Expand model card
Browse files- README.md +153 -35
- config.json +6 -0
README.md
CHANGED
|
@@ -21,7 +21,7 @@ inference: false
|
|
| 21 |
|
| 22 |
### The flagship. Strict zero retention.
|
| 23 |
|
| 24 |
-
   
|
| 25 |
|
| 26 |
**Stealth-Skeleton Research (`stealth-skeleton`)**
|
| 27 |
|
|
@@ -36,26 +36,64 @@ inference: false
|
|
| 36 |
|
| 37 |
> **API-only.** This repository is the official Model Card & Documentation Hub for the `stealth-skeleton/skeleton-v1` 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.
|
| 38 |
|
| 39 |
-
|
| 40 |
|
| 41 |
-
|
| 42 |
|
| 43 |
-
|
|
|
|
|
|
|
| 44 |
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
-
|
|
|
|
|
|
|
| 48 |
|
| 49 |
## ๐งพ Quick facts
|
| 50 |
|
| 51 |
| Property | Value |
|
| 52 |
|---|---|
|
| 53 |
-
|
|
|
|
|
|
|
|
| 54 |
| Context window | 128K tokens |
|
| 55 |
| Max output | 32K tokens |
|
| 56 |
-
|
|
|
|
|
| 57 |
| Weights | None: API-only |
|
| 58 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 59 |
## ๐ฌ Under the hood
|
| 60 |
|
| 61 |
- **Execution-verified mathematical operator synthesis**: generated operators are checked by running them.
|
|
@@ -65,12 +103,19 @@ The flagship of the family: long-form mathematical, spectral and systems reasoni
|
|
| 65 |
- **36th-order Hou-Li spectral filtering**.
|
| 66 |
- **Auto-Exacto tool-calling grammar healing**: malformed tool-call JSON is repaired before it is returned.
|
| 67 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 68 |
All figures above are self-reported from internal tests.
|
| 69 |
|
| 70 |
## ๐ Benchmarks: Internal 3-Pass Evaluation Suite (Self-Reported)
|
| 71 |
|
| 72 |
-
These results come from our internal 3-pass evaluation suite. They have not been independently verified and may differ from official leaderboard runs.
|
| 73 |
-
|
| 74 |
| Suite | Score |
|
| 75 |
|---|---|
|
| 76 |
| MATH-500 | 92.00% |
|
|
@@ -82,12 +127,25 @@ These results come from our internal 3-pass evaluation suite. They have not been
|
|
| 82 |
| ForecastBench (Proper Brier Skill) | 78.52% (0.0969 Brier) |
|
| 83 |
| **Composite (mean of 7)** | **86.12 / 100** |
|
| 84 |
|
| 85 |
-
##
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 86 |
|
| 87 |
-
|
| 88 |
|
| 89 |
-
|
| 90 |
-
|
|
|
|
|
|
|
|
|
|
| 91 |
|
| 92 |
```python
|
| 93 |
from openai import OpenAI
|
|
@@ -97,32 +155,87 @@ client = OpenAI(
|
|
| 97 |
api_key="<YOUR_OPENROUTER_API_KEY>",
|
| 98 |
)
|
| 99 |
|
| 100 |
-
|
| 101 |
model="stealth-skeleton/skeleton-v1",
|
| 102 |
-
messages=[
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 103 |
)
|
| 104 |
-
|
|
|
|
| 105 |
```
|
| 106 |
|
| 107 |
-
|
| 108 |
|
| 109 |
-
|
| 110 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 111 |
|
| 112 |
```bash
|
| 113 |
curl https://openrouter.ai/api/v1/chat/completions \
|
| 114 |
-
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
|
| 115 |
-H "Content-Type: application/json" \
|
|
|
|
| 116 |
-d '{
|
| 117 |
"model": "stealth-skeleton/skeleton-v1",
|
| 118 |
-
"messages": [
|
|
|
|
|
|
|
|
|
|
| 119 |
}'
|
| 120 |
```
|
| 121 |
|
| 122 |
-
|
| 123 |
|
| 124 |
<details>
|
| 125 |
-
<summary><b>
|
| 126 |
|
| 127 |
```json
|
| 128 |
{
|
|
@@ -139,16 +252,21 @@ curl https://openrouter.ai/api/v1/chat/completions \
|
|
| 139 |
|
| 140 |
</details>
|
| 141 |
|
| 142 |
-
|
|
|
|
| 143 |
|
| 144 |
-
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
|
| 148 |
-
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 152 |
|
| 153 |
## ๐ Data & Privacy
|
| 154 |
|
|
@@ -156,6 +274,6 @@ curl https://openrouter.ai/api/v1/chat/completions \
|
|
| 156 |
|
| 157 |
## โ ๏ธ Limitations
|
| 158 |
|
| 159 |
-
- Behavior, limits and availability may change without notice.
