DeepSpeed is a software library that helps machine-learning systems train and run deep-learning models across multiple GPUs or machines. It is used by researchers and engineers who need distributed training and inference for large models. The catalogue entries provide instructions for using DeepSpeed.
Borrowing it
Nothing to install: this file belongs to deepspeedai/DeepSpeed. 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/deepspeedai/DeepSpeed/master/.agents/skills/pr-checklist/SKILL.mdgit clone --depth 1 https://github.com/deepspeedai/DeepSpeedWrote 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/deepspeedai/deepspeed/pr-checklist)<a href="https://agentmods.dev/skills/deepspeedai/deepspeed/pr-checklist"><img src="https://agentmods.dev/badge/skills/deepspeedai/deepspeed/pr-checklist/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/deepspeedai/deepspeed/pr-checklist"><img src="https://agentmods.dev/badge/skills/deepspeedai/deepspeed/pr-checklist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00054 | $0.07189 |
| Opus 5.5 | $0.00022 | $0.02876 |
| Sonnet 5.5 | $0.00011 | $0.01438 |
| Haiku 4.5 | $0.00005 | $0.00719 |
Grade A, and why
pr-checklist 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 17d ago.
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 — 530 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Checklist
This SKILL systematically enforces DeepSpeed's code quality and contribution standards during the pull request process, to reduce the burden of manual code review on maintainers.
It assumes the repo-level rules in AGENTS.md and
CONTRIBUTING.md are already in context; this checklist adds the
review-time judgement that those rule lists do not cover.
Usage
Use this checklist to polish a change in preparation for a DeepSpeed pull request, to analyze an open pull request (as author or as reviewer), or to confirm contribution standards are met before requesting maintainer attention. Go through each item and verify it has been addressed in the diff / pull request.
This is intended to be used with a human in the loop. When running programmatically, prepare notes on every section to produce a thorough report, with a clear header summarizing findings and areas needing attention. When running interactively, present that report with a concrete list of actionable follow-ups and design decisions to make, then iterate.
Mark each item OK, Needs work, or N/A — and for anything that cannot be verified from the repo alone (hardware runs, convergence results, upstream issue context), say so explicitly rather than assuming it passed. A silent omission reads as a pass and is worse than a flagged gap.
Re-review
To update a PR following a review, read all comments and threads on the PR. For each concrete concern, work with your human operator to decide a plan of action: either a direct reply, or a change plus an acknowledging comment. Keep the PR description fresh and all documentation (including code comments and tutorials) in the change up-to-date.
gh pr view --comments does not include inline review comments. Set pr to the PR number, then
fetch all pages of conversation comments, review submissions, and inline comments (including
replies):
gh api --paginate "repos/deepspeedai/DeepSpeed/issues/$pr/comments"
gh api --paginate "repos/deepspeedai/DeepSpeed/pulls/$pr/reviews"
gh api --paginate "repos/deepspeedai/DeepSpeed/pulls/$pr/comments"
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.
- 17d ago First seen · 530 lines · 54 tokens per session scan A 1b86df79058f
pr-checklist is a skill published in the GitHub repository deepspeedai/DeepSpeed (43,271 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 7,189 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 2026-10-12.
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