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 agentlas-ai/agentlas-desktop --skill arboretogit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/arboreto)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/arboreto"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/arboreto/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/agentlas-ai/agentlas-desktop/arboreto"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/arboreto.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00066 | $0.02364 |
| Opus 5.5 | $0.00026 | $0.00946 |
| Sonnet 5.5 | $0.00013 | $0.00473 |
| Haiku 4.5 | $0.00007 | $0.00236 |
Grade A, and why
arboreto 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 1mo 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.
This is a copy
88% identical to arboreto — 348 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arboreto
Overview
Arboreto is a Python library from Aerts Lab for inferring gene regulatory networks (GRNs) from gene expression data. It parallelizes tree-based ensemble regression (GRNBoost2, GENIE3) with Dask across local cores or remote clusters.
Core capability: Identify which transcription factors (TFs) regulate which target genes based on expression patterns across observations (cells, samples, conditions).
Upstream: PyPI 0.1.6 (2021-02-09, latest). Docs: arboreto.readthedocs.io. Primary downstream consumer: pySCENIC.
Quick Start
Install arboreto:
uv pip install arboreto
Basic GRN inference:
import pandas as pd
from arboreto.algo import grnboost2
if __name__ == '__main__':
# Load expression data (genes as columns)
expression_matrix = pd.read_csv('expression_data.tsv', sep='\t')
# Infer regulatory network
network = grnboost2(expression_data=expression_matrix)
# Save results (TF, target, importance)
network.to_csv('network.tsv', sep='\t', index=False, header=False)
Critical: Always use if __name__ == '__main__': guard because Dask spawns new processes.
Core Capabilities
1. Basic GRN Inference
For standard GRN inference workflows including:
- Input data preparation (Pandas DataFrame or NumPy array)
- Running inference with GRNBoost2 or GENIE3
- Filtering by transcription factors
- Output format and interpretation
See: references/basic_inference.md
Use the ready-to-run script: scripts/basic_grn_inference.py for standard inference tasks:
python scripts/basic_grn_inference.py expression_data.tsv output_network.tsv --tf-file tfs.txt --seed 777 --limit 5000
2. Algorithm Selection
Arboreto provides two algorithms:
GRNBoost2 (Recommended):
- Fast gradient boosting-based inference
- Optimized for large datasets (10k+ observations)
- Default choice for most analyses
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.
- 1mo ago First seen · 284 lines · 66 tokens per session scan A 230eac5da0a0
arboreto is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 2,364 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to arboreto, differing in 348 lines, and is treated as a copy.
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