SGLang is a framework for running inference for large language models and multimodal models, meaning it processes inputs to produce model outputs such as text or other media. It is used to serve and accelerate open AI models and related workloads.
Borrowing it
Nothing to install: this file belongs to sgl-project/sglang. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sgl-project/sglang/main/.agents/skills/generate-profile/SKILL.mdgit clone --depth 1 https://github.com/sgl-project/sglangWrote 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/sgl-project/sglang/generate-profile)<a href="https://agentmods.dev/skills/sgl-project/sglang/generate-profile"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/generate-profile/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sgl-project/sglang/generate-profile"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/generate-profile.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00038 | $0.01295 |
| Opus 5.5 | $0.00015 | $0.00518 |
| Sonnet 5.5 | $0.00008 | $0.00259 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
generate-profile 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 14d 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.
if curl -s http://127.0.0.1:<port>/health 2>/dev/null | grep -q "ok\|healthy"; then How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate an E2E Profile of an SGLang Server Run
This skill launches an SGLang server, validates it with a quick accuracy test, generates a profiling trace, and returns the profile file path.
Prerequisites
- A working SGLang installation (
pip install -e .or equivalent) - At least one available CUDA GPU
- The
sgl-evalCLI, installed in its own virtualenv as described in.claude/rules/prefer-sgl-eval.md, not in the server's env
Step-by-step Workflow
Step 1: Launch the server
CUDA_VISIBLE_DEVICES=<gpu_id> sglang serve --model-path <model> --port <port> &
- Default model:
Qwen/Qwen3-8B(good balance of speed and quality) - Default port:
30000 - The server runs in the background. Save the PID for cleanup.
- Use the GPU specified by the user's preferences (check memory files for GPU preferences).
Step 2: Wait for server readiness
Poll the health endpoint until the server is ready:
for i in $(seq 1 120); do
if curl -s http://127.0.0.1:<port>/health 2>/dev/null | grep -q "ok\|healthy"; then
echo "Server ready"
break
fi
sleep 5
done
The server prints "The server is fired up and ready to roll!" to stdout when ready. The health endpoint returns 200 once the server can accept requests.
Typical startup time: 30-90 seconds depending on model size and whether CUDA graphs are being compiled.
Step 3: Validate accuracy (sanity check)
sgl-eval run gsm8k --base-url http://127.0.0.1:<port>/v1 --num-examples 20
- Expected
score: > 80% for capable models (Qwen3-8B, Llama-3.1-8B-Instruct, etc.) - This is a quick sanity check, not a rigorous benchmark.
- If accuracy is unexpectedly low, something is wrong — do not proceed to profiling.
Step 4: Generate the profile
python3 -m sglang.test.send_one --profile
This command:
- Sends a request to the server
- Triggers the profiler for 5 steps (default)
- Generates a trace file under
/tmp/<timestamp>/ - The trace directory contains:
<timestamp>-TP-0.trace.json.gz— Chrome trace format (open inchrome://tracingor Perfetto)server_args.json— the server configuration used
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.
- 14d ago Changed · +1 lines 6c28b99d93aa
- 1mo ago Changed · -2 lines 0d8a29a88eef
- 2mo ago First seen · 144 lines · 38 tokens per session scan A 0fcd80691c8c
generate-profile is a skill published in the GitHub repository sgl-project/sglang (38,324 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 1,295 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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