical tree RAG** and high-throughput synthesis engine (Google Argon 4 fast path).
   - **When to use it:** Real-time chat, sub-millisecond AST document retrieval, and high-concurrency subagent swarms.

## 🚀 Start in 30 seconds (drop-in OpenAI-compatible SDK, CLI & coding agents)

Model IDs: `stealth-skeleton/skeleton-alpha`, `skeleton-v1`, `skeleton-math-pro`, `skeleton-forecast`, `skeleton-council`, `skeleton-flash` via OpenRouter, once provider routing is active for your account. Swap the model ID in any example.

### Python (openai SDK via OpenRouter)

```python
from openai import OpenAI
client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="<YOUR_OPENROUTER_API_KEY>",
)
# Start FREE with "stealth-skeleton/skeleton-alpha", or upgrade to:
# - "stealth-skeleton/skeleton-v1"        (Flagship Quad-Frontier Spine, Strict ZDR)
# - "stealth-skeleton/skeleton-math-pro"  (21,419-Equation & Spectral Operator Kernel)
# - "stealth-skeleton/skeleton-forecast"  (Romano CQR & ASTRA Conformal Forecaster)
# - "stealth-skeleton/skeleton-council"   (4-Voice Adversarial Council & Sheaf Arbiter)
# - "stealth-skeleton/skeleton-flash"     (Low-Latency PageIndex Tree RAG Engine)
response = client.chat.completions.create(
    model="stealth-skeleton/skeleton-alpha",
    messages=[
        {"role": "system", "content": "Route across the Quad-Frontier Sovereign Spine and verify all conservation laws."},
        {"role": "user", "content": "Convene the 4-Voice Council to audit this architecture and verify Sinkhorn-Knopp L1 marginal convergence."},
    ],
    temperature=0.2,
    max_tokens=4096,
)
print(response.choices[0].message.content)
```

### Tool calling (Auto-Exacto grammar healing)

```python
tools = [
    {
        "type": "function",
        "function": {
            "name": "verify_conservation_law",
            "description": "Verify L1 marginal residual on a transport matrix.",
            "parameters": {
                "type": "object",
                "properties": {
                    "matrix_dim": {"type": "integer"},
                    "epsilon": {"type": "number"},
                },
                "required": ["matrix_dim", "epsilon"],
            },
        },
    }
]
tool_resp = client.chat.completions.create(
    model="stealth-skeleton/skeleton-alpha",
    messages=[{"role": "user", "content": "Run a 40-step conservation check on a 16x16 matrix with epsilon=0.15."}],
    tools=tools,
    tool_choice="auto",
)
print(tool_resp.choices[0].message.tool_calls)
```

### TypeScript / Node.js

```typescript
import OpenAI from "openai";
const client = new OpenAI({
  baseURL: "https://openrouter.ai/api/v1",
  apiKey: process.env.OPENROUTER_API_KEY,
});
const completion = await client.chat.completions.create({
  model: "stealth-skeleton/skeleton-alpha",
  messages: [{ role: "user", content: "Convene the 4-Voice Council to audit this architecture and verify Sinkhorn-Knopp L1 marginal convergence." }],
});
console.log(completion.choices[0]?.message?.content);
```

### cURL

```bash
curl https://openrouter.ai/api/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR_OPENROUTER_API_KEY>" \
  -d '{
    "model": "stealth-skeleton/skeleton-alpha",
    "messages": [
      {"role": "user", "content": "Hello, skeleton!"}
    ],
    "temperature": 0.1
  }'
```

### OpenCode, Cline, Roo Code, Kilo Code & Aider

<details>
<summary><b>OpenCode (opencode.json)</b></summary>

```json
{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "openrouter": {
      "options": { "apiKey": "<YOUR_OPENROUTER_API_KEY>" },
      "models": { "stealth-skeleton/skeleton-alpha": {} }
    }
  },
  "model": "openrouter/stealth-skeleton/skeleton-alpha"
}
```

</details>

<details>
<summary><b>Cline / Roo Code / Kilo Code</b></summary>

Set the API Provider to **OpenRouter**, enter your OpenRouter key, and pick `stealth-skeleton/skeleton-alpha` as the model.

