[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>

.agents/skills/vllm-ascend-model-adapter/references/deliverables.md

@@ -0,0 +1,47 @@
+# Deliverables
+
+## Required outputs in current repo
+
+1. One final signed commit (`git commit -sm ...`) containing the adaptation changes.
+2. Chinese analysis report（精简但完整）:
+   - model architecture summary
+   - incompatibility root causes
+   - code changes and rationale
+   - startup and inference verification evidence
+   - feature status matrix（supported / unsupported / checkpoint-missing / not-applicable）
+   - max model len: config theoretical vs runtime practical
+   - dummy-vs-real validation matrix（what dummy proved / what only real proved）
+   - false-ready cases and final resolution path（if any）
+   - fallback ladder evidence（which fallback was tried, what changed）
+3. Chinese compact runbook:
+   - how to start server in `/workspace` (direct command, default `:8000`)
+   - how to run OpenAI-compatible validation
+   - optional eager fallback command
+   - optional `TORCHDYNAMO_DISABLE=1` fallback command (if relevant)
+4. Test config YAML at `tests/e2e/models/configs/<ModelName>.yaml` — must include `model_name`, `hardware`, `tasks` with accuracy metrics (name + value), and `num_fewshot`. Use accuracy results from evaluation to populate metric values. Follow the schema of existing configs (e.g. `Qwen3-8B.yaml`).
+5. Tutorial doc at `docs/source/tutorials/models/<ModelName>.md` — must follow the standard template: Introduction, Supported Features, Environment Preparation (with docker tabs for A2/A3), Deployment (with serve script), Functional Verification (with curl example), Accuracy Evaluation, Performance. Fill in model-specific details (HF path, hardware requirements, TP size, max-model-len, served-model-name, sample curl, accuracy table).
+6. Post SKILL.md content or AI-assisted workflow summary as a comment on the originating GitHub issue.
+
+## Commit discipline
+
+- Keep one signed commit for code changes in the current working repo.
+- If implementation occurred in `/vllm-workspace/*`, backport minimal final diff to current repo before commit.
+- Keep diff scoped to target model adaptation.
+
+## Validation discipline
+
+- Always provide log file paths for key claims.
+- Keep docs synchronized with latest successful test mode (do not leave stale command variants as default).
+- Final report must include pass/fail reason for each key feature attempt: ACLGraph / EP / flashcomm1 / MTP / multimodal.
+- EP and flashcomm1 are MoE-only checks; for non-MoE models mark as not-applicable with evidence.
+- Final report should include baseline capacity result (`128k + bs16`) or explicit reason if not feasible.
+- Dummy-first can be used to speed up iterations, but real-weight gate is mandatory before final sign-off.
+- Startup-only evidence is insufficient; include first-request smoke results.
+
+## Suggested final response structure
+
+- What changed
+- What went well / what went wrong
+- Validation performed
+- Commit hash and changed files
+- Optional next step