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/ci-workflow-guide/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/ci-workflow-guide)<a href="https://agentmods.dev/skills/sgl-project/sglang/ci-workflow-guide"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/ci-workflow-guide/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/ci-workflow-guide"><img src="https://agentmods.dev/badge/skills/sgl-project/sglang/ci-workflow-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00059 | $0.07275 |
| Opus 5.5 | $0.00024 | $0.02910 |
| Sonnet 5.5 | $0.00012 | $0.01455 |
| Haiku 4.5 | $0.00006 | $0.00728 |
Grade A, and why
ci-workflow-guide scanned grade A with 0 findings 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 today.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SGLang CI Workflow Orchestration Guide
This skill covers the CI infrastructure layer — how tests are dispatched, gated, and aborted on failure across stages. For test authoring (templates, fixtures, registration, model selection), see the write-sglang-test skill.
Naming Conventions
- Suite:
base-{a,b,c}-test-{gpu_count}-gpu-{hardware}(e.g.,base-b-test-1-gpu-small) - Test group: Directory-level registered test group under
test/registered/(e.g.,hicachemaps totest/registered/hicache/test_*.py) - CI runner:
{gpu_count}-gpu-{hardware}(e.g.,1-gpu-5090,4-gpu-h100,8-gpu-h200)
Key Files
| File | Role |
|---|---|
.github/workflows/pr-test.yml |
Main workflow — all stages, jobs, conditions, matrix definitions |
.github/workflows/pr-test-extra.yml |
NVIDIA/shared Extra workflow, gated by BOTH run-ci and run-ci-nv-extra labels |
scripts/ci/opt_in_ci_tiers.json |
CI selections: name → workflow files and an optional opt-in label |
.github/workflows/pr-gate.yml |
PR gating: draft check, run-ci label, per-user rate limiting |
.github/actions/check-pr-test-health/action.yml |
Cross-job fail-fast: queries API for any failed job |
.github/actions/wait-for-jobs/action.yml |
Stage gating: polls API until stage jobs complete |
.github/actions/check-maintenance/action.yml |
Maintenance mode check |
.github/scripts/ci-labels.cjs |
Resolves the four CI control labels into dispatch axes |
test/run_suite.py |
Suite runner: collects, filters, partitions, executes tests |
python/sglang/test/ci/ci_register.py |
Test registration (AST-parsed markers), LPT auto-partition |
python/sglang/test/ci/ci_utils.py |
run_unittest_files(): execution, retry, continue-on-error |
scripts/ci/utils/slash_command_handler.py |
Handles slash commands from PR comments |
Architecture Overview
┌──────────────┐
│ build kernel │
└──────┬───────┘
│
├─ check-changes ──── detects which packages changed
│ (main_package, sgl_kernel, jit_kernel, multimodal_gen)
│
├─ call-gate ──────── pr-gate.yml (draft? label? rate limit?)
│
├─────────────────────────────────────────────────────┐
│ │
▼ │
┌─────────────────────────────────────┐ │
│ Base A (~3 min) │ │
│ pre-flight check │ │
│ │ │
│ ┌─────────────────────────────┐ │ │
│ │ base-a-test-1-gpu-small │ │ │
│ │ (small GPUs) │ │ │
│ └─────────────────────────────┘ │ │
│ ┌─────────────────────────────┐ │ │
│ │ base-a-test-cpu │ │ │
│ │ (CPU) │ │ │
│ └─────────────────────────────┘ │ │
└──────┬──────────────────────────────┘ │
│ │
▼ ▼
┌─────────────────────────────────────┐ ┌──────────────────────────┐
│ Base B (~30 min) │ │ kernel test │
│ base tests │ └──────────────────────────┘
│ │ ┌──────────────────────────┐
│ ┌─────────────────────────────┐ │ │ multimodal gen test │
│ │ base-b-test-1-gpu-small │ │ └──────────────────────────┘
│ │ (small GPUs, e.g. 5090) │ │
│ └─────────────────────────────┘ │
│ ┌─────────────────────────────┐ │
│ │ base-b-test-1-gpu-large │ │
│ │ (large GPUs, e.g. H100) │ │
│ └─────────────────────────────┘ │
│ ┌─────────────────────────────┐ │
│ │ base-b-test-2-gpu-large │ │
│ │ (large GPUs, e.g. H100) │ │
│ └─────────────────────────────┘ │
└──────┬──────────────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ Base C (~30 min) │
│ advanced tests │
│ │
│ ┌─────────────────────────────┐ │
│ │ base-c-test-4-gpu-h100 │ │
│ │ (H100 GPUs) │ │
│ └─────────────────────────────┘ │
│ ┌─────────────────────────────┐ │
│ │ base-c-test-8-gpu-h200 │ │
│ │ (8 x H200 GPUs) │ │
│ └─────────────────────────────┘ │
│ ┌─────────────────────────────┐ │
│ │ base-c-test-4-gpu-b200 │ │
│ │ (4 x B200 GPUs) │ │
│ └─────────────────────────────┘ │
│ ┌─────────────────────────────┐ │
│ │ Other advanced tests │ │
│ │ (DeepEP, PD Disagg, GB300) │ │
│ └─────────────────────────────┘ │
└──────┬──────────────────────────────┘
│
▼
┌─────────────────────────────────────┐
│ pr-test-finish │
│ aggregates all results, fails if │
│ any job failed/cancelled │
└─────────────────────────────────────┘
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.
- today Changed · +24 lines dae4a45fae71
- yesterday Changed · +19 lines 98209c68e162
- 7d ago Changed · +1 lines dae0b770d3ec
- 13d ago Changed 91c2d479f137
- 23d ago Changed 8ac88d896962
- 1mo ago Changed · +6 lines · -1 tokens per session 2e1ccfc43a47
- 1mo ago Changed · +2 lines 5169079beca7
- 2mo ago First seen · 406 lines · 60 tokens per session scan A 6b3ee541c275
ci-workflow-guide is a skill published in the GitHub repository sgl-project/sglang (38,324 stars, last pushed today), licensed Apache-2.0. It adds 59 tokens to every session and 7,275 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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mcp-dependency-review
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baby-sit
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
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Ultra-fast LLM inference on custom LPU hardware. OpenAI-compatible API at api.groq.com. Lowest latency in the industry (500-1000+ tok/s). Supports chat completions, vision, audio (Whisper STT + TTS), tool calling, JSON mode, and streaming. Free tier available. Inference only — no training.
meta-long-running-build-watchdog
Watches a long-running command via tmux, lets sub-agent diagnose failures and propose a fix, and records the diagnosis to memory. Designed for overnight model fine-tunes, CI image builds, or repeated regression suites that may fail intermittently.