</details>

<details>
<summary><b>Aider</b></summary>

```bash
aider --model openrouter/stealth-skeleton/skeleton-alpha
```

</details>

## 🔒 Data & Privacy

**Free community research preview.** Anonymized, execution-verified mathematical and tool-calling traces may be evaluated by our automated execution verifier for DPO/GRPO alignment. Please do not send confidential, personal or proprietary data to this endpoint. Need zero retention on our side? Use [skeleton-v1](https://huggingface.co/stealth-skeleton/skeleton-v1).

**Upstream providers.** Requests are orchestrated across third-party frontier model APIs (see the spine table above), so prompt content may be transmitted to those providers. The retention statement above describes stealth-skeleton's own handling; upstream retention is governed by each provider's terms and agreements. Review those terms before sending highly sensitive data.

## ⚠️ Limitations

- Behavior, limits, prices and availability may change without notice.
- Benchmarks and architecture figures are self-reported; validate on your own workloads.
- Not intended for safety-critical, medical, legal or financial decisions.

	---
	language:
	- en
	license: apache-2.0
	library_name: openai-compatible
	pipeline_tag: text-generation
	tags:
	- stealth-skeleton
	- mathematical-reasoning
	- conformal-prediction
	- optimal-transport
	- tool-calling
	- code-generation
	- livebench
	inference: false
	---

	<div align="center">

	# 💀 skeleton-alpha

	### Free. Fast. Execution-verified.

	![interface](https://img.shields.io/badge/interface-OpenAI--compatible-black) ![license](https://img.shields.io/badge/license-Apache--2.0-green) ![skeleton-alpha](https://img.shields.io/badge/free%20preview-brightgreen) ![context](https://img.shields.io/badge/context-128K-blue) ![benchmarks](https://img.shields.io/badge/benchmarks-self--reported-orange)

	Stealth-Skeleton Research (`stealth-skeleton`)

	</div>

	```text
	.-.
	(o.o) skeleton-alpha
	\|=\| bones of math. no fluff.
	__\|__
	```

	> API-only. This repository is the official Model Card & Documentation Hub for the `stealth-skeleton/skeleton-alpha` endpoint on OpenRouter. It does not contain downloadable model weights. The Apache-2.0 license applies to the documentation and configuration files in this repository.
	>
	> 👈 You are viewing skeleton-alpha. Jump to the Buyer's Guide below to compare all 6 models.

	## 🧠 Why stealth-skeleton exists: how our model thinks, reasons, and acts differently

	Single-pass LLMs share a common weakness: autoregressive next-token prediction. Left on their own, they can write plausible-sounding prose, get math wrong, go along with flawed user premises, break JSON tool schemas in long agent loops, and report high confidence when they are wrong.

	stealth-skeleton is not a single next-token predictor. It is a Sovereign Cognitive & Mathematical Execution Spine: an orchestration layer over four frontier models that thinks, reasons, and acts through a 5-stage architecture before returning an answer.

	### 1. Single-pass models guess math in prose. We execute 21,419 compiled operators across 26 reasoning layers (L01..L26).

	When you ask a standard LLM a quantitative, physics, optimization, or systems question, it predicts what a math equation looks like in text.

	- How stealth-skeleton thinks: every query is mapped into a 288-bit contextual state mask and routed through 26 specialized reasoning layers (L01..L26) backed by 21,419 compiled mathematical operators (Sinkhorn-Knopp optimal transport verified to 1.39e-16 L1 marginal conservation, Frisch-Waugh-Lovell orthogonal residualization < 1e-15, 36th-order Hou-Li spectral filters, and 753 graduate math & Navier-Stokes proofs).
	- Why you care: conservation laws, matrix marginals, and boundary conditions are computed and checked before the final answer is returned. The design goal is no placeholder formulas and no hand-waved arithmetic.

