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 allenai/vla-evaluation-harness --skill add-model-servergit clone --depth 1 https://github.com/allenai/vla-evaluation-harnessWrote 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/allenai/vla-evaluation-harness/add-model-server)<a href="https://agentmods.dev/skills/allenai/vla-evaluation-harness/add-model-server"><img src="https://agentmods.dev/badge/skills/allenai/vla-evaluation-harness/add-model-server/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/allenai/vla-evaluation-harness/add-model-server"><img src="https://agentmods.dev/badge/skills/allenai/vla-evaluation-harness/add-model-server.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 Prompt Injection · line 111 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00032 | $0.01056 |
| Opus 5.5 | $0.00013 | $0.00422 |
| Sonnet 5.5 | $0.00006 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
add-model-server 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 28d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add a model server
Work from the target harness checkout; paths below are relative to it. Read
AGENTS.md, CONTRIBUTING.md, configs/README.md, and a comparable server.
For an existing integration, focus on the changed behavior.
1. Integration scope
Identify the checkpoint, upstream evaluator, target embodiment/benchmark, required inputs, action format, and temporal behavior. Checkpoint variants may differ.
Choose an existing bridge config, a focused bridge extension, or a dedicated server
based on what is missing. Use PredictModelServer for blocking inference and
ModelServer for custom async behavior; use run_server() for the CLI.
2. Dependencies and model assets
Resolve weights/revision, architecture config, tokenizer, image processor, adapters, normalization statistics/dataset keys, and auxiliary models.
- Reuse Hub caches or explicit local paths; document downloads and overrides. Verify requested files/revisions, not merely the existence of any snapshot.
- Fail on missing checkpoint/suite assets; do not select another checkpoint or invent processor defaults. Expose supported local tokenizer/processor overrides.
- Use a PEP 723 uv script with an editable harness dependency at the correct relative
path, commit-pinned git dependencies, and
exclude-newerset to the verification date. Verify resolved imports and real inference in that isolated environment. - Prefer installable upstream packages; use the existing pinned-clone/cache pattern when packaging is unavailable. Never clone over a user-provided checkout.
Load the policy once during initialization. Put checkpoint configs under
configs/model_servers/<name>/, using extends: _base.yaml for shared settings.
3. Inference adapter
Trace observation → preprocessing → policy → decoded action → benchmark input. Assign each transform to one layer: camera order/resize/crop/aspect ratio, pixel and state normalization, action denormalization, frame/rotation/units, and gripper mapping. Inspect upstream processors before adding transforms they already perform.
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.
- 28d ago Changed · -136 lines · -48 tokens per session 3ccf3c4d59d1
- 2mo ago First seen · 230 lines · 80 tokens per session scan A cb5c5893cc5b
add-model-server is a skill published in the GitHub repository allenai/vla-evaluation-harness (661 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 1,056 once invoked, about $0.0001 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.
Other skills, from other repositories
arboreto
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
deepspot-m
Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.
torchdrug
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
pyhealth
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer…
nemo-mbridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…