feat(okr30): add EAGLE3 Claude Code skills for triage, validation, and new-model support

.claude/skills/eagle3-new-model/SKILL.md

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+---
+name: eagle3-new-model
+description: >
+  Add a new model to the EAGLE3 offline pipeline. Generates an hf_offline_eagle3.yaml
+  launcher config for a new model checkpoint, choosing the right hidden state dump
+  backend (TRT-LLM / HF / vLLM) and GPU configuration.
+  Use when user wants to run EAGLE3 on a model that does not yet have a YAML in
+  tools/launcher/examples/ or asks how to configure the pipeline for a new checkpoint.
+---
+
+# EAGLE3 New Model Configuration
+
+This skill guides you through creating `tools/launcher/examples/<Org>/<Model>/hf_offline_eagle3.yaml`
+for a new model.
+
+## Step 1 — Look up the model architecture
+
+Determine these values from the HuggingFace model card, `config.json`, and vLLM docs:
+
+| Property | Where to find it |
+|---|---|
+| Total / active parameters | Model card |
+| Dense or MoE? | `config.json` → `num_experts`, `num_experts_per_tok` |
+| Attention type (MHA / GQA / MLA / SWA) | Model card |
+| Multimodal? (vision encoder) | Model card |
+| BF16 weight size (GB) | `total_params × 2 bytes` |
+| Special serving flags | vLLM docs, model README (`--trust-remote-code`, parsers) |
+
+## Step 2 — Calculate GPU requirements (OCI-HSG / GB200)
+
+OCI-HSG nodes: **4 GPUs × 192 GB HBM3e = 768 GB per node**
+
+```text
+BF16 weight size  = total_params × 2 bytes
+GPUs needed       = ceil(weight_size_GB / 192)
+nodes             = ceil(gpus_needed / 4)
+tp                = min(gpus_needed, 4)
+```
+
+| Model | Weights (BF16) | GPUs | nodes | tp |
+|---|---|---|---|---|
+| 8B dense | ~16 GB | 1 | 1 | 4 |
+| 70B dense | ~140 GB | 1 | 1 | 4 |
+| 685B MoE | ~340 GB | 2 | 1 | 4 |
+| 1T MoE | ~595 GB | 4 | 1 | 4 |
+
+## Step 3 — Choose the hidden state dump backend
+
+| Backend | Script | When to use |
+|---------|--------|-------------|
+| vLLM | `common/eagle3/dump_offline_data_vllm.sh` | Default; broad coverage via vLLM + speculators |
+| HF | `common/eagle3/dump_offline_data_hf.sh` | VLMs, custom-code models, SWA attention |
+| TRT-LLM | `common/eagle3/dump_offline_data.sh` | Pure-text models with TRT-LLM support (needs `--tp`/`--moe-ep`) |
+
+Use **HF** when the model is a VLM or uses sliding window attention (TRT-LLM does not support these).
+Use **vLLM** for everything else as the default.
+
+## Step 4 — Write the YAML
+
+Create `tools/launcher/examples/<Org>/<Model>/hf_offline_eagle3.yaml`.
+Use an existing config as a reference (e.g., `tools/launcher/examples/Qwen/Qwen3.5-35B-A3B/hf_offline_eagle3.yaml`).
+
+### Header comment
+
+```yaml
+# EAGLE3 offline speculative decoding pipeline for <org>/<model>.
+#
+# <Model> is a <size> <dense|MoE> model. <brief notes: attention type, special reqs>
+# BF16 weights ~<size> GB — fits on <N> GB200 node(s) (<N> × 192 GB).
+#
+# <Special requirements (if any)>
+#
+# 4-step pipeline:
+#   task_0: Data synthesis — query vLLM server to generate prompt samples
+#   task_1: Dump hidden states — run target model to capture hidden states
+#   task_2: Offline training — train the EAGLE3 draft head
+#   task_3: Benchmark — evaluate speculative decoding speedup via VLLM
+#
+# Usage:
+#   uv run launch.py --yaml examples/<Org>/<Model>/hf_offline_eagle3.yaml --yes
+#   uv run slurm.py --yaml modules/Model-Optimizer/tools/launcher/examples/<Org>/<Model>/hf_offline_eagle3.yaml --yes
+
+job_name: <Model>_EAGLE3_offline
+pipeline:
+  allow_to_fail: false
+  skip: false
+  note:
+
+  global_vars:
+    hf_model: /hf-local/<org>/<model>
+```
+
+### task_0 — Data synthesis (`common/vllm/query.sh`)
+
+Args before `--` go to the vLLM server; args after `--` go to `query.py`.
