synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill scvelogit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/scvelo)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/scvelo"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/scvelo/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/synthetic-sciences/openscience/scvelo"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/scvelo.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 | $0.00056 | $0.03003 |
| Opus 5.5 | $0.00022 | $0.01201 |
| Sonnet 5.5 | $0.00011 | $0.00601 |
| Haiku 4.5 | $0.00006 | $0.00300 |
Grade A, and why
scvelo 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
97% identical to scvelo — 452 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 — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scVelo — RNA Velocity Analysis
Overview
scVelo is the leading Python package for RNA velocity analysis in single-cell RNA-seq data. It infers cell state transitions by modeling the kinetics of mRNA splicing — using the ratio of unspliced (pre-mRNA) to spliced (mature mRNA) abundances to determine whether a gene is being upregulated or downregulated in each cell. This allows reconstruction of developmental trajectories and identification of cell fate decisions without requiring time-course data.
Installation: uv pip install scvelo
Key resources:
- Documentation: https://scvelo.readthedocs.io/
- GitHub: https://github.com/theislab/scvelo
- Paper: Bergen et al. (2020) Nature Biotechnology. PMID: 32747759
When to Use This Skill
Use scVelo when:
- Trajectory inference from snapshot data: Determine which direction cells are differentiating
- Cell fate prediction: Identify progenitor cells and their downstream fates
- Driver gene identification: Find genes whose dynamics best explain observed trajectories
- Developmental biology: Model hematopoiesis, neurogenesis, epithelial-to-mesenchymal transitions
- Latent time estimation: Order cells along a pseudotime derived from splicing dynamics
- Complement to Scanpy: Add directional information to UMAP embeddings
Prerequisites
scVelo requires count matrices for both unspliced and spliced RNA. These are generated by:
- STARsolo or kallisto|bustools with
lamannomode - velocyto CLI:
velocyto run10x/velocyto run - alevin-fry / simpleaf with spliced/unspliced output
Data is stored in an AnnData object with layers["spliced"] and layers["unspliced"].
Standard RNA Velocity Workflow
1. Setup and Data Loading
import scvelo as scv
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
# Configure settings
scv.settings.verbosity = 3 # Show computation steps
scv.settings.presenter_view = True
scv.settings.set_figure_params('scvelo')
# Load data (AnnData with spliced/unspliced layers)
# Option A: Load from loom (velocyto output)
adata = scv.read("cellranger_output.loom", cache=True)
# Option B: Merge velocyto loom with Scanpy-processed AnnData
adata_processed = sc.read_h5ad("processed.h5ad") # Has UMAP, clusters
adata_velocity = scv.read("velocyto.loom")
adata = scv.utils.merge(adata_processed, adata_velocity)
# Verify layers
print(adata)
# obs × var: N × G
# layers: 'spliced', 'unspliced' (required)
# obsm['X_umap'] (required for visualization)
What ships with it
2 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 Changed · +5 lines 03c33dfc97c8
- 1mo ago First seen · 331 lines · 56 tokens per session scan A a868f47965db
scvelo is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 3,003 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to scvelo, differing in 452 lines, and is treated as a copy.
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