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 extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData.
Install Sc Gene Programs
$ npx skills add TianGzlab/OmicsClaw --skill sc-gene-programs -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-gene-programs --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-gene-programs .claude/skills/sc-gene-programs && 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-gene-programs" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-gene-programs into .claude/skills/sc-gene-programs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-gene-programs", 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-gene-programsType 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-gene-programs -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-gene-programs --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-gene-programs .agents/skills/sc-gene-programs && 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-gene-programs" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-gene-programs into .agents/skills/sc-gene-programs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-gene-programs", 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-gene-programs -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-gene-programs --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-gene-programs .cursor/skills/sc-gene-programs && 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-gene-programs" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-gene-programs into .cursor/skills/sc-gene-programs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-gene-programs", 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-gene-programs--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-gene-programs -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TianGzlab/OmicsClaw sc-gene-programs --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-gene-programs .gemini/skills/sc-gene-programs && 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-gene-programs" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-gene-programs into .gemini/skills/sc-gene-programs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-gene-programs", 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-gene-programsInstalls 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-gene-programs -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-gene-programs .github/skills/sc-gene-programs && 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-gene-programs" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-gene-programs into .github/skills/sc-gene-programs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-gene-programs", 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-gene-programs -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-gene-programs --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-gene-programs .opencode/skills/sc-gene-programs && 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-gene-programs" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/scrna/sc-gene-programs into .opencode/skills/sc-gene-programs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sc-gene-programs", 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.
Facts
- Skill name
sc-gene-programs- GitHub stars
- 161
- Token cost
- ~1.8k tokens
- SKILL.md length
- 621 words
- Files
- 10 (incl. references)
- Skills in repo
- 88
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Load when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData.
- Works in 7 steps: Auto-fallback check: try import cnmf; if… → Load AnnData (--input) or build a demo. → Preflight: pick source matrix per… → …
- Tasks that involve Bioinformatics
- SKILL.md covers When to use, Use in an analysis step, Inputs & Outputs and Flow, plus 5 more sections
- Runs Python scripts from its folder; calls python
What it does
Sc Gene Programs is an agent skill from TianGzlab/OmicsClaw. Load when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData. Skip when ranking marker genes per cluster (use sc-markers); inferring TF → target regulons (use sc-grn).
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including reference files (for example `_api.py`, `examples/example_step.py` and `references/methodology.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.
When your agent uses it
- Tasks that involve Bioinformatics
Example prompts
- “/sc-gene-programs”
Requirements
- Python 3
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Auto-fallback check: try import cnmf; if it fails, switch to nmf and record the fallback.
- Load AnnData (--input) or build a demo.
- Preflight: pick source matrix per --layer (auto-prefer layers["counts"] for cnmf when --layer is unset); reject negative values; warn if…
- Run cNMF (consensus NMF with --n-iter iterations per factorization) or sklearn NMF (single run, --seed).
- Build top-genes-per-program table; compute per-program correlation matrix.
- Detect degenerate output → record diagnostics; do NOT raise.
- Save tables, figures, processed.h5ad, report.md, result.json.
What it can do on your machine
Read from SKILL.md and the folder at commit 90a3bec. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sc Gene Programs loads about 1.8k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 63 tokens; SKILL.md has 621 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.
Safety
Auto-check passed
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.
SKILL.md
The full file from TianGzlab/OmicsClaw at commit 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 621 words, ~1,770 tokens.
Download SKILL.mdSave it as
.claude/skills/sc-gene-programs/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.- name
- sc-gene-programs
- description
- Load when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData. Skip when ranking marker genes per cluster (use sc-markers); inferring TF → target regulons (use sc-grn).
- tags
- singlecell, scrna, gene-programs, nmf, cnmf, factorisation
sc-gene-programs
When to use
The user has a non-negative scRNA AnnData (raw counts or log-normalised expression) and wants to decompose it into K gene programs (latent factors) plus a per-cell usage matrix. Two methods:
cnmf(default) — consensus NMF (multiple runs + clustering of factors) for stable programs. Auto-falls back tonmfif thecnmfpackage isn't installed.nmf— sklearn NMF, single run.
Output: tables/program_usage.csv (cells × K), tables/program_weights.csv
(programs × genes), tables/top_program_genes.csv (top-N genes per program).
For per-cluster marker discovery use sc-markers; for TF → target
regulons use sc-grn; for per-cell pathway scores against curated
gene sets use sc-pathway-scoring.
Use in an analysis step
Use load_skill from the notebook SDK; write returned objects with
write_output. This runnable example is also in examples/example_step.py.
