synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add synthetic-sciences/openscience --skill sglanggit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/sglang)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/sglang"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/sglang.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.03226 |
| Opus 5 | $0.00036 | $0.01613 |
| Sonnet 5 | $0.00014 | $0.00645 |
| Haiku 4.5 | $0.00007 | $0.00323 |
Grade A, and why
sglang scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:30000/v1/chat/completions \ This is a copy
91% identical to sglang — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SGLang
High-performance serving framework for LLMs and VLMs with RadixAttention for automatic prefix caching.
When to use SGLang
Use SGLang when:
- Need structured outputs (JSON, regex, grammar)
- Building agents with repeated prefixes (system prompts, tools)
- Agentic workflows with function calling
- Multi-turn conversations with shared context
- Need faster JSON decoding (3× vs standard)
Use vLLM instead when:
- Simple text generation without structure
- Don't need prefix caching
- Want mature, widely-tested production system
Use TensorRT-LLM instead when:
- Maximum single-request latency (no batching needed)
- NVIDIA-only deployment
- Need FP8/INT4 quantization on H100
Quick start
Installation
# pip install (recommended)
pip install "sglang[all]"
# With FlashInfer (faster, CUDA 11.8/12.1)
pip install sglang[all] flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
# From source
git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e "python[all]"
Launch server
# Basic server (Llama 3-8B)
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-8B-Instruct \
--port 30000
# With RadixAttention (automatic prefix caching)
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-8B-Instruct \
--port 30000 \
--enable-radix-cache # Default: enabled
# Multi-GPU (tensor parallelism)
python -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3-70B-Instruct \
--tp 4 \
--port 30000
Basic inference
import sglang as sgl
# Set backend
sgl.set_default_backend(sgl.OpenAI("http://localhost:30000/v1"))
# Simple generation
@sgl.function
def simple_gen(s, question):
s += "Q: " + question + "\n"
s += "A:" + sgl.gen("answer", max_tokens=100)
# Run
state = simple_gen.run(question="What is the capital of France?")
print(state["answer"])
# Output: "The capital of France is Paris."
Structured JSON output
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 449 lines · 72 tokens per session scan A cee337564afa
sglang is a skill published in the GitHub repository synthetic-sciences/openscience (3,501 stars, last pushed today), licensed Apache-2.0. It adds 72 tokens to every session and 3,226 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to sglang, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
sglang
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
sglang
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
sglang
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
sglang
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
sglang
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
sglang
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.