Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NVIDIA-NeMo/Megatron-Bridgenpx agentmods add skills/nvidia-nemo/megatron-bridge/verl-e2e-testingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/verl-e2e-testing)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/verl-e2e-testing"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/verl-e2e-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/verl-e2e-testing"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/verl-e2e-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 343 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Rogue Agent · line 433 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Agent Snooping · line 444 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What 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 5.1 | $0.00068 | $0.05911 |
| Opus 5.5 | $0.00027 | $0.02364 |
| Sonnet 5.5 | $0.00014 | $0.01182 |
| Haiku 4.5 | $0.00007 | $0.00591 |
Grade A, and why
verl-e2e-testing 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 1mo 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 — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
verl E2E Testing
Validate a Megatron-Bridge change through verl's Megatron backend. This catches integration issues that Bridge-only tests miss: provider/configuration finalization, HF import through Bridge, PEFT wrapping, DDP wrapping, optimizer setup, rollout/ref wiring, and checkpoint ownership by an external RL loop.
Use this as an external compatibility smoke test after the relevant Bridge unit and functional tests are green.
This is not a replacement for Bridge correctness tests. The default verl run proves that the changed Bridge path can survive an external RL training loop; numerical parity, import/export fidelity, and feature-specific behavior still come from focused Bridge tests.
Scope
Think in coverage levels. Start with Level 0 and add only the levels justified by the change.
| Level | Required when | What it proves |
|---|---|---|
| 0: LoRA + DDP smoke | Any Bridge change that claims verl compatibility | verl can import the local Bridge checkout, apply PEFT, wrap with Megatron DDP, build optimizer state, run rollout/ref/critic wiring, and finish one training step |
| 1: Save/resume | PEFT, checkpointing, HF export, adapter export, optimizer state, or resume behavior changed | verl-owned checkpoint scheduling can save and reload Bridge-built model state |
| 2: Parallelism stress | Provider finalization, mpu-derived settings, TP/PP/CP/EP, sequence parallel, or dispatcher behavior changed | provider settings remain correct under non-trivial Megatron parallel state |
| 3: Optional Megatron-FSDP | Only when downstream explicitly asks for verl Megatron-FSDP coverage or the change directly touches that integration path | the same provider works when verl selects Megatron-FSDP instead of DDP |
| 4: Feature-specific e2e | The change depends on runtime behavior not exercised by the default text-only smoke | the requested feature is activated by a targeted verl command or script |
| 5: Convergence / learning signal | Optimizer, scheduler, loss, reward, PEFT trainability, gradient flow, or training stability changed | metrics move in the expected direction over a short run and do not silently produce zero/NaN/unstable updates |
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.
- 1mo ago First seen · 510 lines · 68 tokens per session scan A e91d417301ea
verl-e2e-testing is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (970 stars, last pushed today), licensed Apache-2.0. It adds 68 tokens to every session and 5,911 once invoked, about $0.0003 per session on Opus 5.5. 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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