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 skills add graph-robots/open-robot-skills --skill molmoact-liberogit clone --depth 1 https://github.com/graph-robots/open-robot-skillsWrote 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/graph-robots/open-robot-skills/molmoact-libero)<a href="https://agentmods.dev/skills/graph-robots/open-robot-skills/molmoact-libero"><img src="https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/molmoact-libero/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/graph-robots/open-robot-skills/molmoact-libero"><img src="https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/molmoact-libero.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00226 | $0.01364 |
| Opus 5 | $0.00113 | $0.00682 |
| Sonnet 5 | $0.00045 | $0.00273 |
| Haiku 4.5 | $0.00023 | $0.00136 |
Grade A, and why
molmoact-libero 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 11d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
molmoact-libero
Closed-loop VLA-policy skill backed by one model checkpoint: AllenAI's
MolmoAct LIBERO checkpoint (allenai/MolmoAct-7B-D-LIBERO-0812). The skill
is the model — it owns its serving preset (molmoact-libero), so a policy
node names this skill, not a free-floating policy_id. The closed-loop
replan/execute/terminate body and the load-bearing LIBERO observation
encoding live in gap.runtime.policy.run_policy_loop; the websocket client
is resolved (and cached per preset) through the executor's PolicyExecutor.
This is the MolmoAct alternative to pi05-libero for the same task
family — the two are the policy A/B axis the benchmark ablates. Pick whichever
the task / experiment calls for; their capability envelope is the same.
Capability
- Embodiment: Franka Panda (LIBERO/robosuite), OSC_POSE delta action
space
[Δx, Δy, Δz, Δrx, Δry, Δrz, gripper]. No embodiment translation happens in the loop — the checkpoint's native action space is forwarded tosim.apply_policy_action. - Tasks: the LIBERO pick-and-place distribution — pick a tabletop rigid object, optionally place/drop it in a container. Works best steered: perceive the target and hover the end-effector above it (preserving the current rotation) before handing over, so the policy starts in-distribution.
- Not for: deformables / cloth folding, articulated objects, non-LIBERO embodiments, or tasks the checkpoint never saw. If the task is outside this envelope, pick a different skill or report a missing capability — do not delegate it here and hope.
Serving
The bundle ships its own server.py and declares MolmoAct-flavored openpi
as a git dep in its own pyproject.toml, so the bundle is self-contained:
no $GAP_OPENPI_DIR clone, no shared venv. First-run setup is
gap skills install molmoact-libero, which uv syncs the bundle's .venv/
with vLLM + MolmoAct deps. The launcher then spawns the server via
uv run --project policies/molmoact-libero -- python server.py ... (so the
bundle's own venv activates automatically) and downloads the checkpoint from
hf://allenai/MolmoAct-7B-D-LIBERO-0812 on first run.
What ships with it
5 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.
- 11d ago First seen · 99 lines · 226 tokens per session scan A b245d2a94d6f
molmoact-libero is a skill published in the GitHub repository graph-robots/open-robot-skills (41 stars, last pushed yesterday), licensed Apache-2.0. It adds 226 tokens to every session and 1,364 once invoked, about $0.0011 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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Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging…
fine-tuning-openvla-oft
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diansai-2026
A full workflow guide for control-focused electronics competitions, covering the path from understanding the problem to testing and writing the design report.
neuroskill-bci
Use live BCI cognitive and mood state from NeuroSkill.