AI Research Skills Library is a collection of reusable instructions that guide AI agents through research and machine-learning engineering tasks, from finding ideas and writing papers to training, evaluation, and deployment. It is for configuring agents such as Claude Code, Codex, and Gemini to perform research workflows.
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/Orchestra-Research/AI-Research-SKILLsnpx agentmods add skills/orchestra-research/ai-research-skills/openvla-oftWrote 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/orchestra-research/ai-research-skills/openvla-oft)<a href="https://agentmods.dev/skills/orchestra-research/ai-research-skills/openvla-oft"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/openvla-oft/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/orchestra-research/ai-research-skills/openvla-oft"><img src="https://agentmods.dev/badge/skills/orchestra-research/ai-research-skills/openvla-oft.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 | $0.00103 | $0.04257 |
| Opus 5 | $0.00051 | $0.02129 |
| Sonnet 5 | $0.00021 | $0.00851 |
| Haiku 4.5 | $0.00010 | $0.00426 |
Grade A, and why
fine-tuning-openvla-oft 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 9d 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 — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenVLA-OFT
Fine-tuning and evaluation workflows for OpenVLA-OFT and OpenVLA-OFT+ from the official openvla-oft codebase. Covers blank-machine setup plus LoRA-based adaptation of OpenVLA for robot action generation with continuous action prediction heads.
Quick start
Clone the public repo, follow the official setup, then evaluate a pretrained LIBERO checkpoint:
git clone https://github.com/moojink/openvla-oft.git
cd openvla-oft
python experiments/robot/libero/run_libero_eval.py \
--pretrained_checkpoint moojink/openvla-7b-oft-finetuned-libero-spatial \
--task_suite_name libero_spatial \
--center_crop True \
--num_trials_per_task 50 \
--seed 7
Core concepts
What OpenVLA-OFT changes: Standard OpenVLA tokenizes continuous actions into discrete bins, losing precision. OFT replaces this with dedicated continuous action heads (L1 regression or diffusion) while keeping the VLA backbone frozen and adapting via LoRA.
OFT vs OFT+ variants:
| Variant | FiLM | Images | Typical use |
|---|---|---|---|
| OFT | Off | 2 (front + wrist) | LIBERO simulation |
| OFT+ | On | 3 (high + left + right wrist) | ALOHA real-world |
Key architecture choices:
- LoRA adaptation: Rank-32 LoRA on VLA backbone (no full fine-tuning needed)
- Continuous actions: L1 regression head (default) or diffusion head
- FiLM conditioning: Feature-wise Linear Modulation for stronger language grounding in OFT+
- Multi-image input: Configurable 2 or 3 camera streams via
num_images_in_input
Compute requirements
| Task | GPU | VRAM | Notes |
|---|---|---|---|
| LIBERO evaluation | 1x A100/A40 | ~16 GB | Single GPU |
| ALOHA evaluation | 1x A100/A40 | ~18 GB | Single GPU |
| LIBERO fine-tuning | 8x A100 | ~27 GB/GPU | Paper default |
| ALOHA fine-tuning (OFT+) | 8x A100 | ~35 GB/GPU | FiLM + 3 images |
| LoRA merge | 1x any GPU | ~16 GB | One-time step |
Expected performance benchmarks
Official results (paper setup, seed=7, 50 trials per task):
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 442 lines · 103 tokens per session scan A 07492556c747
fine-tuning-openvla-oft is a skill published in the GitHub repository Orchestra-Research/AI-Research-SKILLs (12,567 stars, last pushed 2mo ago), licensed MIT. It adds 103 tokens to every session and 4,257 once invoked, about $0.0005 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 2026-09-03.
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molmoact-libero
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