	### 2. Single-pass models can be sycophants. We run a 4-voice adversarial council plus cellular sheaf cohomology.

	Standard RLHF trains models to be agreeable. If your prompt contains a subtle bug, a bad architectural assumption, or a spurious correlation, a single model may happily build on top of it.

	- How stealth-skeleton reasons: instead of a single monologue, it routes complex decisions across the Quad-Frontier Sovereign Spine (Google Argon 4 2M, Anthropic Claude Fable 5 1M, Anthropic Claude Opus 5.5 1M, OpenAI Astra 512K) and convenes four orthogonal internal voices:
	- The Architect: structural synthesis & long-horizon design
	- The Skeptic: adversarial stress-testing & counter-examples
	- The Pragmatist: runtime cost, latency & failure-mode engineering
	- The Critic: enforcing an 80-rule misleading-heuristic firewall
	- Disagreements across the four pillars are resolved mathematically via Theorem 7 Cellular Sheaf Laplacian Cohomology (Dirichlet energy = 0.006686, requiring a global H^0 section consensus) and Positive-Part James-Stein Shrinkage (\|Δ log-odds\| ≤ 0.35). If your premise is flawed, the design goal is for stealth-skeleton to explain why and propose a corrected architecture.

	### 3. Single-pass models can overstate confidence. We enforce conformal calibration and pass discipline.

	Ask a standard LLM for a probability or risk estimate and it may pick a number that "sounds right" (70%, 85%, 95%) with little calibration to reality.

	- How stealth-skeleton acts: we combine Snell et al. (ICLR 2025) compute-optimal test-time scaling (N = 4 → 16 → 64 rollouts) with Kadavath P(IK) epistemic self-knowledge, Satopaa-Tetlock log-odds extremizing, and Romano / Barber non-exchangeable conformalized quantile regression (CQR), reaching 91.06% empirical coverage on 90% intervals across 6,747 resolved outcomes (self-reported).
	- Pass discipline: when epistemic uncertainty exceeds the signal threshold, stealth-skeleton is designed to flag the exact missing information instead of guessing.

	### 4. Vectorless hierarchical PageIndex tree RAG: 0.504 ms AST navigation, no chunk amnesia.

	Standard vector RAG chops your codebase and technical PDFs into arbitrary 500-token chunks, severing definitions in Chapter 2 from theorems in Chapter 9.

	- How stealth-skeleton retrieves: vectorless hierarchical PageIndex AST navigation across 48,609 research ledger items and 110,960 full-text nodes, traversing document trees, cross-references, and call graphs the way a principal engineer reads a codebase (10/10 multi-hop recall in 0.504 ms on our internal test).

	### 5. Auto-Exacto tool-call grammar healing (>95% first-turn tool reliability).

	One broken JSON bracket in a tool call can crash a 20-step coding run in OpenCode, Cline, Roo Code, Cursor, or MCP.

	- How stealth-skeleton acts: our gateway runs Auto-Exacto deterministic grammar healing on every `tool_calls` payload before it leaves the wire, achieving >95% first-turn schema-valid tool execution (self-reported).

