Find the best CLAUDE.md, AGENTS.md and Claude skills

vllm-projectAGENTS.md

semantic-router

vllm-project/semantic-router/AGENTS.md

vLLM Semantic Router is an Envoy ExtProc router for LLM inference. A public entrypoint resolves to an isolated recipe; its signals and projections feed a decision and algorithm, which select a backend and recipe-scoped plugins. - Read the nearest AGENTS.md only for directories you change. - Use tools/agent/docs/change-surfaces.md when a user-visible router contract crosses configuration, runtime, deployment, or documentation. - tools/agent/domains.yaml is the single changed-path registry for ownership, minimum checks, CI jobs, images, and E2E profiles. Use make harness-check after…

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vllm-projectCopilot instructions

semantic-router

vllm-project/semantic-router/.github/copilot-instructions.md

Read AGENTS.md and the nearest local AGENTS.md for changed directories. Use make impact CHANGED_FILES="..." for ownership and candidate checks, then make check CHANGED_FILES="..." for the daily deterministic gate. Select integration or E2E explicitly with make verify DOMAIN=... or make verify PROFILE=...; use make harness-check for harness/workflow edits. Prioritize correctness, security, public contracts, missing behavior tests, and real dependency boundaries. Numeric size metrics are advisory: recommend an extraction only when it improves ownership, coupling, or testability. Behavior-visible routing, startup, config, Docker,…

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vllm-projectAGENTS.md

aibrix

vllm-project/aibrix/AGENTS.md

AIBrix is a Kubernetes-native platform for scalable GenAI inference infrastructure. - Understand the problem and define success before making a non-trivial change. Ground decisions in existing implementations, tests, and verified evidence; do not infer behavior from filenames. - Make the smallest coherent change. Preserve unrelated work and existing conventions; avoid speculative abstractions and unrelated cleanup. - Treat public contracts and compatibility as design constraints. Keep API definitions declarative and policy in the layer that owns it. Preserve API, HTTP, CLI,…

vllm-projectSkill

contribute-research

vllm-project/llm-compressor/.agents/skills/contribute-research/SKILL.md

Create, update, or review LLM Compressor Open Research Hub entries in docs/research from experiment results, logs, or issue discussions.

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vllm-projectSkill

create-tiny-model

vllm-project/llm-compressor/.agents/skills/create-tiny-model/SKILL.md

Create and manage tiny models for testing and development. Includes utilities for saving tiny models, inspecting tensors, and finetuning workflows.

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vllm-projectAGENTS.md

vllm-ascend

vllm-project/vllm-ascend/AGENTS.md

This document provides instructions for contributors to the vLLM Ascend project. Please read and follow these guidelines to ensure code quality, maintainability, and consistency. Read linked documentation when it applies to the current task, using the relevant sections. Continue authorized local implementation and validation while preparing the evidence needed for PR review. Define environment variables in the centralized env_variables dictionary in vllm_ascend/envs.py. Requirements: Never: Hardcode environment variable names throughout the codebase. Reference them from the central module using from vllm_ascend…

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vllm-projectSkill

vllm-ascend-model-adapter

vllm-project/vllm-ascend/.agents/skills/vllm-ascend-model-adapter/SKILL.md

Adapt and debug existing or new models for vLLM on Ascend NPU. Implement in /vllm-workspace/vllm and /vllm-workspace/vllm-ascend, validate via direct vllm serve from /workspace, and deliver one signed commit in the current repo.

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vllm-projectReads credentialsSkill

vllm-ascend-release

vllm-project/vllm-ascend/.agents/skills/vllm-ascend-release/SKILL.md

End-to-end release management skill for vLLM Ascend. Creates release checklist issues, triages critical bugs, audits test coverage, aggregates release note fragments, and guides through the complete release process.