+
+```yaml
+  task_0:
+    script: common/vllm/query.sh
+    args:
+      - --model <<global_vars.hf_model>>
+      - --tensor-parallel-size <TP>
+      - --trust-remote-code          # add only if required
+      - --                           # separator
+      - --data /hf-local/modelopt/Speculative-Decoding-Dataset-v2-default
+      - --save /scratchspace/data
+    environment:
+      - HF_LOCAL: /hf-local
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: <nodes>
+      ntasks_per_node: 1
+      gpus_per_node: 4
+      container: vllm/vllm-openai:latest
+```
+
+### task_1 — Hidden states (vLLM backend, default)
+
+```yaml
+  task_1:
+    script: common/eagle3/dump_offline_data_vllm.sh
+    args:
+      - --input-data /scratchspace/data
+      - --output-dir /scratchspace/offline_hidden_states
+      - --max-seq-len 8192
+    environment:
+      - HF_MODEL_CKPT: <<global_vars.hf_model>>
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: <nodes>
+      ntasks_per_node: 1
+      gpus_per_node: 4
+      container: vllm/vllm-openai:latest
+```
+
+For **HF backend** (VLMs, SWA models), use `dump_offline_data_hf.sh` instead — same args, no TP flags needed.
+
+For **TRT-LLM backend**, use `dump_offline_data.sh` and add `--tp <TP>` and `--moe-ep 1` (or appropriate EP).
+
+### task_2 — Offline training (`common/eagle3/train_eagle.sh`)
+
+```yaml
+  task_2:
+    script: common/eagle3/train_eagle.sh
+    args:
+      - --config modules/Model-Optimizer/modelopt_recipes/general/speculative_decoding/eagle3.yaml
+      - model.model_name_or_path=<<global_vars.hf_model>>
+      - data.offline_data_path=/scratchspace/offline_hidden_states
+      - training.output_dir=/scratchspace/eagle3
+      - training.training_seq_len=4096
+      - training.disable_tqdm=true
+      - training.ar_validate_steps=500000
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: 1
+      ntasks_per_node: 1
+      gpus_per_node: 4
+      container: nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc10
+```
+
+> **MoE note:** For MoE models with large per-expert hidden dims, consider increasing
+> `intermediate_size` in `eagle_config.json` to match the model's `moe_intermediate_size`.
+
+### task_3 — Benchmark (`common/specdec_bench/quick_check.sh`)
+
+```yaml
+  task_3:
+    script: common/specdec_bench/quick_check.sh
+    args:
+      - --draft_model_dir /scratchspace/export
+      - --draft_length 3
+      - --output_length 4096
+      - --engine VLLM
+      - --tp_size <TP>
+      - --ep_size 1
+      - --speculative_algorithm EAGLE3
+      - --mtbench /hf-local/HuggingFaceH4/mt_bench_prompts/raw/question.jsonl
+      - --concurrency 1
+    environment:
+      - HF_LOCAL: /hf-local
+      - HF_MODEL_CKPT: <<global_vars.hf_model>>
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: <nodes>
+      ntasks_per_node: 1
+      gpus_per_node: 4
+      container: vllm/vllm-openai:latest
+```
+
+## Step 5 — Common model-specific adjustments
+
+| Situation | What to change |
+|---|---|
+| Requires `--trust-remote-code` | Add to task_0 vLLM args (before `--`) |
+| VLM / multimodal | Use `dump_offline_data_hf.sh` for task_1 |
+| Sliding window attention | Use `dump_offline_data_hf.sh` or `_vllm.sh` for task_1 |
+| MoE with large expert hidden dim | Increase `intermediate_size` in eagle_config.json |
+| Non-standard attention (MLA) | Verify `eagle_decoder_type` in the eagle3 recipe YAML |
+| Custom tokenizer (e.g., tiktoken) | Set `TIKTOKEN_RS_CACHE_DIR` env var in task_0 and task_1 |
+| NVFP4 quant model | task_0/task_3 use quant container; task_1/task_2 use BF16 base model — add `hf_model_bf16` global_var |
+| Model needs `trust_remote_code` at benchmark | Add `--trust-remote-code` to task_3 args |
+
+## Step 6 — Test with dry run
+
+Preview the resolved config before submitting:
+
+```bash
+uv run launch.py --yaml examples/<Org>/<Model>/hf_offline_eagle3.yaml --dryrun --yes -v
+```
+
+## Step 7 — Update triage chart
+
+After adding a new model, add a row to the test matrix in
+`tools/launcher/examples/EAGLE3_TRIAGE.md` with status 🔲 (not yet tested).