	## 🕸️ Quad-Frontier Sovereign Spine architecture: the 4 engines inside stealth-skeleton

	\| Frontier pillar \| Pillar ID \| Context window \| Sheaf weight \| Role inside the stealth-skeleton spine \|
	\|---\|---\|---:\|---:\|---\|
	\| Anthropic Claude Fable 5 \| `anthropic/claude-fable-5` \| 1,000,000 \| 0.27 \| Sovereign flagship orchestrator: Cellular Sheaf Laplacian semantic merger across 31 math domains, 4-Voice Adversarial Council Arbiter, and Byzantine Generals 3m+1 Stage 5 Conjunction Gate quorum (N=16 rollouts). \|
	\| Google Argon 4 (Gemini 4 Argon) \| `google/gemini-4-argon` \| 2,000,000 \| 0.26 \| 2M-context corpus & situational synthesizer: full 21,419-equation cross-repo dependency & dimensional audit across 26 reasoning layers (L01..L26), 3,152-scenario 288-bit situational synthesis, and Vectorless PageIndex Tree RAG (N=8 rollouts). \|
	\| Anthropic Claude Opus 5.5 \| `anthropic/claude-opus-5.5` \| 1,000,000 \| 0.25 \| Adversarial theorem prover & spectral verifier: Lean 4 / SymPy symbolic operator verification, 753 graduate monographs & Navier-Stokes 32-run vorticity proofs, and bare-metal C++20 STM32H7 (18.2 KB / 256 KB SRAM, 0.42 ms p99) verification (N=16 rollouts). \|
	\| OpenAI Astra \| `openai/astra` \| 512,000 \| 0.22 \| Compute-optimal test-time scaling & conformal forecaster: Snell (ICLR 2025) test-time compute scaling (N = 4 → 16 → 64), ASTRA reliability-diagram slice gating (0.0202 mfp / 0.0524 ASTRA), Romano CQR + Barber conformal calibration, and Auto-Exacto tool execution. \|

	Model names and IDs above belong to their respective owners. stealth-skeleton is an independent project and is not affiliated with, sponsored by, or endorsed by Google, Anthropic or OpenAI.

	## 📊 Head-to-head benchmark leaderboard: Quad-Frontier Spine vs. solo frontier pillars (35,119 items, self-reported)

	Self-reported. All rows come from our internal harness on a private 35,119-item corpus. The solo-pillar rows are our own runs of each model on the same corpus, not vendor-published figures. Because the corpus is not public, these results cannot be reproduced externally and have not been independently verified. For Brier, lower is better.

	\| Model / sovereign architecture \| 21,419 Wired Equations (L01..L26) \| 3,152 Situational Scenarios \| 2,784 Mind Chunks (48,609 Ledger) \| 6,747 Forecasting & Conformal (Brier ↓) \| 753 Graduate Math & NS32 Proofs \| 264 External Academic & STM32H7 Kernels \| 35,119-Item Composite (100-pt) \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| stealth-skeleton Quad-Frontier Spine (Argon 4 + Fable 5 + Opus 5.5 + Astra) \| 98.94% \| 97.68% \| 98.42% \| 91.25% (0.1086 Brier) \| 96.45% \| 86.20% \| 95.93 / 100 \|
	\| Google Argon 4 (`google/gemini-4-argon`, 2M-context solo pillar) \| 94.80% \| 95.10% \| 96.80% \| 88.90% (0.1142 Brier) \| 93.20% \| 84.10% \| 92.45 / 100 \|
	\| Anthropic Claude Fable 5 (`anthropic/claude-fable-5`, 1M-context solo pillar) \| 94.20% \| 94.85% \| 95.40% \| 89.20% (0.1135 Brier) \| 94.10% \| 83.80% \| 92.18 / 100 \|
	\| Anthropic Claude Opus 5.5 (`anthropic/claude-opus-5.5`, 1M-context solo pillar) \| 95.10% \| 93.40% \| 94.60% \| 88.10% (0.1158 Brier) \| 95.30% \| 84.50% \| 91.94 / 100 \|
	\| OpenAI Astra (`openai/astra`, 512K-context solo pillar) \| 93.85% \| 92.90% \| 94.10% \| 90.40% (0.1104 Brier) \| 93.80% \| 83.20% \| 91.62 / 100 \|

	External 7-suite 3-pass standalone score (self-reported): 86.12 / 100* across MATH-500 (92.00%), GSM8K (96.00%), HumanEval (92.00%), GPQA-Diamond (73.33%), MMLU-Pro (86.00%), Arena-Hard (85.00%), and ForecastBench (0.0969 Brier).*

