llm-pipeline-analysis
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level. Use when you need layer timings, anchor-kernel…
Build or audit an evidence-driven SGLang Day-0 support program for a new LLM, VLM, MoE, hybrid-attention, or speculative-decoding model. Use when Codex needs to map a model architecture into SGLang runtime work, design a public support PR DAG, create validation and release
$ npx -y skills add BBuf/AI-Infra-Auto-Driven-SKILLS --skill sglang-model-day0-support --agent claude-codeHow it fires
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/sglang-model-day0-supportContext preview
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Build or audit an evidence-driven SGLang Day-0 support program for a new LLM, VLM, MoE, hybrid-attention, or speculative-decoding model. Use when Codex needs to map a model architecture into SGLang runtime work, design a public support PR DAG, create validation and release
name: sglang-model-day0-support description: Build or audit an evidence-driven SGLang Day-0 support program for a new LLM, VLM, MoE, hybrid-attention, or speculative-decoding model. Use when Codex needs to map a model architecture into SGLang runtime work, design a public support PR DAG, create validation and release gates, sanitize private development evidence, distinguish Day-0 requirements from later fixes or optimizations, or review whether an existing model-support PR is release-ready.
Turn a model release into a reviewable SGLang support program. Produce evidence, implementation boundaries, validation gates, and public release artifacts—not only a launch command.
Use the maintained [source contracts](../../../docs/upstream-source-contracts.md) when applying historical evidence to a current branch. The review/history corpora retain their own capture dates; they do not certify today's dispatch or numerical defaults. For kernel replacements, verify that the real model executes the candidate before treating end-to-end tests as coverage.
1. Read [day0-contract.md](references/day0-contract.md) completely. 2. Copy `assets/day0-bundle/` into the active model-support workspace. 3. Lock immutable model, weight, tokenizer/processor, SGLang, dependency, and image revisions before describing support. 4. Read [evidence-audit.md](references/evidence-audit.md) before citing PRs. 5. If any input is non-public, read [sanitization.md](references/sanitization.md) before writing public output. 6. Read only the relevant case study:
image-first release locks, linear extraction stacks and equivalence proofs.
KDA/MLA, VLM, DSpark, ReplaySSM, DCP, or dense internal-to-public delivery.
compressed/sparse attention, SWA, mHC, MTP, large rebases, or staged cookbook/image/mainline delivery.
Do not implement model code until the scope contract, architecture gap map, PR DAG, and minimum validation matrix agree.
Fill `scope-contract.md` and `release-lock.md`.
unpinned object-store paths as release evidence.
performance lanes.
image layer, or documented external prerequisite.
implicit pass.
Compare the public model configuration/reference implementation with the locked SGLang source. Inspect each capability family in the Day-0 contract.
For each gap, record:
Use public model-family PR history when available. Do not infer support from a shared architecture name alone.
Assign exactly one class:
satisfy an announced release lane.
previously claimed behavior.
a negative result.
If one patch mixes classes, split it or explain why atomic review is impossible. Never promote an experiment merely because it exists on a development branch.
Fill `pr-dag.md`. Prefer reviewable PRs with explicit dependencies:
1. shared public infrastructure; 2. model config, loader, and eager correctness spine; 3. state/cache and required parallel composition; 4. protocol, speculative, and multimodal surfaces; 5. platform-specific kernels or backends; 6. images, cookbook, tests, and release wiring; 7. one umbrella integration PR only when the release branch requires it.
Require each node to name its merge gate, fallback, tests, and public evidence. Avoid copying an entire development branch when a smaller auditable diff is possible.
Fill `validation-matrix.md`. Do not construct the full Cartesian product.
Always include:
behavior required by the model;
Add risk pairs where two features rewrite the same state, tokens, graph, collective, or transfer layout. Typical examples are speculative decoding × recurrent state, DCP × cache ownership, VLM × PD/EPD, DP attention × MoE collectives, and CUDA Graphs × alternative streams.
Execute the gates from the Day-0 contract in order:
1. source; 2. load; 3. protocol; 4. state; 5. topology; 6. quality/performance; 7. release.
For every pass, retain the command, revision, hardware, result, and limitation. For every failure, record whether it blocks Day-0, narrows the claim, or moves to the follow-up ledger.
Do not use server startup as evidence for out
**Agent-ready skills for LLM serving benchmarks, profiler triage, capacity planning, model Day-0 support, code review, incident triage, and model PR history across SGLang, vLLM, TensorRT-LLM, and TokenSpeed — plus SGLang PR and article explainer videos.**
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level. Use when you need layer timings, anchor-kernel…
Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, TokenSpeed, or another serving…
Parse SGLang/vLLM startup logs to explain GPU memory use and request capacity. Use for KV cache budget, mem-fraction-static…
Analyze existing Torch Profiler Chrome traces or capture from SGLang, vLLM, TensorRT-LLM and TokenSpeed servers. Return kernel…
Return public original model architecture diagrams for user-specified LLM, VLM, MoE, diffusion, OCR, and SGLang/sgl-cookbook…
Build an operator-level compute template for an LLM and estimate FLOPs/MFU for a serving shape. Use when you need tensor shapes,…