Molt is a PyTorch-based reinforcement-learning framework for training software agents, including multimodal and multi-turn agents. It is intended for research using Ray for orchestration, vLLM for model rollouts, and NVIDIA AutoModel with FSDP2 for training.
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 instructions/nvidia-nemo/labs-molt/agents-mdgit clone --depth 1 https://github.com/NVIDIA-NeMo/labs-moltWrote 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/instructions/nvidia-nemo/labs-molt/agents-md)<a href="https://agentmods.dev/instructions/nvidia-nemo/labs-molt/agents-md"><img src="https://agentmods.dev/badge/instructions/nvidia-nemo/labs-molt/agents-md.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00324 | $0.00324 |
| Opus 5 | $0.00162 | $0.00162 |
| Sonnet 5 | $0.00065 | $0.00065 |
| Haiku 4.5 | $0.00032 | $0.00032 |
Grade A, and why
labs-molt AGENTS.md 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 5d 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.
What it actually says
Molt — working rules for AI assistants
Code standards (hard rules, not preferences)
The priority order is explicit: human readability comes first; coding-agent traceability is the minimum gate. A human should understand code in one pass, and an agent must be able to trace a feature from CLI flag to executed branch, tensor/record, metric, and test without reconstructing hidden control flow.
- Over-complex or hard-to-follow code is a bug, not a style issue.
- Reduce complexity and line count — prefer deleting code over adding it.
- No over-encapsulation: a helper needs 3+ real call sites AND nontrivial logic; never wrap trivial code or add classes/files for one call site.
- Do NOT remove features, performance knobs, or observability in the name of simplicity — knobs default ON stay ON.
- A "bug" that cannot trigger under the shipped recipes is not worth fixing.
Details: .claude/skills/simplicity-first (invoke before any code change).
Comments
Concise "why" only, 2-4 lines, written for an external reader: no job ids, commit hashes, single-run metrics, or internal cluster paths; keep upstream issue/PR links.
Workflow
- Reviews report findings only; fixes ship as one minimal diff per issue after approval.
- Verify with
python -m compileall -q molt examples/python testsandpython -m pytest -q; shell scripts withbash -n.
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.
- 5d ago First seen · 32 lines · 324 tokens per session scan A bd49158685de
labs-molt AGENTS.md is an instructions file published in the GitHub repository NVIDIA-NeMo/labs-molt (1,012 stars, last pushed 2d ago), licensed Apache-2.0. It adds 324 tokens to every session, about $0.0016 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.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.