scvelo SKILL.md skillA
scvelo 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/scvelo@v2.0Pins v2.0 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/scvelo/SKILL.md- Versions recorded
- 7
- Latest version
- v2.0, committed Oct 1, 2026
- First version recorded
- Jun 11, 2026
- Size
- 12,486 bytes
- Repository stars
- 56,497
- License
- MIT
Performs RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.
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
# scVelo RNA Velocity ## When to use Use for kinetic analysis of aligned spliced/unspliced RNA counts, directional trajectory hypotheses, gene-level phase portraits, and dynamical latent time. Use the [model reference](references/velocity_models.md) for assumptions, transition probabilities, and an optional CellRank handoff. RNA velocity estimates an expression derivative under a model. Smooth arrows are not observed cell movement, lineage tracing, causal drivers, or proof of future fate. The dynamical estimator is not automatically more accurate than a steady-state estimator on every dataset. ## Tested environment and compatibility The maintained workflow targets scVelo **0.3.4**, Scanpy **1.12.4**, AnnData **0.13.4**, NumPy **2.5.3**, pandas **2.3.3**, SciPy **1.18.1**, Matplotlib **3.11.2**, and loompy **3.0.8** on Python 3.13. Use an isolated environment: ```bash uv venv --python 3.13 .venv-velocity uv pip install --python .venv-velocity/bin/python \ scvelo==0.3.4 scanpy==1.12.4 anndata==0.13.4 numpy==2.5.3 \ pandas==2.3.3 scipy==1.18.1 matplotlib==3.11.2 loompy==3.0.8 ``` The packages above were resolved from cache and tested in an isolated `uv` …
Read the whole file at its exact version.
How to install
mdr add k-dense-ai/scientific-agent-skills/scvelo@sha256:c43258bccdbaa577The line at the top of this page pins the label v2.0, 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_ts2yaoimxg3zjg7e)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 7 versions of this file, between Jun 11, 2026 and Oct 1, 2026, about one every 19 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v2.0 latest | 2026-10-01 | a92663a | 12,486 B | A | view · diff |
| v1.3 | 2026-09-30 | ce9e6e9 | 10,970 B | A | view · diff |
| v1.2 | 2026-07-31 | 4226ca8 | 10,895 B | A | view · diff |
| v1.1 | 2026-07-31 | bd99c01 | 10,895 B | A | view · diff |
| v1.1 | 2026-07-28 | 4fb7e0b | 10,892 B | A | view · diff |
| v1.0 | 2026-07-26 | b085e11 | 10,303 B | A | view · diff |
| git:20260611.1b8fae3 | 2026-06-11 | 1b8fae3 | 10,308 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 (12486 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 · 56,497 stars · license MIT · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ts2yaoimxg3zjg7e GET https://markdownregistry.com/api/v1/resolve?ref=k-dense-ai/scientific-agent-skills/scvelo GET https://markdownregistry.com/api/v1/blob/c43258bccdbaa5772fd1aeac8d12d6107dd9c9b19c6a3a81fde7d421c0d2609e
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.