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-validatenpx skills add NVIDIA/Model-Optimizer --skill eagle3-validategit 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 | $0.00062 | $0.01014 |
| Opus 5 | $0.00031 | $0.00507 |
| Sonnet 5 | $0.00012 | $0.00203 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
eagle3-validate 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 2d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EAGLE3 Pipeline Validation
Verify that an EAGLE3 pipeline run completed successfully and meets quality criteria.
Step 0 — Identify the experiment
Find the most recent experiment directory (or ask the user for the path):
ls -td experiments/cicd/cicd_* | head -5
Each experiment directory has one subdirectory per task (numbered 0–3), each containing a
log file whose name varies by launch mode (Slurm: sbatch_*.out, local Docker: *.log).
Step 1 — Check task outcomes
Match the log files generally and read the tail of each:
find experiments/<exp_id>/ -type f \( -name '*.out' -o -name '*.log' \) | sort | while read -r f; do
echo "=== $f ==="; tail -50 "$f"; echo
done
All 4 tasks must complete without error. Look for:
exit code: 0or no error — successDUE TO TIME LIMIT— timeoutFAILED/signal/ exception traceback — failure
If any task failed, suggest running /eagle3-triage instead.
Step 2 — Verify artifacts exist
Check each step produced the expected output (artifacts live on the cluster at /scratchspace/).
Confirm via log messages:
| Step | Expected log evidence | Artifact |
|---|---|---|
| task_0 | "Saved N samples" or progress bar completing | /scratchspace/data/*.jsonl |
| task_1 | "Successfully processed N conversations" | /scratchspace/offline_hidden_states/*.pt |
| task_2 | Training loss decreasing, "export complete" | /scratchspace/eagle3/model.safetensors, /scratchspace/export/ |
| task_3 | Average Acceptance Length ... ratio: X.XX |
JSON result files |
Step 3 — Check acceptance rate
In the task_3 log, find:
Average Acceptance Length {'accept': X, 'count': Y, 'ratio': Z.ZZ}
The ratio field is the acceptance rate (AR).
| Criterion | Threshold | Status |
|---|---|---|
| AR (MT-Bench) | >= 2.1 | PASS / FAIL |
If the log shows AR ... < lower bound, the run already triggered a threshold failure (exit code 1).
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.
- 2d ago First seen · 120 lines · 62 tokens per session scan A 2034017ff352
eagle3-validate is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,675 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 1,014 once invoked, about $0.0003 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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