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 liortesta/ClawdAgent --skill sglanggit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote 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/liortesta/clawdagent/sglang)<a href="https://agentmods.dev/skills/liortesta/clawdagent/sglang"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/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 | $0.03178 |
| Opus 5 | $0.00036 | $0.01589 |
| Sonnet 5 | $0.00014 | $0.00636 |
| Haiku 4.5 | $0.00007 | $0.00318 |
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
100% identical to sglang — 0 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 — 443 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 · 443 lines · 72 tokens per session scan A 05e664a43bb8
sglang is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 11d ago), licensed Apache-2.0. It adds 72 tokens to every session and 3,178 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 100% identical to sglang, differing in 0 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.