agentmods.dev

sc-velocity

sc-velocity is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 78 tokens per session (1,702 once invoked), scanned A, original, Apache-2.0.

A workflow that estimates RNA velocity for individual cells from spliced and unspliced RNA layers. RNA velocity uses the balance between these RNA forms to estimate a cell's likely near-term change.

In plain words
What is it for?
Use it to calculate velocity vectors, identify influential genes, map results onto embeddings, and optionally estimate latent time.
Why use it?
It provides direction and magnitude for cell-state movement, helping reveal possible transitions in a single-cell dataset. One mode can also estimate latent time, an inferred position along a biological process.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 160repo +1 1mo ago A scan SkillSpector: warn 78 tokens original Apache-2.0

Good fit Use it to calculate velocity vectors, identify influential genes, map results onto embeddings, and optionally estimate latent time.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tiangzlab/omicsclaw/sc-velocity
View source ↗ TianGzlab/OmicsClaw tiangzlab.github.io
Install

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.

Any agent
npx skills add TianGzlab/OmicsClaw --skill sc-velocity
Clone the repo
git clone --depth 1 https://github.com/TianGzlab/OmicsClaw

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for sc-velocity

README.md
[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-velocity.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-velocity)
Your own site
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-velocity"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-velocity.svg" alt="Measured on agentmods" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,702 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

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 →

  • high Rogue Agent · line 3
    Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.
    Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00078 $0.01702
Opus 5 $0.00039 $0.00851
Sonnet 5 $0.00016 $0.00340
Haiku 4.5 $0.00008 $0.00170

Measured 4d ago against content hash 229e443f56f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

sc-velocity 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 4d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (sc_velocity.py, tests/test_sc_velocity.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/singlecell/scrna/sc-velocity/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sc-velocity

When to use

The user has a scRNA AnnData with layers["spliced"] and layers["unspliced"] already populated (typically from sc-velocity-prep running velocyto / STARsolo / kb-python) and wants per-cell velocity vectors, magnitude maps, and optional latent time. Three scVelo modes:

  • scvelo_stochastic (default) — fast, robust to noise.
  • scvelo_dynamical — full splicing-kinetics model + latent time (slower, more interpretable).
  • scvelo_steady_state — simplest approximation, fastest.

For ordering cells along a trajectory without splicing kinetics use sc-pseudotime. To generate the spliced/unspliced layers from raw FASTQs / cellranger output, run sc-velocity-prep first.

Inputs & Outputs

Inputs

  • Modalities: scrna
  • File types: .h5ad
  • Requires a preprocessed AnnData (X normalised, PCA/neighbours present)

Outputs

  • tables/cell_metadata.csv
  • tables/top_velocity_genes.csv
  • tables/velocity_cells.csv
  • tables/velocity_summary.csv
  • figures/latent_time_distribution.png
  • figures/latent_time_umap.png
  • figures/r_embedding_discrete.png
  • figures/r_embedding_feature.png
  • figures/r_velocity.png
  • figures/velocity_magnitude_distribution.png
  • figures/velocity_magnitude_umap.png
  • figures/velocity_stream.png
  • figures/velocity_top_genes.png
  • adata_with_velocity.h5ad
  • analysis_summary.txt
  • processed.h5ad
  • report.md
  • result.json
  • Processed AnnData (saves_h5ad) — adds layers: velocity
  • When --method is scvelo_dynamical:
    • AnnData additionally guarantees obs: latent_time
    • Produces artifact singlecell.latent_time as processed.h5ad (h5ad)

Flow

  1. Load AnnData (--input) or build a synthetic demo with spliced / unspliced layers.
  2. Preflight requires_layers=("spliced", "unspliced") for the chosen method.
  3. Run scVelo: filter & normalise → moments → velocity (mode-specific) → velocity graph.
  4. If scvelo_dynamical: also compute latent time and gene-level dynamics.
  5. Detect degenerate output (zero velocity genes / all-NaN) and emit a multi-action fix message in result.json["suggested_actions"] — does NOT raise.
  6. Render figures, write tables, save processed.h5ad, report.md, result.json.

Read the full file on GitHub · 135 lines

Files

What ships with it

7 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.

Changes

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.

  1. 4d ago First seen · 135 lines · 78 tokens per session scan A 229e443f56f5

Subscribe to this mod's changes

sc-velocity is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,702 once invoked, about $0.0004 per session on Opus 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 2026-09-03.

RNA-velocityScientificbioinformaticskineticsknowledge-graphlatent-timellm-agentmulti-agentsmulti-omicsscVeloscrnasinglecellvelocity
Related

Other skills, from other repositories

spatial-velocity

RNA velocity and cellular dynamics analysis for spatial transcriptomics data.

ShangBioLab/SpatialClaw · 17 tokens

Developmental Gene Panel Design Workflow

Panel design for DEVELOPING / dynamic systems (embryonic organs, differentiation, regeneration). The target experiment is usually a LATE / terminal stage, but the biology is a trajectory: terminal cell types are end-products of earlier lineage programs. A panel built from the target stage alone resolves terminal…

aristoteleo/PantheonOS · 129 tokens

Virtual Embryo — atlas data + knowledge graph

Query the Virtual Embryo knowledge graph (mouse/human developmental biology: genes, anatomy, Theiler/Carnegie stages, gene expression, diseases, papers) and its 3D atlas catalog (anatomical OPT/light-sheet volumes + 3D spatial- transcriptomics datasets), and visualise those datasets in 3D with the volume3d / spatial3d…

aristoteleo/PantheonOS · 170 tokens

pkpd-modeling

Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…

K-Dense-AI/scientific-agent-skills · 273 tokens

neuropixels-analysis

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when…

K-Dense-AI/scientific-agent-skills · 98 tokens

onekgpd

Query the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals…

K-Dense-AI/scientific-agent-skills · 143 tokens