arboreto SKILL.md skillA
arboreto from k-dense-ai/scientific-agent-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add k-dense-ai/scientific-agent-skills/arboreto@v1.3Pins v1.3 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- k-dense-ai/scientific-agent-skills
- Path
skills/arboreto/SKILL.md- Versions recorded
- 5
- Latest version
- v1.3, committed Oct 1, 2026
- First version recorded
- Jun 11, 2026
- Size
- 10,661 bytes
- Repository stars
- 53,159
- License
- MIT
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Arboreto ## When to use Use Arboreto to rank candidate regulator-target associations from expression measurements. GRNBoost2 fits stochastic gradient boosting regressions; GENIE3 fits random forests. Each target is predicted from candidate regulators, excluding itself. These are observational predictive associations, not proof of direct binding, activation/repression, or causal regulation. The latest PyPI release checked is **0.1.6** (2021-02-09). Read the Docs still labels its documentation 0.1.5; the current GitHub source contains fixes that are **not in the PyPI wheel**. Do not assume a successful unpinned installation can run inference. See [compatibility and distribution details](references/distributed_computing.md). ## Installation and compatibility The following exact stack passed dense and CSC-sparse GRNBoost2, dense GENIE3, custom GBM/RF, and wrapper smoke tests on macOS arm64 with Python 3.11.11: ```bash uv venv --python 3.11 .venv-arboreto uv pip install --python .venv-arboreto/bin/python \ 'arboreto==0.1.6' 'dask[complete]==2024.7.1' 'distributed==2024.7.1' \ 'numpy==1.26.4' 'pandas==2.2.3' 'scikit-learn==1.5.2' 'scipy==1.13.1' ``` …
Read the whole file at its exact version.
How to install
mdr add k-dense-ai/scientific-agent-skills/arboreto@sha256:38ff6f24b8fd4a86The line at the top of this page pins the label v1.3, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_3keog2264guntimk)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Jun 11, 2026 and Oct 1, 2026, about one every 28 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.3 latest | 2026-10-01 | a92663a | 10,661 B | A | view · diff |
| v1.2 | 2026-09-30 | ce9e6e9 | 9,834 B | A | view · diff |
| v1.1 | 2026-09-02 | 1e5eeff | 9,416 B | A | view · diff |
| v1.0 | 2026-07-26 | b085e11 | 8,459 B | A | view · diff |
| git:20260611.1b8fae3 | 2026-06-11 | 1b8fae3 | 8,464 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (10661 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
k-dense-ai/scientific-agent-skills · 53,159 stars · license MIT · pushed 2026-10-19 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_3keog2264guntimk GET https://markdownregistry.com/api/v1/resolve?ref=k-dense-ai/scientific-agent-skills/arboreto GET https://markdownregistry.com/api/v1/blob/38ff6f24b8fd4a86e2a57ae7bb7669fe4ce401f7375efcc82a6c004f4c51d344
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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