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# vLLM Ascend skills
This directory contains the skills for vLLM Ascend.
Note: Please copy the skills directory `.agents/skills` to `.claude/skills` if you want to use the skills in this repo with Claude code.
## Table of Contents
- [vLLM Ascend Model Adapter Skill](#vllm-ascend-model-adapter-skill)
- [vLLM Ascend main2main Skill](#vllm-ascend-main2main-skill)
- [vLLM Ascend Release Note Writer Skill](#vllm-ascend-release-note-writer-skill)
## vLLM Ascend Model Adapter Skill
[Doc][Skill] Introduce AI-assisted model-adaptation workflow for vllm-ascend (#6731) ### What this PR does / why we need it This PR introduces the **first AI-assisted model-adaptation skill package** for `vllm-ascend`. The goal is to make model adaptation work (especially for recurring feature-request issues) **repeatable, auditable, and easier to hand off**. ### Scope in this PR This PR adds only skill/workflow assets under: - `.agents/skills/vllm-ascend-model-adapter/SKILL.md` - `.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md` - `.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md` - `.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md` ### Workflow improvements The skill standardizes: 1. **Environment assumptions** used in our Docker setup - implementation roots: `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend` - serving root: `/workspace` - model path convention: `/models/<model-name>` 2. **Validation strategy** - Stage A: fast `--load-format dummy` gate - Stage B: mandatory real-weight gate before sign-off - avoid false-ready by requiring request-level checks (not startup log only) 3. **Feature-first verification checklist** - ACLGraph / EP / flashcomm1 / MTP / multimodal - explicit `supported / unsupported / not-applicable / checkpoint-missing` outcomes 4. **Delivery contract** - minimal scoped code changes - required artifacts (Chinese report + runbook, e2e config YAML, tutorial doc) - one signed commit in delivery repo ### What this PR does NOT do - No runtime/kernel/model patch is included in this PR. - No direct model support claim is made by this PR alone. - Model-specific adaptation/fix work should be submitted in follow-up PRs using this skill as the workflow baseline. ### Why this matters for maintainers This gives the repo a shared, explicit AI-assistance protocol, so future model-adaptation PRs are easier to review, compare, and reproduce. --------- Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com> Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
2026-02-26 08:48:15 +08:00
Adapt and debug models for vLLM on Ascend NPU — covering both already-supported
architectures and new models not yet registered in vLLM.
### What it does
[Doc][Skill] Introduce AI-assisted model-adaptation workflow for vllm-ascend (#6731) ### What this PR does / why we need it This PR introduces the **first AI-assisted model-adaptation skill package** for `vllm-ascend`. The goal is to make model adaptation work (especially for recurring feature-request issues) **repeatable, auditable, and easier to hand off**. ### Scope in this PR This PR adds only skill/workflow assets under: - `.agents/skills/vllm-ascend-model-adapter/SKILL.md` - `.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md` - `.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md` - `.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md` ### Workflow improvements The skill standardizes: 1. **Environment assumptions** used in our Docker setup - implementation roots: `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend` - serving root: `/workspace` - model path convention: `/models/<model-name>` 2. **Validation strategy** - Stage A: fast `--load-format dummy` gate - Stage B: mandatory real-weight gate before sign-off - avoid false-ready by requiring request-level checks (not startup log only) 3. **Feature-first verification checklist** - ACLGraph / EP / flashcomm1 / MTP / multimodal - explicit `supported / unsupported / not-applicable / checkpoint-missing` outcomes 4. **Delivery contract** - minimal scoped code changes - required artifacts (Chinese report + runbook, e2e config YAML, tutorial doc) - one signed commit in delivery repo ### What this PR does NOT do - No runtime/kernel/model patch is included in this PR. - No direct model support claim is made by this PR alone. - Model-specific adaptation/fix work should be submitted in follow-up PRs using this skill as the workflow baseline. ### Why this matters for maintainers This gives the repo a shared, explicit AI-assistance protocol, so future model-adaptation PRs are easier to review, compare, and reproduce. --------- Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com> Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
2026-02-26 08:48:15 +08:00
This skill guides an AI agent through a deterministic workflow to:
