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fla-mr-readiness

Validates pull requests against CONTRIBUTING.md, test plans, and benchmark evidence requirements.

Updated Sep 11, 2026
Search Tags:#testing#code review#checklist#contribution#pull request#github#benchmarking
One-click install
npx skills add https://github.com/swiss-ai/flash-linear-attention --skill fla-mr-readiness-swiss-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fla-mr-readiness
Source: https://github.com/swiss-ai/flash-linear-attention/tree/main/.agents/skills/fla-mr-readiness
Command: npx skills add https://github.com/swiss-ai/flash-linear-attention --skill fla-mr-readiness-swiss-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Contributors to the Flash Linear Attention repository often open pull requests that miss required tests, lack benchmark evidence, or violate contribution guidelines, causing review delays and rejections. ## Core Features & Use Cases - Pre-flight Checklist: Walks through CONTRIBUTING.md compliance, change scoping, and duplicate PR detection using GitHub CLI searches. - Test Plan Enforcement: Locates dependent tests via scripts/find_dependent_tests.py and requires passing results before submission. - Benchmark Evidence Standards: Defines required performance evidence for kernel changes, including hardware, workload shape, and before/after numbers. - Use Case: Before opening a PR that modifies a Gated DeltaNet kernel, run through the checklist to confirm dependent tests pass, gather H100 benchmark numbers, and format the PR body with the required Summary, Test plan, and Benchmark sections. ## Quick Start Review my current branch changes and check whether my pull request is ready to submit according to the FLA contribution requirements.

Frequently Asked Questions about fla-mr-readiness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prepare a pull request for the flash-linear-attention repo?▼

Read CONTRIBUTING.md, confirm your change scope, search for duplicate PRs with gh pr list, run dependent tests via scripts/find_dependent_tests.py, and write a PR body with Summary, Test plan, and Benchmark sections.

What benchmark evidence is required for kernel changes in FLA?▼

Kernel changes require before/after benchmarks on the same hardware, covering dense and varlen workloads where applicable. Include workload shape, hardware model, benchmark command, and a conclusion about the performance impact.

How do I find which tests are affected by my code change?▼

Run python scripts/find_dependent_tests.py with your changed file or directory as the argument. Execute those tests locally and ensure they pass before opening the PR.

Can I commit NCU profiling files to the repository?▼

No, do not commit .ncu-rep files or raw profile dumps. Summarize profiling results in the PR body and keep the artifacts local.

Should I use torch.cuda checks directly in FLA tests?▼

No, use the device and platform wrappers from fla.utils such as device, device_platform, IS_NVIDIA, IS_AMD, and IS_INTEL. Add a small fla.utils helper if existing wrappers are insufficient.

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