Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nvidia/model-optimizer/eagle3-triagenpx skills add NVIDIA/Model-Optimizer --skill eagle3-triagegit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00073 | $0.02264 |
| Opus 5 | $0.00036 | $0.01132 |
| Sonnet 5 | $0.00015 | $0.00453 |
| Haiku 4.5 | $0.00007 | $0.00226 |
Grade A, and why
eagle3-triage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EAGLE3 Pipeline Triage
Diagnose failures in the 4-step EAGLE3 offline pipeline. This skill walks through each step, identifies the failure point, and provides actionable fixes.
Pipeline Overview
| Step | Script | Purpose | Common failure area |
|---|---|---|---|
| task_0 | common/vllm/query.sh |
Data synthesis via vLLM server | Server startup, model loading, OOM |
| task_1 | common/eagle3/dump_offline_data_vllm.sh (or _hf.sh / .sh) |
Dump hidden states | Backend selection, OOM, unsupported arch |
| task_2 | common/eagle3/train_eagle.sh |
Train EAGLE3 draft head | Dependencies, training crash, export |
| task_3 | common/specdec_bench/quick_check.sh |
Benchmark acceptance rate | Engine startup, draft model loading |
Step 0 — Locate the experiment
Ask the user for one of:
- Experiment directory (e.g., the
--job-dirpassed tolaunch.pyorslurm.py) - The model name / YAML they ran
Find recent experiments under the job directory:
ls -td experiments/cicd/cicd_* | head -10
# or wherever --job-dir was pointed
Each experiment directory contains one subdirectory per task (task_0 through task_3),
each with a log file whose name varies by launch mode (Slurm: sbatch_*.out, local
Docker: *.log).
Step 1 — Fetch logs for the failed task
Match the log files generally and read the tail of each — errors appear 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
Look for the first task with a non-zero exit code or error message.
Step 2 — Diagnose by step
task_0 failures (Data Synthesis)
How it works: Launches a vLLM OpenAI-compatible server, polls /health until ready,
then runs query.py to generate synthetic prompt/response pairs.
Output goes to /scratchspace/data/.
| Error pattern | Root cause | Fix |
|---|---|---|
| Server never becomes healthy (hangs at health check) | Model too large for allocated GPUs, or vLLM startup crash | Check BF16 weight size vs total allocated GPU memory; increase TP and/or nodes. |
CUDA out of memory during model load |
Insufficient GPU memory | Reduce --max-model-len or increase --tensor-parallel-size |
trust_remote_code error |
Model requires custom code but flag not set | Add --trust-remote-code before the -- separator in task_0 args |
| Vocab / tokenizer error | Missing tokenizer cache (e.g., GPT-OSS-20B needs TIKTOKEN_RS_CACHE_DIR) |
Set TIKTOKEN_RS_CACHE_DIR to a pre-populated cache path in the environment |
| Architecture not supported | vLLM version doesn't support this model | Try a newer vLLM container (vllm/vllm-openai:latest) |
CANCELLED ... DUE TO TIME LIMIT |
Job wall-clock limit too short | Increase Slurm --time. Note: afterany deps let task_1 still start. |
Empty /scratchspace/data/ |
query.py ran but produced no output | Check --data path exists and contains prompts. Check query.py logs. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 169 lines · 73 tokens per session scan A 05b8947a0081
eagle3-triage is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,675 stars, last pushed yesterday), licensed Apache-2.0. It adds 73 tokens to every session and 2,264 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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