1. Triage a model checkpoint (architecture, quant type, multimodal capability).
2. Implement minimal code changes in `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend`.
3. Validate via a two-stage gate (dummy fast gate + real-weight mandatory gate).
4. Deliver one signed commit with code, test config, and tutorial doc.
### File layout
[Doc][Skill] Introduce AI-assisted model-adaptation workflow for vllm-ascend (#6731) ### What this PR does / why we need it This PR introduces the **first AI-assisted model-adaptation skill package** for `vllm-ascend`. The goal is to make model adaptation work (especially for recurring feature-request issues) **repeatable, auditable, and easier to hand off**. ### Scope in this PR This PR adds only skill/workflow assets under: - `.agents/skills/vllm-ascend-model-adapter/SKILL.md` - `.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md` - `.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md` - `.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md` ### Workflow improvements The skill standardizes: 1. **Environment assumptions** used in our Docker setup - implementation roots: `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend` - serving root: `/workspace` - model path convention: `/models/<model-name>` 2. **Validation strategy** - Stage A: fast `--load-format dummy` gate - Stage B: mandatory real-weight gate before sign-off - avoid false-ready by requiring request-level checks (not startup log only) 3. **Feature-first verification checklist** - ACLGraph / EP / flashcomm1 / MTP / multimodal - explicit `supported / unsupported / not-applicable / checkpoint-missing` outcomes 4. **Delivery contract** - minimal scoped code changes - required artifacts (Chinese report + runbook, e2e config YAML, tutorial doc) - one signed commit in delivery repo ### What this PR does NOT do - No runtime/kernel/model patch is included in this PR. - No direct model support claim is made by this PR alone. - Model-specific adaptation/fix work should be submitted in follow-up PRs using this skill as the workflow baseline. ### Why this matters for maintainers This gives the repo a shared, explicit AI-assistance protocol, so future model-adaptation PRs are easier to review, compare, and reproduce. --------- Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com> Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
2026-02-26 08:48:15 +08:00
| File | Purpose |
| ---- | ------- |
| `SKILL.md` | Skill definition, constraints, and execution playbook |
| `references/workflow-checklist.md` | Step-by-step commands and templates |
| `references/troubleshooting.md` | Symptom-action pairs for common failures |
| `references/fp8-on-npu-lessons.md` | FP8 checkpoint handling on Ascend |
| `references/multimodal-ep-aclgraph-lessons.md` | VL, EP, and ACLGraph patterns |
| `references/deliverables.md` | Required outputs and commit discipline |
### Quick start
[Doc][Skill] Introduce AI-assisted model-adaptation workflow for vllm-ascend (#6731) ### What this PR does / why we need it This PR introduces the **first AI-assisted model-adaptation skill package** for `vllm-ascend`. The goal is to make model adaptation work (especially for recurring feature-request issues) **repeatable, auditable, and easier to hand off**. ### Scope in this PR This PR adds only skill/workflow assets under: - `.agents/skills/vllm-ascend-model-adapter/SKILL.md` - `.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md` - `.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md` - `.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md` ### Workflow improvements The skill standardizes: 1. **Environment assumptions** used in our Docker setup - implementation roots: `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend` - serving root: `/workspace` - model path convention: `/models/<model-name>` 2. **Validation strategy** - Stage A: fast `--load-format dummy` gate - Stage B: mandatory real-weight gate before sign-off - avoid false-ready by requiring request-level checks (not startup log only) 3. **Feature-first verification checklist** - ACLGraph / EP / flashcomm1 / MTP / multimodal - explicit `supported / unsupported / not-applicable / checkpoint-missing` outcomes 4. **Delivery contract** - minimal scoped code changes - required artifacts (Chinese report + runbook, e2e config YAML, tutorial doc) - one signed commit in delivery repo ### What this PR does NOT do - No runtime/kernel/model patch is included in this PR. - No direct model support claim is made by this PR alone. - Model-specific adaptation/fix work should be submitted in follow-up PRs using this skill as the workflow baseline. ### Why this matters for maintainers This gives the repo a shared, explicit AI-assistance protocol, so future model-adaptation PRs are easier to review, compare, and reproduce. --------- Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com> Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
2026-02-26 08:48:15 +08:00
1. Open a conversation with the AI agent inside the vllm-ascend dev container.
2. Invoke the skill (e.g. `/vllm-ascend-model-adapter`).
3. Provide the model path (default `/models/<model-name>`) and the originating issue number.
4. The agent follows the playbook in `SKILL.md` and produces a ready-to-merge commit.
### Key constraints
[Doc][Skill] Introduce AI-assisted model-adaptation workflow for vllm-ascend (#6731) ### What this PR does / why we need it This PR introduces the **first AI-assisted model-adaptation skill package** for `vllm-ascend`. The goal is to make model adaptation work (especially for recurring feature-request issues) **repeatable, auditable, and easier to hand off**. ### Scope in this PR This PR adds only skill/workflow assets under: - `.agents/skills/vllm-ascend-model-adapter/SKILL.md` - `.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md` - `.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md` - `.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md` ### Workflow improvements The skill standardizes: 1. **Environment assumptions** used in our Docker setup - implementation roots: `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend` - serving root: `/workspace` - model path convention: `/models/<model-name>` 2. **Validation strategy** - Stage A: fast `--load-format dummy` gate - Stage B: mandatory real-weight gate before sign-off - avoid false-ready by requiring request-level checks (not startup log only) 3. **Feature-first verification checklist** - ACLGraph / EP / flashcomm1 / MTP / multimodal - explicit `supported / unsupported / not-applicable / checkpoint-missing` outcomes 4. **Delivery contract** - minimal scoped code changes - required artifacts (Chinese report + runbook, e2e config YAML, tutorial doc) - one signed commit in delivery repo ### What this PR does NOT do - No runtime/kernel/model patch is included in this PR. - No direct model support claim is made by this PR alone. - Model-specific adaptation/fix work should be submitted in follow-up PRs using this skill as the workflow baseline. ### Why this matters for maintainers This gives the repo a shared, explicit AI-assistance protocol, so future model-adaptation PRs are easier to review, compare, and reproduce. --------- Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com> Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
2026-02-26 08:48:15 +08:00
- Never upgrade `transformers`.