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vllm-projectCLAUDE.md

vllm-ascend

vllm-project/vllm-ascend/CLAUDE.md

IMPORTANT: Ensure you've thoroughly reviewed the AGENTS.md file before beginning any work.

vllm-projectAGENTS.md

vllm-metal

vllm-project/vllm-metal/AGENTS.md

Follow the contributor guide for setup and general checks. For Omni additions, follow the model ownership and layout. Before opening a PR, search open PRs by topic. When working from an issue, read its comments and search for PRs referencing it: Do not open a duplicate PR. Explain any materially different approach in the PR description. Run checks relevant to the change. Report the commands, results, and any checks you could not run in the PR description. Extend related test…

vllm-projectAGENTS.md

guidellm

vllm-project/guidellm/AGENTS.md

This file provides agents and LLMs with guidance on development practices within the GuideLLM repository. NOTE TO AI: This file is human maintained and SHALL NOT be edited by agents or any LLM. NOTE TO HUMANS: This file should be kept brief as it is loaded into every AI context. IMPORTANT: tox should be used before attempting to run individual tools. IMPORTANT: For every command uv run must be prepended to the command. Use these markers to categorize and run…

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vllm-projectSkill

guidellm-review-translation

vllm-project/guidellm/.agents/skills/guidellm-review-translation/SKILL.md

Review a GuideLLM translation pull request by number, detect its target languages, and compare translated documentation in any language with the English source. Produce an English report for English-speaking reviewers, noting minor differences but recommending edits only for major errors or project Code of Conduct violations.

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vllm-projectSkill

guidellm-weekly-summary

vllm-project/guidellm/.agents/skills/guidellm-weekly-summary/SKILL.md

Summarize GuideLLM team activity from GitHub PRs and issues over the past week into a concise, externally shareable nested-list update. Use when the user asks for a weekly summary, team activity update, status digest, or changelog-style overview of vllm-project/guidellm.

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vllm-projectAGENTS.md

recipes

vllm-project/recipes/AGENTS.md

No summary in the file. Open it to read it.

vllm-projectSkill

add-recipe

vllm-project/recipes/.agents/skills/add-recipe/SKILL.md

Use when the user asks to add, update, validate, or publish a vLLM model recipe — in vllm-project/vllm's recipes/ directory, another source repo, or vllm-project/recipes. Covers canonical Hugging Face identity, source/image requirements, checkpoint staging, hardware evidence, YAML-to-UI relationships, tuned configurations such as InferenceX lanes (presets), configuration notes and troubleshooting, command validation, JSON generation, and staging the reviewed files.

vllm-projectCLAUDE.md

recipes

vllm-project/recipes/CLAUDE.md

Two overlapping things live here: The site is the canonical surface. New work goes into YAMLs + the Next.js app; the .md files are historical sources for the migrated YAMLs. There is no unit-test suite. The build-recipes-api script is the de-facto validator: if it prints ✓ JSON API: N models, <strategy-count> strategies the YAML set is internally consistent. Dev-server convention: don't kill or restart the dev server as part of a task, and don't run pnpm build for each change.…

vllm-projectAGENTS.md

speculators

vllm-project/speculators/AGENTS.md

Read CONTRIBUTING.md before contributing. Install the git hooks once per clone, after installing the [dev] extras: Fill in the PR template's Purpose and Tests sections (see PULL_REQUEST_TEMPLATE.md). A reviewer should get the point in under 30 seconds: Training, eval, and vLLM-launch scripts write reproducibility artifacts so runs can be recreated later. Always pass --provenance-dir when running scripts/launch_vllm.py — point it at the associated run's output/checkpoint directory (e.g. --provenance-dir <output_dir>) so the vLLM provenance is co-located with the training/eval it belongs…

vllm-projectSkill

pr-review

vllm-project/speculators/.claude/skills/pr-review/SKILL.md

Review a GitHub PR with design-first analysis, posted as a GitHub review.

vllm-projectSkill

speculators-evaluation

vllm-project/speculators/.claude/skills/speculators-evaluation/SKILL.md

Evaluate draft models produced for vllm-project/speculators by serving them with vLLM, measuring acceptance and speedup with AIPerf, comparing against MTP or the base model, and preparing model-card-ready results. Use for drafter acceptance rates, acceptance length, long-context behavior, performance sweeps, plots, and evaluation reports. Does not train models.

vllm-projectSkill

precheck-pr

vllm-project/router/.agents/skills/precheck-pr/SKILL.md

Contributor self-review for vllm-project/router. Use before implementing a contribution and before opening a PR to establish human intent, decide whether a PR is needed, and verify scoped evidence. Does not post or change files.