Eagle3 Review Logs Agent Skill

> 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.

820 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

3381

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/NVIDIA/Model-Optimizer --skill eagle3-review-logs

The instruction itself

10 sections, as written by the author

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.

ls -td experiments/cicd/cicd_* | head -10

If no experiments exist, ask the user for the directory.

Step 1 — Read all task logs

Each experiment has one subdirectory per task (0–3). Log filenames vary by launch mode

(Slurm writes sbatch_*.out, local Docker writes *.log), so match log files generally and

read the tail of each in a single Bash call — errors surface at the end:

find experiments/<exp_id>/ -type f \( -name '*.out' -o -name '*.log' \) | sort | while read -r f; 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:

  uv run launch.py --yaml examples/<Org>/<Model>/hf_offline_eagle3.yaml \
      pipeline.task_0.skip=true \
      --yes

If the failure pattern looks new, suggest capturing it in the team's internal triage

tracker, and use /eagle3-triage for a deeper diagnosis.

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. |

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17k tokens

How to use it

Copy the folder

Take nvidia/eagle3-review-logs from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.