The CLI remains available for standalone reports and galleries.
# Extract six NMF programs from the log-normalized PBMC68k snapshot.
# Reads pbmc68k_reduced.
# Calls sc-gene-programs: find_programs, top_program_genes, usage_figure.
from skills._sdk.notebook import load_demo, load_skill, write_output
programs = load_skill('sc-gene-programs')
adata = load_demo('pbmc68k_reduced').raw.to_adata()
result = programs.find_programs(adata, method='nmf')
top = programs.top_program_genes(result, n=10)
write_output(result, 'intermediate/programs.h5ad')
write_output(top, 'tables/top_program_genes.csv')
write_output(programs.usage_figure(result), 'figures/program_usage.png')
assert result.obsm['X_gene_programs'].shape == (adata.n_obs, 6)
assert (result.obsm['X_gene_programs'] >= 0).all()
assert top['program'].nunique() == 6
assert set(top.gene).issubset(adata.var_names)Inputs & Outputs
Input is a non-negative AnnData expression matrix; layer selects another
matrix. PCA and neighbors are not required. find_programs returns a copy
with obsm["X_gene_programs"] and tables exposed by the API helpers.
The CLI writes processed.h5ad, report.md, result.json,
tables/program_usage.csv (cells × programs),
tables/program_weights.csv (programs × genes), and
tables/top_program_genes.csv. cNMF additionally writes
tables/program_tpm.csv. Gallery figures are mean_program_usage.png
and, for multiple programs, program_correlation.png. Figure source
tables, including program correlations, live under figure_data/.
Flow
- Auto-fallback check: try
import cnmf; if it fails, switch tonmfand record the fallback. - Load AnnData (
--input) or build a demo. - Preflight: pick source matrix per
--layer(auto-preferlayers["counts"]for cnmf when--layeris unset); reject negative values; warn ifn_genes < 50or running NMF on raw counts without--layer counts. - Run cNMF (consensus NMF with
--n-iteriterations per factorization) or sklearn NMF (single run,--seed). - Build top-genes-per-program table; compute per-program correlation matrix.
- Detect degenerate output → record diagnostics; do NOT raise.
- Save tables, figures,
processed.h5ad,report.md,result.json.
Gotchas
run_info(result)records requested and executed methods and the reason when missing cNMF falls back to sklearn NMF. CLIresult.json["summary"]["backend"]reports the executed backend.- CLI preflight rejects negative input. The API retains the existing solver's negative-to-zero clipping; use a non-negative matrix for interpretable
program_weights. - cNMF prefers
layers["counts"]whenlayeris unset; NMF uses X. A missing cNMF backend falls back to NMF's matrix selection. n_iter/--n-iteris the maximum number of iterations per factorization, not the number of consensus replicates.run_infoincludes the cNMF replicate count when used.- CLI
result.json["summary"]["degenerate_output"]reports collapsed programs without making the run fail. Inspect the flag anddegenerate_issuesbefore using the tables.
Show full SKILL.md (235 more words)Show less
Key CLI
# Demo (cNMF on synthetic data, falls back to NMF if cnmf missing)
python skills/singlecell/scrna/sc-gene-programs/sc_gene_programs.py --demo --output /tmp/sc_gp_demo
# cNMF with 8 programs on raw counts
python skills/singlecell/scrna/sc-gene-programs/sc_gene_programs.py \
--input clustered.h5ad --output results/ \
--method cnmf --n-programs 8 --n-iter 200 --layer counts
# NMF on log-normalised .X (faster, less stable)
python skills/singlecell/scrna/sc-gene-programs/sc_gene_programs.py \
--input normalized.h5ad --output results/ \
--method nmf --n-programs 10 --top-genes 50See also
references/parameters.md— every CLI flag, NMF / cNMF tunablesreferences/methodology.md— when consensus NMF wins; layer-selection guidereferences/output_contract.md—obsm["X_gene_programs"]/tables/program_*.csvschemas- Adjacent skills:
sc-preprocessing(upstream — produces a non-negative.Xorlayers["counts"]),sc-markers(parallel — cluster markers, NOT latent factors),sc-pathway-scoring(parallel — supervised program scoring against curated gene sets),sc-grn(parallel — TF → target regulons; complementary to gene programs)
Dependencies
Python packages this skill's script needs. They are not installed for you — check before a long run.
anndata, cnmf, matplotlib, numpy, pandas, scanpy, scikit-learn, scipy
API
<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->find_programs(adata, *, method: str='cnmf', n_programs: int=6, n_iter: int=400, layer: str | None=None, top_genes: int=30, random_state: int=0)
Return a copy with per-cell usage in obsm['X_gene_programs'].