- Start `vllm serve` from `/workspace` (direct command, port 8000).
- Dummy-only evidence is not sufficient — real-weight validation is mandatory.
- Final delivery is exactly one signed commit in the current repo.
### Two-stage validation
[Doc][Skill] Introduce AI-assisted model-adaptation workflow for vllm-ascend (#6731) ### What this PR does / why we need it This PR introduces the **first AI-assisted model-adaptation skill package** for `vllm-ascend`. The goal is to make model adaptation work (especially for recurring feature-request issues) **repeatable, auditable, and easier to hand off**. ### Scope in this PR This PR adds only skill/workflow assets under: - `.agents/skills/vllm-ascend-model-adapter/SKILL.md` - `.agents/skills/vllm-ascend-model-adapter/references/workflow-checklist.md` - `.agents/skills/vllm-ascend-model-adapter/references/troubleshooting.md` - `.agents/skills/vllm-ascend-model-adapter/references/multimodal-ep-aclgraph-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/fp8-on-npu-lessons.md` - `.agents/skills/vllm-ascend-model-adapter/references/deliverables.md` ### Workflow improvements The skill standardizes: 1. **Environment assumptions** used in our Docker setup - implementation roots: `/vllm-workspace/vllm` and `/vllm-workspace/vllm-ascend` - serving root: `/workspace` - model path convention: `/models/<model-name>` 2. **Validation strategy** - Stage A: fast `--load-format dummy` gate - Stage B: mandatory real-weight gate before sign-off - avoid false-ready by requiring request-level checks (not startup log only) 3. **Feature-first verification checklist** - ACLGraph / EP / flashcomm1 / MTP / multimodal - explicit `supported / unsupported / not-applicable / checkpoint-missing` outcomes 4. **Delivery contract** - minimal scoped code changes - required artifacts (Chinese report + runbook, e2e config YAML, tutorial doc) - one signed commit in delivery repo ### What this PR does NOT do - No runtime/kernel/model patch is included in this PR. - No direct model support claim is made by this PR alone. - Model-specific adaptation/fix work should be submitted in follow-up PRs using this skill as the workflow baseline. ### Why this matters for maintainers This gives the repo a shared, explicit AI-assistance protocol, so future model-adaptation PRs are easier to review, compare, and reproduce. --------- Signed-off-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com> Co-authored-by: QwertyJack <7554089+QwertyJack@users.noreply.github.com>
2026-02-26 08:48:15 +08:00
- **Stage A (dummy)**: fast architecture / operator / API path check with `--load-format dummy`.
- **Stage B (real)**: real-weight loading, fp8/quant path, KV sharding, runtime stability.
Both stages require request-level verification (`/v1/models` + at least one chat request),
not just startup success.
## vLLM Ascend main2main Skill
Migrate changes from the main vLLM repository to the vLLM Ascend repository, ensuring compatibility and performance optimizations for Ascend NPUs.
### What it does
This skill facilitates the process of:
1. Identifying changes in the main vLLM repository.
2. Applying necessary modifications for Ascend support.
3. Validating the changes in an Ascend environment.
4. Delivering a ready-to-merge commit with optimized code and configurations.
### Quick start
1. Open a conversation with the AI agent inside the vllm-ascend dev container.
2. Invoke the skill (e.g. `/main2main`).
3. The agent follows the playbook and produces a ready-to-merge commit.
## vLLM Ascend Release Note Writer Skill
You just need to say: `Please help me write a 0.13.0 release note based on commits from v0.11.0 and releases/v0.13.0`
### What it does
This skill guides you through a structured workflow to:
1. Fetch commits between two versions using the provided script.
2. Analyze and categorize each commit in a CSV workspace.
3. Draft highlights and write polished release notes.
4. Generate release notes organized by category (Features, Hardware Support, Performance, Dependencies, etc.).
### File layout
| File | Purpose |
| ---- | ------- |
| `SKILL.md` | Skill definition, workflow, and writing guidelines |
| `references/ref-past-release-notes-highlight.md` | Style and category reference for release notes |
| `scripts/fetch_commits-optimize.py` | Script to fetch commits between versions |
### Quick start
1. Open a conversation with the AI agent.
2. Invoke the skill (e.g. `/vllm-ascend-release-note-writer`).
3. Follow the workflow steps:
- Fetch commits between versions
- Analyze commits in CSV format
- Draft and edit highlights
4. Output files are saved to `vllm-ascend-release-note/output/$version`
### Key guidelines
- Use one-level headings (###) for sections in a specific order: Highlights, Features, Hardware and Operator Support, Performance, Dependencies, Deprecation & Breaking Changes, Documentation, Others.
- Focus on user-facing impact and include context for practical usage.
- Verify details by checking linked PRs (use GitHub API for descriptions if needed).
- Keep notes concise and avoid unnecessary technical details.