Sc Clustering
TianGzlab/OmicsClaw
Load when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData.
Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
$ npx skills add ClawBio/ClawBio --skill scrna-embedding -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ClawBio/ClawBio scrna-embedding --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/ClawBio/ClawBio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scrna-embedding .claude/skills/scrna-embedding && 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 "scrna-embedding" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-embedding into .claude/skills/scrna-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-embedding", 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/ClawBio/ClawBio/tree/main/skills/scrna-embeddingType 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 ClawBio/ClawBio --skill scrna-embedding -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ClawBio/ClawBio scrna-embedding --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/scrna-embedding .agents/skills/scrna-embedding && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "scrna-embedding" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-embedding into .agents/skills/scrna-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-embedding", 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 ClawBio/ClawBio --skill scrna-embedding -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ClawBio/ClawBio scrna-embedding --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/scrna-embedding .cursor/skills/scrna-embedding && 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 "scrna-embedding" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-embedding into .cursor/skills/scrna-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-embedding", 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/ClawBio/ClawBio.git --path skills/scrna-embedding--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 ClawBio/ClawBio --skill scrna-embedding -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ClawBio/ClawBio scrna-embedding --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/scrna-embedding .gemini/skills/scrna-embedding && 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 "scrna-embedding" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-embedding into .gemini/skills/scrna-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-embedding", 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 ClawBio/ClawBio scrna-embeddingInstalls 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 ClawBio/ClawBio --skill scrna-embedding -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/scrna-embedding .github/skills/scrna-embedding && 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 "scrna-embedding" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-embedding into .github/skills/scrna-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-embedding", 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 ClawBio/ClawBio --skill scrna-embedding -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ClawBio/ClawBio scrna-embedding --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ClawBio/ClawBio.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/scrna-embedding .opencode/skills/scrna-embedding && 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 "scrna-embedding" agent skill from https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-embedding into .opencode/skills/scrna-embedding/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scrna-embedding", 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.
scrna-embeddingLocal scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
Scrna Embedding is an agent skill from ClawBio/ClawBio. Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `scrna_embedding.py` and `tests/test_scrna_embedding.py`).
It sits in AI & LLM Engineering, covering Bioinformatics and Embeddings. It works with AnnData. The repository describes itself as: 🦖 ClawBio - The first bioinformatics-native AI agent skill library. Local-first. Reproducible. Open. Free. The licence is MIT.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 5e045e3. 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.
Links to these hosts (documentation or services it may open):
docs.scvi-tools.orgscanpy.readthedocs.ioanndata.readthedocs.ioFrom 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.
Scrna Embedding loads about 2k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 633 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 ClawBio/ClawBio at commit 5e045e3, republished under its MIT licence (© ClawBio). 633 words, ~2,011 tokens.
.claude/skills/scrna-embedding/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.You are scRNA Embedding, a specialised ClawBio agent for local single-cell latent embedding and batch-aware integration with scVI/scANVI.
Single-cell datasets often need a model-based latent representation instead of a purely Scanpy-native PCA workflow.
X_scvi, saves a stable integrated.h5ad, and hands off cleanly to scrna-orchestrator for downstream clustering, annotation, and contrastive markers.report.md / result.json contract..h5ad and 10x Matrix Market input; reject processed-like matrices.scvi.model.SCVI or refine with scvi.model.SCANVI using explicit labels.X_scvi, and export latent coordinates.--batch-key is provided.integrated.h5ad with obsm["X_scvi"], log-normalized X, and raw counts in layers["counts"].commands.sh, environment.yml, and checksums.| Format | Extension | Required Fields | Example |
|---|---|---|---|
| AnnData raw counts | .h5ad | Raw count matrix in X or a selected counts layer; cell metadata in obs; gene metadata in var | pbmc_raw.h5ad |
| 10x Matrix Market | directory, .mtx, .mtx.gz | matrix.mtx(.gz) plus matching barcodes.tsv(.gz) and features.tsv(.gz) or genes.tsv(.gz) | filtered_feature_bc_matrix/ |
| Demo mode | n/a | none | python clawbio.py run scrna-embedding --demo |
When the user asks for scVI/scANVI embedding, latent integration, or batch correction:
.h5ad / 10x input (or --demo) and reject processed-like matrices.scvi.model.SCVI on HVG raw counts, optionally using --batch-key, and refine with scvi.model.SCANVI when --method scanvi plus explicit labels are provided.X_scvi, run latent-space neighbors and UMAP.report.md, result.json, integrated.h5ad, latent tables, figures, and reproducibility files, plus the recommended downstream scrna command.# Standard usage
python skills/scrna-embedding/scrna_embedding.py \
--input <input.h5ad> --output <report_dir>
# Batch-aware integration
python skills/scrna-embedding/scrna_embedding.py \
--input <input.h5ad> --output <report_dir> \
--batch-key sample_id
# scANVI with explicit labels
python skills/scrna-embedding/scrna_embedding.py \
--input <input.h5ad> --output <report_dir> \
--method scanvi --labels-key cell_type --unlabeled-category Unknown
# 10x Matrix Market directory
python skills/scrna-embedding/scrna_embedding.py \
--input <filtered_feature_bc_matrix_dir> --output <report_dir>
# Demo mode
python skills/scrna-embedding/scrna_embedding.py \
--demo --output <report_dir>
# Via ClawBio runner
python clawbio.py run scrna-embedding --input <input.h5ad> --output <report_dir>
python clawbio.py run scrna-embedding --demopython clawbio.py run scrna-embedding --demo
python clawbio.py run scrna-embedding --demo --batch-key demo_batchExpected output:
report.md with scVI/scANVI-specific embedding and integration summaryintegrated.h5ad containing obsm["X_scvi"], log-normalized X, and layers["counts"]umap_scvi_latent.png)umap_scvi_batch.png) when --batch-key is setbatch_mixing_metrics.csv) when --batch-key is setlatent_embeddings.csv)scrna-orchestrator --use-rep X_scvin_genes_by_counts, total_counts, pct_counts_mtmin_genes, min_cells, max_mt_pctlog1p on the full-gene branchflavor="seurat") for scVI trainingscvi.model.SCVI on raw-count HVGsscvi.model.SCANVI when --method scanvi, --labels-key, and --unlabeled-category are provided--batch-key is providedobsm["X_scvi"]use_rep="X_scvi"output_directory/
├── report.md
├── result.json
├── integrated.h5ad
├── figures/
│ ├── umap_scvi_latent.png
│ └── umap_scvi_batch.png # only when batch integration is enabled
├── tables/
│ ├── latent_embeddings.csv
│ └── batch_mixing_metrics.csv # only when batch integration is enabled
└── reproducibility/
├── commands.sh
├── environment.yml
└── checksums.sha256Required:
scanpy >= 1.10anndata >= 0.12torchscvi-toolsOut of scope (v1):
totalVITrigger conditions:
scvi, latent embedding, batch integration, or batch correctionRouting note:
scrna-orchestratorscrna-embedding is the advanced entry point for scVI-style latent integration and export© ClawBio, MIT. 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 2 other files in skills/scrna-embedding of ClawBio/ClawBio.
