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.
Borrowing it
Nothing to install: this file belongs to NVIDIA-NeMo/labs-molt. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NVIDIA-NeMo/labs-molt/main/.agents/skills/simplicity-first/SKILL.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/skills/nvidia-nemo/labs-molt/simplicity-first)<a href="https://agentmods.dev/skills/nvidia-nemo/labs-molt/simplicity-first"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/labs-molt/simplicity-first/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/labs-molt/simplicity-first"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/labs-molt/simplicity-first.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.00629 |
| Opus 5.5 | $0.00020 | $0.00252 |
| Sonnet 5.5 | $0.00010 | $0.00126 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
simplicity-first 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 2mo 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Simplicity is the first principle
Human readability comes first; coding-agent traceability is the minimum gate. A human should understand the code in one pass. An agent must at least be able to trace a feature from CLI flag to executed branch, tensor/record, metric, and test without reconstructing hidden control flow. Every rule below is an instance of that ordering. These are hard correctness rules, not style preferences. A violation is a bug and must be fixed before the change ships.
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Code a human can't follow at a glance is a bug. If a reviewer can't read a function top-to-bottom in one pass, restructure or delete it. Nesting, indirection, and clever constructs count against correctness — cleverness that costs comprehension is a defect, whatever it saves.
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Too much / redundant code is a bug. Solve the problem in the fewest lines that stay readable. Prefer deleting code over adding it. A fix that adds more than ~20 lines for a problem statable in one sentence is suspect — find the smaller fix first.
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Simplicity is the core engineering metric. When two designs both work, ship the one with less code, fewer concepts, fewer files. Never add config, record types, or return-shape changes "for the future".
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No over-encapsulation. No new class / dataclass / helper / module for a single call site. A helper needs 3+ real call sites AND nontrivial logic — otherwise inline it. Never wrap trivial code. Never change a function signature or return shape to thread data that only one caller needs.
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Simplicity is not deletion of capability. Features, performance knobs, and observability are intentional — do not remove them in the name of simplicity. Knobs default ON stay ON. Simplify the implementation, keep the behavior surface.
Checklist before finishing any change
- Would a human reading this cold understand it in one pass? That is the gate.
- Could this diff be half the size? If unsure, make it smaller.
- Any new class or file? Justify each with 3+ call sites, or delete it.
- Any signature / return-shape change? Verify every caller genuinely needs it.
- Comments: concise "why" only, 2-4 lines max, written for an external reader — no job ids, commit hashes, single-run metrics, or internal paths; keep upstream issue/PR links.
- One problem = one minimal diff. Do not batch unrelated "improvements".
- A "bug" that cannot trigger under the real recipes is not worth fixing.
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.
- 2mo ago First seen · 50 lines · 51 tokens per session scan A bf4cd934475f
simplicity-first is a skill published in the GitHub repository NVIDIA-NeMo/labs-molt (1,209 stars, last pushed 2d ago), licensed Apache-2.0. It adds 51 tokens to every session and 629 once invoked, about $0.0002 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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