BBuf/sglang-humanize-review

SGLang Humanize Review

Overview

Use this skill when the user asks for a human-style SGLang code review or wants review feedback that resembles SGLang maintainers instead of generic linting.

The bundled corpus was collected from sgl-project/sglang PRs created in 2024 and 2025, excluding PRs authored by bots or obvious coding-agent accounts. It contains 10,959 inline review threads and 18,266 human reviewer comments. Each thread preserves:

• PR metadata
• file path and code language
• GitHub diff_hunk code context
• original reviewer comment text
• replies grouped into the same review discussion
• original comment language, including non-English and CJK text

Read references/corpus-summary.md first for coverage, counts, top paths, and category distribution. Do not load the gzip corpus directly into context; query it with the helper script.

Corpus Tools

Search the corpus by topic, path, category, or reviewer:

python3 skills/sglang-humanize-review/scripts/query_sglang_review_corpus.py \
  --query cuda --limit 5

python3 skills/sglang-humanize-review/scripts/query_sglang_review_corpus.py \
  --path python/sglang/srt --category correctness --limit 8

python3 skills/sglang-humanize-review/scripts/query_sglang_review_corpus.py \
  --query server_args --format jsonl --limit 3

The full corpus is:

references/sglang-review-corpus-2024-2025.jsonl.gz

Regenerate it only when the user asks to refresh the evidence:

python3 skills/sglang-humanize-review/scripts/collect_sglang_review_corpus.py \
  --repo sgl-project/sglang \
  --start-year 2024 \
  --end-year 2025 \
  --out-dir skills/sglang-humanize-review/references

Review Workflow

1. Inspect the actual diff first.
   • Use git diff, gh pr diff, or the patch supplied by the user.
   • Identify changed SGLang subsystems: server args, scheduler, memory/cache, model runner, attention backend, quantization, kernels, OpenAI API, metrics, docs, or tests.
2. Read references/corpus-summary.md.
   • Note top review surfaces and categories that overlap with the diff.
   • Preserve the original language of any relevant corpus examples; do not translate user-facing comments unless the user asks.
3. Query similar review threads.
   • Search by path first for touched SGLang modules.
   • Search by risk keyword next, for example cuda, kv cache, server_args, openai, logprob, tp, dp, eagle, fp8, benchmark, or pytest.
   • Prefer evidence from the same subsystem over broad keyword matches.
4. Produce a code-review response.
   • Lead with concrete findings ordered by severity.
   • Include file and line references from the reviewed diff.
   • Explain the failure mode, not just the preferred style.
   • Suggest a fix or validation step when the issue is actionable.
   • Keep nits separate from correctness, performance, or compatibility risks.
5. If no issue is found, say so clearly.
   • Mention the main residual risk and the test or benchmark coverage that would increase confidence.

SGLang Review Heuristics From The Corpus

Prioritize these risks because they recur heavily in the 2024-2025 human review threads:

• Model and quantization behavior: model config drift, tokenizer assumptions, FP8/INT4 quantization paths, MoE routing, speculative decoding, and attention backend compatibility.
• Correctness before style: edge cases, failed assertions, unexpected error codes, shape/dtype mismatches, state cleanup, and silent behavior changes.
• GPU and kernel paths: CUDA graph capture, Triton/CUDA kernels, FlashInfer and FlashAttention behavior, launch conditions, SM compatibility, and fallback behavior.
• Server API compatibility: OpenAI-compatible request/response shapes, server_args, CLI defaults, endpoint behavior, streaming, and backward compatibility.
• Memory and cache lifecycle: KV cache accounting, radix cache resets, memory pool ownership, eviction, fragmentation, and OOM behavior.
• Distributed runtime: TP/DP/PP/EP rank assumptions, NCCL paths, synchronization, worker state, race conditions, and hang risk.
• Tests and benchmarks: ask for targeted tests when behavior changes, and ask for benchmark evidence when a change claims performance or touches a hot path.
• Docs and examples: keep docs aligned with CLI defaults, endpoint names, model support, install steps, and version-specific behavior.
• Observability: review metrics, logs, warning levels, traceability, and error messages when operational behavior changes.

Review Style

Mirror human SGLang review habits:

• Be terse but specific.
• Prefer a question when intent is ambiguous.
• Call out production-facing behavior changes explicitly.
• Do not invent a corpus precedent; query the corpus when using it as evidence.
• Keep multilingual comments intact. If a relevant thread is Chinese or another language, use it as-is for evidence and answer in the user's language unless the user asks otherwise.
• Avoid cargo-culting old comments. Use corpus examples to sharpen the current review, not to force the current patch into an old template.

Output Contract

For a normal review, return:

• Findings first, ordered by severity, with file/line references.
• Open questions or assumptions.
• Test or benchmark gaps.
• A short summary only after findings.

For a review-prep pass before the user opens a PR, return:

• likely reviewer concerns
• missing tests or benchmark evidence
• suggested patch cleanup
• corpus queries used

For a corpus-backed explanation, include the query terms and summarize the matched review behavior without dumping long comment bodies.