fxa-test-draft
Drafts Jest tests for changed code. Defaults to staged/unstaged changes or the most recent commit. Output is a starting point for review, not final.
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Drafts Jest tests for changed code. Defaults to staged/unstaged changes or the most recent commit. Output is a starting point for review, not final.
Validates that Jest tests in a given file pass both as a full suite and individually in isolation, catching hidden order dependencies and shared mutable state.
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level. Use when you need layer timings, anchor-kernel boundaries, representative kernel flows, or Perfetto time ranges.
Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, TokenSpeed, or another serving framework. Use when a user wants to find the best deployment command for one model across multiple serving frameworks under the same workload, GPU budget, and latency SLA.
Unified LLM torch-profiler triage skill for `sglang`, `vllm`, `TensorRT-LLM`, and `TokenSpeed`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server when supported and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.
Perform SGLang code review in the style of human maintainers by consulting the full non-agent PR review episode corpus from project start through the latest refresh (June 2026), including inline review threads, top-level PR comments, review submissions, original multilingual text, and multi-round discussions. Use when reviewing SGLang PRs, diffs, patches, or local changes for correctness, tests, performance, GPU/runtime risks, API compatibility, and maintainability.
Run an autonomous Humanize-governed SGLang SOTA performance loop for one LLM model: first perform a fixed fair SGLang benchmark against the requested comparison framework set, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches SGLang code, optionally uses ncu-report-skill for kernel evidence, and revalidates until SGLang matches or beats the best observed requested framework under the same workload and SLA.
Write CI-executable acceptance/integration tests for protocol or end-to-end changes (TR-F022). Use whenever a milestone subtask needs CI-backed acceptance, covering test/integration integration tests and unit tests.
Unified conventions for writing Go unit/integration tests (TR-F022). Use when adding tests for any Go code change.