cNMF uses counts when available; missing cNMF falls back to sklearn NMF, recorded by run_info. NMF uses X unless layer is set. Negative values are clipped to zero by the existing solver. n_iter limits each factorization's iterations, not the number of cNMF replicates. Both methods use random_state. Program weights and ranked genes are available through the table helpers.
program_weights(adata) -> pd.DataFrame
Return program-by-gene weights from find_programs.
top_program_genes(adata, *, n: int | None=None) -> pd.DataFrame
Return ranked genes and weights; n optionally limits genes per program.
program_correlation(adata) -> pd.DataFrame
Return Pearson correlations between per-cell program usages.
usage_figure(adata)
Return a heatmap figure of cells by program usage, without writing files.
run_info(adata, *, keep: bool=True) -> dict
Return methods and solver diagnostics; keep=False removes the run record.
<!-- api:end -->© 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
Files
SKILL.md and 9 other files (references) in skills/singlecell/scrna/sc-gene-programs of TianGzlab/OmicsClaw.
- SKILL.md
- _api.py
- examples/example_step.py
- references/methodology.md
- references/output_contract.md
- references/parameters.md
- references/r_visualization.md
- sc_gene_programs.py
- tests/test_programs_api.py
- tests/test_sc_gene_programs_methods.py
Open the folder on GitHubat commit 90a3bec
Compare with similar skills
Sc Gene Programs 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 Gene Programs this skillTianGzlab/OmicsClaw | 161 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Scanpy Single-Cell Analysisdavila7/claude-code-templates | 32k | 16 repos | ~2.8k | Automated safety check: Pass | MIT | |
| PyDESeq2 Differential Expressiondavila7/claude-code-templates | 32k | 12 repos | ~4k | Automated safety check: Pass | MIT | |
| Single-Cell Initial AnalysisLigphiDonk/Oh-my--paper | 738 | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Anndataaipoch/medical-research-skills | 2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Multiomics StatisticsVectorSpaceLab/AREX-Skill | 328 | — | ~1k | Automated safety check: Pass | GPL-3.0 |
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Single-Cell Initial Analysis
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Related
Works with
Categories
Questions about Sc Gene Programs
What does Sc Gene Programs do?
Load when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData. Sc Gene Programs is an agent skill from TianGzlab/OmicsClaw. Load when extracting gene programs (NMF / cNMF factorisation) and per-cell program usage scores from a non-negative scRNA AnnData.
When should I use Sc Gene Programs?
Sc Gene Programs fits situations like: tasks that involve Bioinformatics.
How do I install Sc Gene Programs in Claude Code?
Run `npx skills add TianGzlab/OmicsClaw --skill sc-gene-programs -a claude-code`. Or copy the skill folder (skills/singlecell/scrna/sc-gene-programs in TianGzlab/OmicsClaw) into .claude/skills/sc-gene-programs in your project. Claude Code loads it when a task matches its description.
How do I install Sc Gene Programs in Codex?
Run `npx skills add TianGzlab/OmicsClaw --skill sc-gene-programs -a codex`. Or copy the skill folder (skills/singlecell/scrna/sc-gene-programs in TianGzlab/OmicsClaw) into .agents/skills/sc-gene-programs in your project. Codex loads it when a task matches its description.
Can I use Sc Gene Programs in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TianGzlab/OmicsClaw --skill sc-gene-programs -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-gene-programs, .gemini/skills/sc-gene-programs, .github/skills/sc-gene-programs and .opencode/skills/sc-gene-programs in your project.
What does Sc Gene Programs need to run?
Going by SKILL.md and its folder, Sc Gene Programs needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Sc Gene Programs access the network?
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.
Is Sc Gene Programs safe to install?
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.
What licence does Sc Gene Programs use?
Sc Gene Programs 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.
How many tokens does Sc Gene Programs use?
About 1.8k tokens (SKILL.md is roughly 7.1k 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 1.3k tokens, read only when the agent opens those files.
What are the alternatives to Sc Gene Programs?
Skills that share tags, products or a category with Sc Gene Programs: Scanpy Single-Cell Analysis (davila7/claude-code-templates, 32k stars), PyDESeq2 Differential Expression (davila7/claude-code-templates, 32k stars), Single-Cell Initial Analysis (LigphiDonk/Oh-my--paper, 738 stars) and Anndata (aipoch/medical-research-skills, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sc Gene Programs?
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.