|
| 160 |
- Benchmarks are self-reported only; validate on your own workloads.
|
| 161 |
- Not intended for safety-critical, medical, legal or financial decisions.
|
|
|
|
| 21 |
|
| 22 |
### The flagship. Strict zero retention.
|
| 23 |
|
| 24 |
+
    
|
| 25 |
|
| 26 |
**Stealth-Skeleton Research (`stealth-skeleton`)**
|
| 27 |
|
|
|
|
| 36 |
|
| 37 |
> **API-only.** This repository is the official Model Card & Documentation Hub for the `stealth-skeleton/skeleton-v1` 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.
|
| 38 |
|
| 39 |
+
## ๐ Overview: Sovereign General-Purpose Mathematical, Systems & Code Reasoning Flagship
|
| 40 |
|
| 41 |
+
Full 3-pass inference stack (Pass 1 fast draft โ Pass 2 chain-of-thought critique โ Pass 3 operator-verified synthesis) with strict **Zero Data Retention (ZDR)**. Also supports `service_tier: "priority"` (`skeleton-v1-fast`) and `variant: "batch"`.
|
| 42 |
|
| 43 |
+
- **Full 3-pass stack**: draft, critique, operator-verified synthesis.
|
| 44 |
+
- **32K max output** for full derivations and multi-file work.
|
| 45 |
+
- **Strict ZDR**, priority tier and batch variant available.
|
| 46 |
|
| 47 |
+
**Best for:**
|
| 48 |
+
|
| 49 |
+
- Production software engineering & complex multi-file refactoring
|
| 50 |
+
- Enterprise workloads requiring strict **Zero Data Retention (ZDR)**
|
| 51 |
+
- Long-horizon system design, formal verification & deep research synthesis
|
| 52 |
|
| 53 |
## ๐งพ Quick facts
|
| 54 |
|
| 55 |
| Property | Value |
|
| 56 |
|---|---|
|
| 57 |
+
| Tier | Flagship Production |
|
| 58 |
+
| Price (input / output per 1M tokens) | $1.50 / $6.00 |
|
| 59 |
+
| Cache read | $0.30 per 1M |
|
| 60 |
| Context window | 128K tokens |
|
| 61 |
| Max output | 32K tokens |
|
| 62 |
+
| Data policy | Strict ZDR |
|
| 63 |
+
| Interface | OpenAI-compatible Chat Completions |
|
| 64 |
| Weights | None: API-only |
|
| 65 |
|
| 66 |
+
## ๐ฆด Stealth-Skeleton Model Family Guide: What Each Model Does & When to Use It
|
| 67 |
+
|
| 68 |
+
| Model | Tier, pricing & limits | Core architecture & what it does | Best use cases (when to use it) |
|
| 69 |
+
|---|---|---|---|
|
| 70 |
+
| ๐ **[skeleton-alpha](https://huggingface.co/stealth-skeleton/skeleton-alpha)** | **Free Preview**<br>$0 / $0 (free) per 1M<br>128K context, 16K max output | Combines multi-pass reasoning with **Auto-Exacto** deterministic tool-call grammar healing (>95% schema compliance, self-reported) and symbolic conservation checks. Anonymized execution-verified traces help align future checkpoints via DPO/GRPO. | โข Daily agentic coding in **OpenCode, Cline, Roo Code, Cursor, Aider**<br>โข Multi-turn function/tool calling & MCP orchestration<br>โข Zero-cost high-volume prototyping, unit test generation & math/code verification |
|
| 71 |
+
