sc-velocity-prep

sc-velocity-prep is a skill for Claude Code, Codex from TianGzlab/OmicsClaw. It costs 70 tokens per session (1,956 once invoked), scanned A, original, Apache-2.0.

A preparation workflow for turning single-cell sequencing files into an AnnData dataset with spliced and unspliced RNA layers. These layers record RNA molecules before and after processing and are needed for RNA velocity analysis.

In plain words
What is it for?
Use it before sc-velocity when starting with raw sequencing output or a dataset without spliced and unspliced layers.
Why use it?
It removes the manual work of extracting velocity layers from BAM, FASTQ, Cell Ranger, STARsolo, or loom files. It can also add those layers to an already processed dataset.

Skill for Claude CodeCodex

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

Good fit Use it before sc-velocity when starting with raw sequencing output or a dataset without spliced and unspliced layers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tiangzlab/omicsclaw/sc-velocity-prep
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-prep
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-prep

README.md
[![agentmods](https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-velocity-prep/github.svg)](https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-velocity-prep)
Your own site
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-velocity-prep"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-velocity-prep/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.

agentmods 80×15 button for sc-velocity-prep

Your own site · 80×15
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-velocity-prep"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-velocity-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,956 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 $0.00070 $0.01956
Opus 5 $0.00035 $0.00978
Sonnet 5 $0.00014 $0.00391
Haiku 4.5 $0.00007 $0.00196

Measured 6d ago against content hash 0dc9a542af5d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

sc-velocity-prep 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 6d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (sc_velocity_prep.py, tests/test_sc_velocity_prep.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-prep/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

sc-velocity-prep

When to use

The user has raw Cell Ranger output (BAM + barcodes), STARsolo output, or paired FASTQs and needs an AnnData with layers["spliced"] / layers["unspliced"] (and optional layers["ambiguous"]) before running sc-velocity. Two backends:

  • velocyto (default) — runs velocyto run against a Cell Ranger BAM using a GTF. Produces a .loom and reads it back into AnnData.
  • starsolo — re-runs alignment from FASTQ via STARsolo with the Velocyto solo subworkflow, or loads existing STARsolo Velocyto output directly when detected.

--base-h5ad lets you merge the velocity layers into an already-processed AnnData (preserves obs / obsm / clustering).

For velocity estimation itself use sc-velocity. For non-velocity scRNA preprocessing use sc-preprocessing.

Inputs & Outputs

Inputs

  • Input kinds: file, directory
  • Modalities: scrna
  • File types: .loom, .fastq, .fq
  • FASTQ structure: valid first record; paired layout
  • Directory layouts (any): paired-fastq, cellranger-output, starsolo-velocity

Outputs

  • tables/Summary.csv
  • tables/barcodes.tsv
  • tables/cell_metadata.csv
  • tables/features.tsv
  • tables/genes.tsv
  • tables/metrics_summary.csv
  • tables/top_velocity_genes.csv
  • tables/velocity_layer_summary.csv
  • figures/velocity_gene_balance.png
  • figures/velocity_layer_fraction.png
  • figures/velocity_layer_summary.png
  • figures/velocity_top_genes_stacked.png
  • 3M-february-2018.txt
  • 737K-august-2016.txt
  • Aligned.sortedByCoord.out.bam
  • analysis_summary.txt
  • multiqc_report.html
  • possorted_genome_bam.bam
  • processed.h5ad
  • velocity_input.h5ad
  • web_summary.html
  • report.md
  • result.json
  • Processed AnnData (saves_h5ad) — adds layers: spliced, unspliced, ambiguous

Flow

  1. Resolve --input (Cell Ranger dir / STARsolo dir / FASTQ / .loom).
  2. For velocyto: locate BAM + barcodes, validate --gtf (or auto-pick from resources/singlecell/references/gtf/), run velocyto run, load .loom.
  3. For starsolo: detect existing STARsolo Velocyto output and load directly, OR re-run STARsolo Velocyto with --reference + --chemistry + auto-detected --whitelist.
  4. Optionally merge layers into --base-h5ad.
  5. Compute layer totals (tables/velocity_layer_summary.csv) and top-gene balance.
  6. Save processed.h5ad, tables, figures, report.md, result.json.

Read the full file on GitHub · 142 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. 6d ago First seen · 142 lines · 70 tokens per session scan A 0dc9a542af5d

Subscribe to this mod's changes

sc-velocity-prep is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 70 tokens to every session and 1,956 once invoked, about $0.0003 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.

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