Scanpy Single-Cell Analysis
davila7/claude-code-templates
Walks through single-cell RNA-seq analysis with Scanpy: loading .h5ad and 10X data, QC, normalization, PCA and UMAP, Leiden clustering, marker genes and cell type annotation.
Load when generating spliced / unspliced layers from Cell Ranger BAM, FASTQ, STARsolo output, or velocyto loom — the prerequisite for sc-velocity.
$ npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-velocity-prep --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/singlecell/scrna/sc-velocity-prep .claude/skills/sc-velocity-prep && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sc-velocity-prep" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-velocity-prep into .claude/skills/sc-velocity-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-velocity-prep", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-velocity-prepType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-velocity-prep --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/singlecell/scrna/sc-velocity-prep .agents/skills/sc-velocity-prep && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sc-velocity-prep" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-velocity-prep into .agents/skills/sc-velocity-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-velocity-prep", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-velocity-prep --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/singlecell/scrna/sc-velocity-prep .cursor/skills/sc-velocity-prep && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sc-velocity-prep" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-velocity-prep into .cursor/skills/sc-velocity-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-velocity-prep", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/TianGzlab/OmicsClaw.git --path skills/singlecell/scrna/sc-velocity-prep--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-velocity-prep --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/singlecell/scrna/sc-velocity-prep .gemini/skills/sc-velocity-prep && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sc-velocity-prep" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-velocity-prep into .gemini/skills/sc-velocity-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-velocity-prep", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install TianGzlab/OmicsClaw sc-velocity-prepInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/singlecell/scrna/sc-velocity-prep .github/skills/sc-velocity-prep && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sc-velocity-prep" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-velocity-prep into .github/skills/sc-velocity-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-velocity-prep", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-velocity-prep --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TianGzlab/OmicsClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/singlecell/scrna/sc-velocity-prep .opencode/skills/sc-velocity-prep && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sc-velocity-prep" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-velocity-prep into .opencode/skills/sc-velocity-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-velocity-prep", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
sc-velocity-prepLoad when generating spliced / unspliced layers from Cell Ranger BAM, FASTQ, STARsolo output, or velocyto loom — the prerequisite for sc-velocity.
Sc Velocity Prep is an agent skill from TianGzlab/OmicsClaw. Load when generating spliced / unspliced layers from Cell Ranger BAM, FASTQ, STARsolo output, or velocyto loom — the prerequisite for sc-velocity. Skip when AnnData already has spliced+unspliced layers (use sc-velocity); any non-velocity preprocessing (use sc-preprocessing).
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/methodology.md`, `references/output_contract.md` and `references/parameters.md`).
It sits in Research & Science, covering Bioinformatics. It works with AnnData. The repository describes itself as: Conversational & memory-enabled AI research partner for multi-omics analysis. CLI + Desktop App (installers in Releases). From biological idea to full research paper. The licence is Apache-2.0.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 90a3bec. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Sc Velocity Prep loads about 2.1k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 73 tokens; SKILL.md has 715 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from TianGzlab/OmicsClaw at commit 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 715 words, ~2,072 tokens.
.claude/skills/sc-velocity-prep/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.This is CLI_ONLY: it runs external preparation tools or imports their files.
from skills._sdk.notebook import run_cli
run_cli("sc-velocity-prep", "--input", "data/velocity.loom",
"--base-h5ad", "data/clustered.h5ad",
inputs=["data/velocity.loom", "data/clustered.h5ad"])The demo splits PBMC3k counts into fixed 82% spliced, 14% unspliced and
4% ambiguous layers. It checks packaging, not velocity biology or external
tool installation. There is no _api.py for this skill.