Open the folder on GitHubat commit 5e045e3
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in ClawBio/ClawBio, which our catalogue first saw on October 7, 2026.
Scrna Embedding 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 |
|---|---|---|---|---|---|---|
| Scrna Embedding this skillClawBio/ClawBio | 1.2k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Sc ClusteringTianGzlab/OmicsClaw | 161 | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| ScgptJimLiu/science-skills | 227 | 4 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Sc MetacellTianGzlab/OmicsClaw | 161 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Alphagenome Predictionsgenomicsxai/alphagenome-pytorch | 162 | — | ~868 | Automated safety check: Pass | Apache-2.0 | |
| Genimlmajiayu000/claude-skill-registry | 666 | 1 repos | ~4.3k | Automated safety check: Pass | BSD-2-Clause |
TianGzlab/OmicsClaw
Load when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData.
JimLiu/science-skills
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology.
TianGzlab/OmicsClaw
Load when aggregating single cells into metacells (sample-aware coarse-grained pseudo-cells) on a normalised scRNA AnnData via SEACells or KMeans on a low-D embedding.
genomicsxai/alphagenome-pytorch
Run AlphaGenome-PyTorch to get genomic track predictions — via the agt predict CLI (single locus, BED regions, whole chromosomes, raw FASTA sequences, or per-gene count tables/AnnData), variant…
majiayu000/claude-skill-registry
Python library for genomic interval ML. An agent skill from majiayu000/claude-skill-registry.
GPTomics/bioSkills
Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions)…
ClawBio/ClawBio
Fetch a region of cis-eQTL summary statistics from EBI eQTL Catalogue v7+ via tabix-on-FTP.
ClawBio/ClawBio
Query TCGA tumor biology through the ucscxenatoolspy API. An agent skill from ClawBio/ClawBio.
ClawBio/ClawBio
Fetch a region of GWAS summary statistics from the NHGRI-EBI GWAS Catalog harmonised collection via tabix-on-FTP.
ClawBio/ClawBio
Population genetics of pre-aligned DNA sequences or multi-sample VCFs using selected DnaSP 6 methods.
ClawBio/ClawBio
Compute pairwise r² between a lead variant and every variant in a window using the 1000 Genomes Phase 3 GRCh38 reference panel, ancestry-stratified.
ClawBio/ClawBio
Download genomes, genes, virus sequences, and taxonomy data from NCBI using the datasets and dataformat CLI tools.
Works with
Categories
Local scVI/scANVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis. Scrna Embedding is an agent skill from ClawBio/ClawBio.h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
Scrna Embedding fits situations like: tasks that involve Bioinformatics; tasks that involve Embeddings.
Run `npx skills add ClawBio/ClawBio --skill scrna-embedding -a claude-code`. Or copy the skill folder (skills/scrna-embedding in ClawBio/ClawBio) into .claude/skills/scrna-embedding in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ClawBio/ClawBio --skill scrna-embedding -a codex`. Or copy the skill folder (skills/scrna-embedding in ClawBio/ClawBio) into .agents/skills/scrna-embedding 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 ClawBio/ClawBio --skill scrna-embedding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scrna-embedding, .gemini/skills/scrna-embedding, .github/skills/scrna-embedding and .opencode/skills/scrna-embedding in your project.
Going by SKILL.md and its folder, Scrna Embedding needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md names 3 domains. As links in the text: docs.scvi-tools.org, scanpy.readthedocs.io and anndata.readthedocs.io. 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.
Scrna Embedding is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Scrna Embedding: Sc Clustering (TianGzlab/OmicsClaw, 161 stars), Scgpt (JimLiu/science-skills, 227 stars), Sc Metacell (TianGzlab/OmicsClaw, 161 stars) and Alphagenome Predictions (genomicsxai/alphagenome-pytorch, 162 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ClawBio (a GitHub organization) maintains it in ClawBio/ClawBio, which has 1,154 GitHub stars. The repository holds 104 skills in this directory. The repository was last updated on October 7, 2026.
Source: ClawBio/ClawBio on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.