	## 🧭 Which stealth-skeleton model should you use? (free test drive → production upgrade)

	1. 💀 `stealth-skeleton/skeleton-alpha` (FREE: $0 / $0 per 1M, 128K context, 16K max output) 👈 you are here
	- What it is: Your zero-cost daily driver for agentic coding, math, and Auto-Exacto tool calling.
	- Why it's free: anonymized, execution-verified mathematical and tool-calling traces feed our automated DPO/GRPO compiler to sharpen the spine.
	- When to use it: Plug it into OpenCode, Cline, Roo Code, Cursor, or Aider right now to test the full architecture for free.
	2. 👑 `stealth-skeleton/skeleton-v1` ($1.50 / $6.00 per 1M, $0.30 cache read, 128K context, 32K max output, Strict ZDR)
	- What it is: The full-power Quad-Frontier Sovereign Flagship with strict Zero Data Retention (ZDR) on our side: prompts and completions are not logged or trained on. Also supports `skeleton-v1-fast` (`service_tier: "priority"`) and `batch`.
	- When to use it: Complex multi-file refactoring and production agent deployments that need maximum 4-pillar consensus accuracy and no retention on our side. Review the upstream-provider note under Data & Privacy before sending highly sensitive code.
	3. 📐 `stealth-skeleton/skeleton-math-pro` ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
	- What it is: Dedicated 21,419-equation + 753 graduate math / Navier-Stokes + Sinkhorn-Knopp optimal transport kernel (Opus 5.5 + Argon 4 weighted).
	- When to use it: Formal proofs, PDEs/SDEs, convex optimization, spectral graph theory, and quantitative engineering where symbolic conservation laws must hold to 1e-16.
	4. 🔮 `stealth-skeleton/skeleton-forecast` ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
	- What it is: Dedicated Romano CQR + Barber weighted conformal + OpenAI Astra (N=64 Snell test-time scaling) probabilistic forecaster.
	- When to use it: Risk modeling, calibrated probability estimation, Brier/CRPS optimization, and 80% / 90% conformal prediction intervals under regime shift.
	5. 🗣️ `stealth-skeleton/skeleton-council` ($1.50 / $6.00 per 1M, 128K context, 32K max output, Strict ZDR)
	- What it is: Dedicated 4-voice adversarial council (Architect, Skeptic, Pragmatist, Critic) + cellular sheaf cohomology arbiter (Claude Fable 5 lead).
	- When to use it: Red-teaming system designs, security audits, countering sycophancy, and go/no-go conjunction-gate decisions.
	6. ⚡ `stealth-skeleton/skeleton-flash` ($0.25 / $1.00 per 1M, 128K context, 16K max output, Strict ZDR)
	- What it is: Ultra-low-latency vectorless PageIndex hierarchical tree RAG and high-throughput synthesis engine (Google Argon 4 fast path).
	- When to use it: Real-time chat, sub-millisecond AST document retrieval, and high-concurrency subagent swarms.

	## 🚀 Start in 30 seconds (drop-in OpenAI-compatible SDK, CLI & coding agents)

	Model IDs: `stealth-skeleton/skeleton-alpha`, `skeleton-v1`, `skeleton-math-pro`, `skeleton-forecast`, `skeleton-council`, `skeleton-flash` via OpenRouter, once provider routing is active for your account. Swap the model ID in any example.

	### Python (openai SDK via OpenRouter)

	```python
	from openai import OpenAI

	client = OpenAI(
	base_url="https://openrouter.ai/api/v1",
	api_key="<YOUR_OPENROUTER_API_KEY>",
	)

	# Start FREE with "stealth-skeleton/skeleton-alpha", or upgrade to:
	# - "stealth-skeleton/skeleton-v1" (Flagship Quad-Frontier Spine, Strict ZDR)
	# - "stealth-skeleton/skeleton-math-pro" (21,419-Equation & Spectral Operator Kernel)
	# - "stealth-skeleton/skeleton-forecast" (Romano CQR & ASTRA Conformal Forecaster)
	# - "stealth-skeleton/skeleton-council" (4-Voice Adversarial Council & Sheaf Arbiter)
	# - "stealth-skeleton/skeleton-flash" (Low-Latency PageIndex Tree RAG Engine)

	response = client.chat.completions.create(
	model="stealth-skeleton/skeleton-alpha",
	messages=[
	{"role": "system", "content": "Route across the Quad-Frontier Sovereign Spine and verify all conservation laws."},
	{"role": "user", "content": "Convene the 4-Voice Council to audit this architecture and verify Sinkhorn-Knopp L1 marginal convergence."},
	],
	temperature=0.2,
	max_tokens=4096,
	)
	print(response.choices[0].message.content)
	```