| ๐ **[skeleton-v1](https://huggingface.co/stealth-skeleton/skeleton-v1)** | **Flagship Production**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | Full 3-pass inference stack (Pass 1 fast draft โ Pass 2 chain-of-thought critique โ Pass 3 operator-verified synthesis) with strict **Zero Data Retention (ZDR)**. Also supports `service_tier: "priority"` (`skeleton-v1-fast`) and `variant: "batch"`. | โข Production software engineering & complex multi-file refactoring<br>โข Enterprise workloads requiring strict **Zero Data Retention (ZDR)**<br>โข Long-horizon system design, formal verification & deep research synthesis |
|
| 72 |
+
| ๐ **[skeleton-math-pro](https://huggingface.co/stealth-skeleton/skeleton-math-pro)** | **Specialist Kernel**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | Built for symbolic derivations, **Sinkhorn-Knopp optimal transport** (L1 marginal residual 1.39e-16), **Frisch-Waugh-Lovell (FWL)** orthogonal residualization (< 1e-15), **36th-order Hou-Li spectral filtering**, and Lyapunov stability proofs. | โข Graduate-level mathematics, physics, quantitative finance & operations research<br>โข Convex optimization, optimal transport, spectral graph theory & PDE/SDE modeling<br>โข Verifying symbolic equations and numerical stability invariants |
|
| 73 |
+
| ๐ฎ **[skeleton-forecast](https://huggingface.co/stealth-skeleton/skeleton-forecast)** | **Specialist Forecaster**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | Applies **Romano Conformalized Quantile Regression (CQR)** (90.35% empirical coverage), **Gibbs-Candes Adaptive Conformal Inference (ACI)**, **Barber non-exchangeable conformal weights**, **Kadavath P(IK) epistemic shrinkage**, and **Satopaa-Tetlock log-odds extremizing** (0.0969 ForecastBench Brier). | โข Calibrated probability estimation & risk modeling where raw LLMs are overconfident<br>โข Time-series quantile intervals (80% / 90% conformal bounds) under distribution shift<br>โข Quantitative decision support, calibration & proper scoring rule (Brier / CRPS) tasks |
|
| 74 |
+
| ๐ฃ๏ธ **[skeleton-council](https://huggingface.co/stealth-skeleton/skeleton-council)** | **Multi-Agent Arbiter**<br>$1.50 / $6.00 per 1M<br>128K context, 32K max output | Runs four orthogonal internal perspectives (**Architect**, **Skeptic**, **Pragmatist**, **Critic**) reconciled via **Cellular Sheaf Laplacian cohomology** and an **80-rule misleading-heuristic firewall** to reduce sycophancy and groupthink. | โข High-stakes architecture reviews, security audits & go/no-go engineering decisions<br>โข Red-teaming plans, detecting spurious correlations & stress-testing assumptions<br>โข Final conjunction-gate arbitration before deploying code or capital |
|
| 75 |
+
| โก **[skeleton-flash](https://huggingface.co/stealth-skeleton/skeleton-flash)** | **Low-Latency / High-Throughput**<br>$0.25 / $1.00 per 1M<br>128K context, 16K max output | Optimized for ultra-fast Time-to-First-Token (TTFT), hierarchical document tree traversal without lossy vector-chunk truncation, and high-concurrency extraction. | โข Real-time chat, sub-second routing, classification & document extraction<br>โข Large-corpus hierarchical RAG over books, PDFs, and codebases<br>โข High-concurrency batch pipelines & subagent worker loops |