+Fill in results after running.

.claude/skills/eagle3-review-logs/SKILL.md

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+---
+name: eagle3-review-logs
+description: >
+  Review EAGLE3 pipeline experiment logs from the launcher's experiments/ directory.
+  Summarizes pass/fail status for all 4 tasks, diagnoses failures with root causes
+  and fixes, and flags warnings. Use when the user asks to review job logs,
+  check experiment results, or diagnose why a specific task failed.
+user_invocable: true
+---
+
+# Review EAGLE3 Experiment Logs
+
+Analyze output logs from an EAGLE3 pipeline run launched via `launch.py` or `slurm.py`.
+
+## Step 0 — Find experiment logs
+
+Locate the experiment directory. The default is `experiments/` relative to the launcher root,
+or wherever `--job-dir` was pointed.
+
+```bash
+ls -td experiments/cicd/cicd_* | head -10
+```
+
+Each experiment has one subdirectory per task (0–3). Logs are `sbatch_*.out` files inside:
+
+```bash
+find experiments/<exp_id>/ -name "sbatch_*.out" | sort
+```
+
+Do this in a single Bash call. If no experiments exist, ask the user for the directory.
+
+## Step 1 — Read all task logs
+
+Read the last 200 lines of each log in parallel. Errors appear at the end:
+
+```bash
+for f in $(find experiments/<exp_id>/ -name "sbatch_*.out" | sort); do
+  echo "=== $f ==="; tail -200 "$f"; echo
+done
+```
+
+## Step 2 — Analyze
+
+For each task log, check:
+
+- **Exit / cancellation**: `DUE TO TIME LIMIT`, `FAILED`, signal (e.g., `signal 15`)
+- **Python exceptions / tracebacks**: last exception is usually the root cause
+- **CUDA errors**: OOM, NCCL timeout
+- **Slurm state**: COMPLETED, FAILED, TIMEOUT, OUT_OF_MEMORY
+- **Success indicators**: "Saved N samples", "Successfully processed N conversations", training loss line, AR output
+
+## Step 3 — Produce report
+
+Output a structured markdown report:
+
+### Summary
+
+- Overall status: PASSED / FAILED / MIXED / PARTIAL
+- Task breakdown: e.g., task_0 TIMEOUT, task_1 FAIL, task_2 skipped, task_3 skipped
+
+### Task Results
+
+For each task (0–3):
+
+**Task N — \<name\>: PASS / FAIL / TIMEOUT**
+- Key output: (e.g., "3277/3295 samples generated" or "Script not found")
+- Error (if failed): quoted error message, max 10 lines
+- Root cause: one-line diagnosis
+- Suggested fix: actionable step
+
+### Warnings
+
+Non-fatal issues worth noting (near-OOM, tokenizer warnings, slow throughput).
+
+## Step 4 — Suggest next steps
+
+Based on results:
+
+- If a task failed due to a known issue, suggest the fix and how to re-run from that task:
+
+  ```bash
+  uv run launch.py --yaml examples/<Org>/<Model>/hf_offline_eagle3.yaml \
+      pipeline.task_0.skip=true \
+      --yes
+  ```
+
+- If the failure pattern is new (not in `tools/launcher/examples/EAGLE3_TRIAGE.md`),
+  suggest adding it to the triage chart using `/eagle3-triage` guidance.
+
+- If all tasks passed, suggest running `/eagle3-validate` to confirm AR meets threshold.
+
+## Known benign patterns (do NOT mark as failures)
+
+| Pattern | Explanation |
+|---|---|
+| vLLM server exit code 143 | SIGTERM — server was killed after queries completed. Expected. |
+| `CANCELLED AT ... DUE TO TASK FAILURE` after `exit code: 0` | Slurm cleanup of worker nodes after main task succeeded. |
+| `destroy_process_group() was not called` | Benign PyTorch shutdown warning. |
+| `tokenizer class ... not equal to the registered tokenizer class` | Harmless tokenizer mismatch warning. |