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
file, directory.loom, .fastq, .fqpaired layoutpaired-fastq, cellranger-output, starsolo-velocityOutputs
tables/top_velocity_genes.csvtables/velocity_layer_summary.csvfigures/velocity_gene_balance.pngfigures/velocity_layer_fraction.pngfigures/velocity_layer_summary.pngfigures/velocity_top_genes_stacked.pngprocessed.h5advelocity_input.h5adfigures/manifest.json, figure_data/manifest.json, plot-data CSV filesreproducibility/commands.sh, reproducibility/requirements.txtartifacts/; imported loom/STARsolo inputs do not create new BAM files.report.mdresult.jsonspliced, unspliced layers; ambiguous only when supplied by the source--input (Cell Ranger dir / STARsolo dir / FASTQ / .loom).velocyto: locate BAM + barcodes, validate --gtf (or auto-pick from resources/singlecell/references/gtf/), run velocyto run, load .loom.starsolo: detect existing STARsolo Velocyto output and load directly, OR re-run STARsolo Velocyto with --reference + --chemistry + auto-detected --whitelist.--base-h5ad.tables/velocity_layer_summary.csv) and top-gene balance.processed.h5ad, tables, figures, report.md, result.json.sc_velocity_prep.py raises ValueError("BAM-backed velocyto preparation requires a GTF file. Pass --gtf /abs/path/to/genes.gtf, or keep one under resources/singlecell/references/gtf/. ..."). Auto-detection only fires if a project-local GTF lives at the recommended path.sc_velocity_prep.py raises ValueError("FASTQ-backed STARsolo velocity preparation requires a STAR genome directory. ...") if --reference is missing and nothing's at resources/singlecell/references/starsolo/. sc_velocity_prep.py raises ValueError("FASTQ-backed STARsolo velocity preparation requires an explicit --chemistry.") when --chemistry auto is left as the default — STARsolo cannot infer 10x v2 vs v3 vs v4 from FASTQ alone.sc_velocity_prep.py raises ValueError("Could not infer a compatible STARsolo whitelist. Pass --whitelist /abs/path/to/3M-february-2018.txt, or keep the whitelist under resources/singlecell/references/whitelists/. ..."). The guesser uses the reference path + chemistry; a non-standard reference layout breaks it.sc_velocity_prep.py is documented as "with a local fallback for index-name quirks"; _load_starsolo_velocyto_dir_safe raises FileNotFoundError(f"Could not locate STARsolo Velocyto matrices under: {path}") when nothing matches even with the fallback. Common when STARsolo finished partial / was killed mid-run.--input mandatory unless --demo (parser.error, exit code 2). sc_velocity_prep.py calls parser.error("--input required when not using --demo"). Once provided, main raises FileNotFoundError(f"Input path not found: {input_path}") for a missing path.--method choices are exactly velocyto / starsolo. sc_velocity_prep.py declares the choices via argparse; kb-python is mentioned in upstream-prep docstrings but is not a valid --method value here. Use the dedicated kb-python tooling outside OmicsClaw if you need that path.sc_velocity_prep.py:main defaults to eight threads. Reference directories named in error messages are suggestions, not shipped assets. Verify the velocyto command can import before a long BAM run; migration preflight found undefined symbol: __log10_finite in the installed copy despite its presence on PATH.sc_velocity_prep.py:main writes a new .raw snapshot and records .X as raw counts even when --base-h5ad contains normalized .X. _lib/upstream.py:merge_velocity_layers also replaces layers["counts"] with the velocity input's counts (the sum of its available splicing layers for imported data). Inspect this existing contract mismatch before treating the merged object's .X or .raw as counts; the spliced/unspliced layers remain separate.# Demo (proportional PBMC3k layers; no velocyto / STARsolo)
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py --demo --output /tmp/sc_velo_prep_demo
# velocyto from a Cell Ranger run (BAM-backed)
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
--input /data/cellranger_run/ --output results/ \
--method velocyto --gtf /refs/Homo_sapiens.GRCh38.gtf
# Load existing STARsolo Velocyto output directly
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
--input /data/starsolo_run/ --output results/ \
--method starsolo
# Re-run STARsolo from FASTQ (chemistry must be explicit)
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
--input /data/fastqs/ --output results/ \
--method starsolo --reference /refs/star_index --chemistry 10xv3
# Merge velocity layers into an existing processed AnnData
python skills/singlecell/scrna/sc-velocity-prep/sc_velocity_prep.py \
--input /data/cellranger_run/ --output results/ \
--method velocyto --gtf /refs/Homo_sapiens.GRCh38.gtf \
--base-h5ad /path/to/clustered.h5adreferences/parameters.md — every CLI flag, per-backend tunablesreferences/methodology.md — when velocyto vs STARsolo wins; whitelist conventionsreferences/output_contract.md — layers["spliced"] / layers["unspliced"] / layers["ambiguous"] schemasc-count / sc-multi-count (upstream — produce the Cell Ranger / STARsolo output this skill consumes), sc-velocity (downstream — consumes layers["spliced"] + layers["unspliced"]), sc-clustering (parallel — pass clustered output as --base-h5ad to keep clusters when adding velocity layers)Python packages this skill's script needs. They are not installed for you — check before a long run.
anndata, matplotlib, numpy, pandas, scanpy, scipy, seaborn
© TianGzlab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 6 other files (references) in skills/singlecell/scrna/sc-velocity-prep of TianGzlab/OmicsClaw.