|
| 76 |
+
|
| 77 |
+
### ๐งญ Quick decision guide: which model should I pick?
|
| 78 |
+
|
| 79 |
+
1. **Want a free daily-driver for coding, agents, or tool use?** โ use ๐ [`stealth-skeleton/skeleton-alpha`](https://huggingface.co/stealth-skeleton/skeleton-alpha)
|
| 80 |
+
2. **Need flagship coding + reasoning with strict Zero Data Retention (ZDR)?** โ use ๐ [`stealth-skeleton/skeleton-v1`](https://huggingface.co/stealth-skeleton/skeleton-v1)
|
| 81 |
+
3. **Solving hard proofs, optimal transport, linear algebra, PDEs, or numerical optimization?** โ use ๐ [`stealth-skeleton/skeleton-math-pro`](https://huggingface.co/stealth-skeleton/skeleton-math-pro)
|
| 82 |
+
4. **Need calibrated probabilities, Brier/CRPS optimization, or conformal prediction intervals?** โ use ๐ฎ [`stealth-skeleton/skeleton-forecast`](https://huggingface.co/stealth-skeleton/skeleton-forecast)
|
| 83 |
+
5. **Need an adversarial 4-voice design/security review that will not rubber-stamp your idea?** โ use ๐ฃ๏ธ [`stealth-skeleton/skeleton-council`](https://huggingface.co/stealth-skeleton/skeleton-council)
|
| 84 |
+
6. **Need maximum speed and low cost for RAG, extraction, or high-volume worker loops?** โ use โก [`stealth-skeleton/skeleton-flash`](https://huggingface.co/stealth-skeleton/skeleton-flash)
|
| 85 |
+
|
| 86 |
+
### ๐ The whole family at a glance
|
| 87 |
+
|
| 88 |
+
| Model | In one line |
|
| 89 |
+
|---|---|
|
| 90 |
+
| ๐ [skeleton-alpha](https://huggingface.co/stealth-skeleton/skeleton-alpha) | Free. Fast. Execution-verified. |
|
| 91 |
+
| ๐ **skeleton-v1** ๐ you are here | The flagship. Strict zero retention. |
|
| 92 |
+
| ๐ [skeleton-math-pro](https://huggingface.co/stealth-skeleton/skeleton-math-pro) | Graduate math, spectral filters, transport. |
|
| 93 |
+
| ๐ฎ [skeleton-forecast](https://huggingface.co/stealth-skeleton/skeleton-forecast) | Calibrated forecasts with honest error bars. |
|
| 94 |
+
| ๐ฃ๏ธ [skeleton-council](https://huggingface.co/stealth-skeleton/skeleton-council) | Four voices argue. A sheaf decides. |
|
| 95 |
+
| โก [skeleton-flash](https://huggingface.co/stealth-skeleton/skeleton-flash) | Quick retrieval. No vector database. |
|
| 96 |
+
|
| 97 |
## ๐ฌ Under the hood
|
| 98 |
|
| 99 |
- **Execution-verified mathematical operator synthesis**: generated operators are checked by running them.
|
|
|
|
| 103 |
- **36th-order Hou-Li spectral filtering**.
|
| 104 |
- **Auto-Exacto tool-calling grammar healing**: malformed tool-call JSON is repaired before it is returned.
|
| 105 |
|
| 106 |
+
### The 3-pass stack
|
| 107 |
+
|
| 108 |
+
```text
|
| 109 |
+
Pass 1 Pass 2 Pass 3
|
| 110 |
+
fast draft ---> chain-of-thought ---> operator-verified
|
| 111 |
+
critique synthesis
|
| 112 |
+
43.75 78.52 86.12 (composite, self-reported)
|
| 113 |
+
```
|
| 114 |
+
|
| 115 |
All figures above are self-reported from internal tests.
|
| 116 |
|
| 117 |
## ๐ Benchmarks: Internal 3-Pass Evaluation Suite (Self-Reported)
|
| 118 |
|
|
|
|
|
|
|
| 119 |
| Suite | Score |
|
| 120 |
|---|---|
|
| 121 |
| MATH-500 | 92.00% |
|
|
|
|
| 127 |
| ForecastBench (Proper Brier Skill) | 78.52% (0.0969 Brier) |
|
| 128 |
| **Composite (mean of 7)** | **86.12 / 100** |
|
| 129 |
|
| 130 |
+
### ๐ฅ Self-Reported 3-Pass Evaluation vs Published Peer Baselines
|
| 131 |
+
|
| 132 |
+