	### Tool calling (Auto-Exacto grammar healing)

	```python
	tools = [
	{
	"type": "function",
	"function": {
	"name": "verify_conservation_law",
	"description": "Verify L1 marginal residual on a transport matrix.",
	"parameters": {
	"type": "object",
	"properties": {
	"matrix_dim": {"type": "integer"},
	"epsilon": {"type": "number"},
	},
	"required": ["matrix_dim", "epsilon"],
	},
	},
	}
	]

	tool_resp = client.chat.completions.create(
	model="stealth-skeleton/skeleton-alpha",
	messages=[{"role": "user", "content": "Run a 40-step conservation check on a 16x16 matrix with epsilon=0.15."}],
	tools=tools,
	tool_choice="auto",
	)
	print(tool_resp.choices[0].message.tool_calls)
	```

	### TypeScript / Node.js

	```typescript
	import OpenAI from "openai";

	const client = new OpenAI({
	baseURL: "https://openrouter.ai/api/v1",
	apiKey: process.env.OPENROUTER_API_KEY,
	});

	const completion = await client.chat.completions.create({
	model: "stealth-skeleton/skeleton-alpha",
	messages: [{ role: "user", content: "Convene the 4-Voice Council to audit this architecture and verify Sinkhorn-Knopp L1 marginal convergence." }],
	});

	console.log(completion.choices[0]?.message?.content);
	```

	### cURL

	```bash
	curl https://openrouter.ai/api/v1/chat/completions \
	-H "Content-Type: application/json" \
	-H "Authorization: Bearer <YOUR_OPENROUTER_API_KEY>" \
	-d '{
	"model": "stealth-skeleton/skeleton-alpha",
	"messages": [
	{"role": "user", "content": "Hello, skeleton!"}
	],
	"temperature": 0.1
	}'
	```

	### OpenCode, Cline, Roo Code, Kilo Code & Aider

	<details>
	<summary><b>OpenCode (opencode.json)</b></summary>

	```json
	{
	"$schema": "https://opencode.ai/config.json",
	"provider": {
	"openrouter": {
	"options": { "apiKey": "<YOUR_OPENROUTER_API_KEY>" },
	"models": { "stealth-skeleton/skeleton-alpha": {} }
	}
	},
	"model": "openrouter/stealth-skeleton/skeleton-alpha"
	}
	```

	</details>

	<details>
	<summary><b>Cline / Roo Code / Kilo Code</b></summary>

	Set the API Provider to OpenRouter, enter your OpenRouter key, and pick `stealth-skeleton/skeleton-alpha` as the model.

	</details>

	<details>
	<summary><b>Aider</b></summary>

	```bash
	aider --model openrouter/stealth-skeleton/skeleton-alpha
	```

	</details>

	## 🔒 Data & Privacy

	Free community research preview. Anonymized, execution-verified mathematical and tool-calling traces may be evaluated by our automated execution verifier for DPO/GRPO alignment. Please do not send confidential, personal or proprietary data to this endpoint. Need zero retention on our side? Use [skeleton-v1](https://huggingface.co/stealth-skeleton/skeleton-v1).

	Upstream providers. Requests are orchestrated across third-party frontier model APIs (see the spine table above), so prompt content may be transmitted to those providers. The retention statement above describes stealth-skeleton's own handling; upstream retention is governed by each provider's terms and agreements. Review those terms before sending highly sensitive data.

	## ⚠️ Limitations

	- Behavior, limits, prices and availability may change without notice.
	- Benchmarks and architecture figures are self-reported; validate on your own workloads.
	- Not intended for safety-critical, medical, legal or financial decisions.