Open the folder on GitHubat commit 90a3bec
Sc Velocity Prep next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sc Velocity Prep this skillTianGzlab/OmicsClaw | 161 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Scanpy Single-Cell Analysisdavila7/claude-code-templates | 32k | 15 repos | ~2.8k | Automated safety check: Pass | MIT | |
| ScgptJimLiu/science-skills | 227 | 4 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| PyDESeq2 Differential Expressiondavila7/claude-code-templates | 32k | 11 repos | ~4k | Automated safety check: Pass | MIT | |
| Anndatadavila7/claude-code-templates | 32k | 11 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Single-Cell Initial AnalysisLigphiDonk/Oh-my--paper | 738 | 1 repos | ~1.4k | Automated safety check: Pass | MIT |
davila7/claude-code-templates
Walks through single-cell RNA-seq analysis with Scanpy: loading .h5ad and 10X data, QC, normalization, PCA and UMAP, Leiden clustering, marker genes and cell type annotation.
JimLiu/science-skills
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology.
davila7/claude-code-templates
Runs differential gene expression analysis on bulk RNA-seq counts with PyDESeq2: design formulas, Wald tests, FDR correction and volcano or MA plots.
davila7/claude-code-templates
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling…
LigphiDonk/Oh-my--paper
Runs a seven-step quality-control and exploration pipeline on scRNA-seq, CyTOF or flow cytometry data and writes a plain-language report of what it found.
harrisongzhang/TheVirtualBiotech
Single-cell RNA-seq data preparation and quality control pipeline.
TianGzlab/OmicsClaw
Load when the user needs Deterministic fixed-period 24-hour single-component cosinor OLS rhythm analysis for a bulk RNA time-course CSV.
TianGzlab/OmicsClaw
Load when correcting batch effects in bulk expression using R sva ComBat or the legacy Python parametric approximation.
TianGzlab/OmicsClaw
Load when discovering bulk gene co-expression modules and hub genes with R WGCNA.
TianGzlab/OmicsClaw
Load when comparing gene expression between two conditions in bulk RNA-seq count data.
TianGzlab/OmicsClaw
Load when estimating cell-type proportions in bulk RNA-seq samples from a single-cell or signature-matrix reference.
TianGzlab/OmicsClaw
Load when running pathway / GO term enrichment on a bulk RNA-seq DE result list.
Works with
Categories
Load when generating spliced / unspliced layers from Cell Ranger BAM, FASTQ, STARsolo output, or velocyto loom — the prerequisite for sc-velocity. Sc Velocity Prep is an agent skill from TianGzlab/OmicsClaw. Load when generating spliced / unspliced layers from Cell Ranger BAM, FASTQ, STARsolo output, or velocyto loom — the prerequisite for sc-velocity.
Sc Velocity Prep fits situations like: tasks that involve Bioinformatics.
Run `npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a claude-code`. Or copy the skill folder (skills/singlecell/scrna/sc-velocity-prep in TianGzlab/OmicsClaw) into .claude/skills/sc-velocity-prep in your project. Claude Code loads it when a task matches its description.
Run `npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a codex`. Or copy the skill folder (skills/singlecell/scrna/sc-velocity-prep in TianGzlab/OmicsClaw) into .agents/skills/sc-velocity-prep in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TianGzlab/OmicsClaw --skill sc-velocity-prep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sc-velocity-prep, .gemini/skills/sc-velocity-prep, .github/skills/sc-velocity-prep and .opencode/skills/sc-velocity-prep in your project.
Going by SKILL.md and its folder, Sc Velocity Prep needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Sc Velocity Prep is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.3k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Sc Velocity Prep: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 32k stars), Scgpt (JimLiu/science-skills, 227 stars), PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars) and Anndata (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
TianGzlab (a GitHub organization) maintains it in TianGzlab/OmicsClaw, which has 161 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on October 7, 2026.
Source: TianGzlab/OmicsClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.