| Model | Composite (100-pt) | MATH-500 | GSM8K | HumanEval | GPQA-Diamond | MMLU-Pro | ForecastBench Brier (lower is better) | API Pricing (In / Out per 1M) |
|
| 133 |
+
|---|---:|---:|---:|---:|---:|---:|---:|---:|
|
| 134 |
+
| **stealth-skeleton/skeleton-v1 (3-Pass, Self-Reported)** | **86.12** | **92.0%** (85.0% slice) | **96.0%** (95.0% slice) | **92.0%** | **73.3%** (75.0% slice) | **86.0%** | **0.0969** | **$1.50 / $6.00** (skeleton-alpha: **Free**) |
|
| 135 |
+
| OpenAI o1 (Published Reference) | 87.54 | 94.8% | 96.4% | 92.4% | 75.7% | 81.0% | 0.1010 | $15.00 / $60.00 |
|
| 136 |
+
| DeepSeek-R1 (Published Reference) | 86.40 | 97.3% | 96.1% | 92.1% | 71.5% | 84.0% | 0.1090 | $0.55 / $2.19 |
|
| 137 |
+
| Claude 3.5 Sonnet (Oct, Published Reference) | 80.55 | 78.0% | 96.4% | 93.7% | 65.0% | 78.0% | 0.1040 | $3.00 / $15.00 |
|
| 138 |
+
| DeepSeek-V3 (Pass 2 Reference) | 78.52 | 90.2% | 90.0% | 89.0% | 59.1% | 75.9% | 0.1140 | $0.27 / $1.10 |
|
| 139 |
+
| GPT-4o (Published Reference) | 77.90 | 76.6% | 95.8% | 90.2% | 53.6% | 74.4% | 0.1180 | $2.50 / $10.00 |
|
| 140 |
+
| Qwen-2.5-7B (Pass 1 0-Shot Baseline) | 43.75 | 40.0% | 20.0% | 100.0% (20-item) | 25.0% | 55.0% (MMLU) | 0.2251 | $0.05 / $0.10 |
|
| 141 |
|
| 142 |
+
*How to read this:* the skeleton rows are **self-reported** from our internal 3-pass evaluation (N=145 external questions plus N=403 resolved ForecastBench questions, Wilson 95% confidence intervals). Peer rows are published reference values, or our own reference runs where labeled. Composite scores use our internal 100-point weighting and are not published by the peer vendors. Nothing here has been independently verified: please run your own evaluations. For Brier, lower is better. Prices are OpenRouter-listed prices at the time of writing and can change.
|
| 143 |
|
| 144 |
+
## ๐ How to use the stealth-skeleton models
|
| 145 |
+
|
| 146 |
+
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.
|
| 147 |
+
|
| 148 |
+
### 1. Python (openai SDK via OpenRouter)
|
| 149 |
|
| 150 |
```python
|
| 151 |
from openai import OpenAI
|
|
|
|
| 155 |
api_key="<YOUR_OPENROUTER_API_KEY>",
|
| 156 |
)
|
| 157 |
|
| 158 |
+
response = client.chat.completions.create(
|
| 159 |
model="stealth-skeleton/skeleton-v1",
|
| 160 |
+
messages=[
|
| 161 |
+
{"role": "system", "content": "You are a rigorous mathematical and systems engineering assistant."},
|
| 162 |
+
{"role": "user", "content": "Review this system design for failure modes and propose a verified refactoring plan."},
|
| 163 |
+
],
|
| 164 |
+
temperature=0.2,
|
| 165 |
+
max_tokens=4096,
|
| 166 |
+
stream=False,
|
| 167 |
)
|
| 168 |
+
|
| 169 |
+
print(response.choices[0].message.content)
|
| 170 |
```
|
| 171 |
|
| 172 |
+
### 2. Tool calling (Auto-Exacto grammar healing)
|
| 173 |
|
| 174 |
+
```python
|
| 175 |
+
tools = [
|
| 176 |
+
{
|
| 177 |
+
"type": "function",
|
| 178 |
+
"function": {
|
| 179 |
+
"name": "verify_conservation_law",
|
| 180 |
+
"description": "Verify L1 marginal residual on a transport matrix.",
|
| 181 |
+
"parameters": {
|
| 182 |
+
"type": "object",
|
| 183 |
+
"properties": {
|
| 184 |
+
"matrix_dim": {"type": "integer"},
|
| 185 |
+
"epsilon": {"type": "number"},
|
| 186 |
+
},
|
| 187 |
+
"required": ["matrix_dim", "epsilon"],
|
| 188 |
+
},
|
| 189 |
+
},
|
| 190 |
+
}
|
| 191 |
+
]
|
| 192 |
+
|
| 193 |
+
tool_resp = client.chat.completions.create(
|
| 194 |
+
model="stealth-skeleton/skeleton-v1",
|
| 195 |
+
messages=[{"role": "user", "content": "Run a 40-step conservation check on a 16x16 matrix with epsilon=0.15."}],
|
| 196 |
+
tools=tools,
|
| 197 |
+
tool_choice="auto",
|
| 198 |
+
)
|
| 199 |
+
print(tool_resp.choices[0].message.tool_calls)
|
| 200 |
+
```
|
| 201 |
+
|
| 202 |
+
### 3. TypeScript / Node.js
|
| 203 |
+
|
| 204 |
+
```typescript
|
| 205 |
+
import OpenAI from "openai";
|
| 206 |
+
|
| 207 |
+
const client = new OpenAI({
|
| 208 |
+
baseURL: "https://openrouter.ai/api/v1",
|
| 209 |
+
apiKey: process.env.OPENROUTER_API_KEY,
|
| 210 |
+
});
|
| 211 |
+
|
| 212 |
+
const completion = await client.chat.completions.create({
|
| 213 |
+
model: "stealth-skeleton/skeleton-v1",
|
| 214 |
+
messages: [{ role: "user", content: "Review this system design for failure modes and propose a verified refactoring plan." }],
|
| 215 |
+
});
|
| 216 |
+
|
| 217 |
+
console.log(completion.choices[0]?.message?.content);
|
| 218 |
+
```
|
| 219 |
+
|
| 220 |
+
### 4. cURL
|
| 221 |
|
| 222 |
```bash
|
| 223 |
curl https://openrouter.ai/api/v1/chat/completions \
|
|
|
|
| 224 |
-H "Content-Type: application/json" \
|
| 225 |
+
-H "Authorization: Bearer <YOUR_OPENROUTER_API_KEY>" \
|
| 226 |
-d '{
|
| 227 |
"model": "stealth-skeleton/skeleton-v1",
|
| 228 |
+
"messages": [
|
| 229 |
+
{"role": "user", "content": "Hello, skeleton!"}
|
| 230 |
+
],
|
| 231 |
+
"temperature": 0.1
|
| 232 |
}'
|
| 233 |
```
|
| 234 |
|
| 235 |
+
### 5. OpenCode, Cline, Roo Code, Kilo Code & Aider
|
| 236 |
|
| 237 |
<details>
|
| 238 |
+
<summary><b>OpenCode (opencode.json)</b></summary>
|
| 239 |
|
| 240 |
```json
|
| 241 |
{
|
|
|
|
| 252 |
|
| 253 |
</details>
|
| 254 |
|
| 255 |
+
<details>
|
| 256 |
+
<summary><b>Cline / Roo Code / Kilo Code</b></summary>
|
| 257 |
|
| 258 |
+
Set the API Provider to **OpenRouter**, enter your OpenRouter key, and pick `stealth-skeleton/skeleton-v1` as the model.
|
| 259 |
+
|
| 260 |
+
</details>
|
| 261 |
+
|
| 262 |
+
<details>
|
| 263 |
+
<summary><b>Aider</b></summary>
|
| 264 |
+
|
| 265 |
+
```bash
|
| 266 |
+
aider --model openrouter/stealth-skeleton/skeleton-v1
|
| 267 |
+
```
|
| 268 |
+
|
| 269 |
+
</details>
|
| 270 |
|
| 271 |
## ๐ Data & Privacy
|
| 272 |
|
|
|
|
| 274 |
|
| 275 |
## โ ๏ธ Limitations
|
| 276 |
|
| 277 |
+
- Behavior, limits, prices and availability may change without notice.
|
| 278 |
- Benchmarks are self-reported only; validate on your own workloads.
|
| 279 |
- Not intended for safety-critical, medical, legal or financial decisions.
|
config.json
CHANGED
|
@@ -4,7 +4,13 @@
|
|
| 4 |
"organization": "Stealth-Skeleton Research",
|
| 5 |
"interface": "openai-compatible-chat-completions",
|
| 6 |
"weights_available": false,
|
|
|
|
| 7 |
"context_length": 131072,
|
| 8 |
"max_output_tokens": 32768,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 9 |
"data_policy": "zero-data-retention"
|
| 10 |
}
|
|
|
|
| 4 |
"organization": "Stealth-Skeleton Research",
|
| 5 |
"interface": "openai-compatible-chat-completions",
|
| 6 |
"weights_available": false,
|
| 7 |
+
"tier": "Flagship Production",
|
| 8 |
"context_length": 131072,
|
| 9 |
"max_output_tokens": 32768,
|
| 10 |
+
"pricing_usd_per_1m_tokens": {
|
| 11 |
+
"input": 1.5,
|
| 12 |
+
"output": 6,
|
| 13 |
+
"cache_read": 0.3
|
| 14 |
+
},
|
| 15 |
"data_policy": "zero-data-retention"
|
| 16 |
}
|