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 inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint…
$ npx skills add TianGzlab/OmicsClaw --skill spatial-trajectory -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TianGzlab/OmicsClaw spatial-trajectory --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/spatial/spatial-trajectory .claude/skills/spatial-trajectory && 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 "spatial-trajectory" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-trajectory into .claude/skills/spatial-trajectory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-trajectory", 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/spatial/spatial-trajectoryType 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 spatial-trajectory -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TianGzlab/OmicsClaw spatial-trajectory --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/spatial/spatial-trajectory .agents/skills/spatial-trajectory && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "spatial-trajectory" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-trajectory into .agents/skills/spatial-trajectory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-trajectory", 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 spatial-trajectory -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TianGzlab/OmicsClaw spatial-trajectory --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/spatial/spatial-trajectory .cursor/skills/spatial-trajectory && 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 "spatial-trajectory" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-trajectory into .cursor/skills/spatial-trajectory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-trajectory", 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/spatial/spatial-trajectory--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 spatial-trajectory -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TianGzlab/OmicsClaw spatial-trajectory --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/spatial/spatial-trajectory .gemini/skills/spatial-trajectory && 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 "spatial-trajectory" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-trajectory into .gemini/skills/spatial-trajectory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-trajectory", 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 spatial-trajectoryInstalls 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 spatial-trajectory -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/spatial/spatial-trajectory .github/skills/spatial-trajectory && 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 "spatial-trajectory" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-trajectory into .github/skills/spatial-trajectory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-trajectory", 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 spatial-trajectory -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 spatial-trajectory --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/spatial/spatial-trajectory .opencode/skills/spatial-trajectory && 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 "spatial-trajectory" agent skill from https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-trajectory into .opencode/skills/spatial-trajectory/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "spatial-trajectory", 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.
spatial-trajectoryLoad when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint…
Spatial Trajectory is an agent skill from TianGzlab/OmicsClaw. Load when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint branch probabilities). Skip when the data has spliced/unspliced layers and you want velocity-driven dynamics (use spatial-velocity); non-spatial scRNA pseudotime (use sc-pseudotime).
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `r_visualization/README.md`, `references/methodology.md` and `references/output_contract.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 MIT.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 6fbd79f. 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 and R), 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.
Spatial Trajectory loads about 1.8k tokens when it runs, and up to ~5.6k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 449 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 6fbd79f, republished under its MIT licence (© TianGzlab). 449 words, ~1,771 tokens.
.claude/skills/spatial-trajectory/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.The user has a preprocessed spatial AnnData (obsm["X_pca"] or
neighbour graph populated) and wants pseudotime / branching
trajectories. Three backends:
dpt (default) — diffusion pseudotime via sc.tl.dpt. Cheap.
Tunable --dpt-n-dcs.cellrank — GPCCA macrostates, terminal-state probabilities,
fate maps, driver-gene ranking. Tunables --cellrank-n-states,
--cellrank-frac-to-keep, --cellrank-schur-components.palantir — waypoint sampling + multi-scale Markov for branch
probabilities. Tunables --palantir-num-waypoints,
--palantir-knn, --palantir-n-components,
--palantir-max-iterations.Cluster column (--cluster-key) auto-detected from leiden /
cell_type / celltype / annotation / cluster / clusters.
For RNA-velocity-driven trajectories use spatial-velocity.
<!-- AUTO-GENERATED from skill.yaml (interface) — do not edit by hand. Regenerate: python scripts/generate_skill_md.py <skill_dir> -->
Inputs
.h5adX normalised, PCA/neighbours present)obsm: X_pcaOutputs
tables/cellrank_driver_genes.csvtables/palantir_branch_probs.csvtables/trajectory_cluster_summary.csvtables/trajectory_diffmap_points.csvtables/trajectory_driver_genes.csvtables/trajectory_fate_probabilities.csvtables/trajectory_fate_probabilities_wide.csvtables/trajectory_genes.csvtables/trajectory_run_summary.csvtables/trajectory_spatial_points.csvtables/trajectory_summary.csvtables/trajectory_terminal_states.csvtables/trajectory_umap_points.csvfigures/cellrank_fate_circular.pngfigures/cellrank_fate_heatmap.pngfigures/cellrank_fate_map.pngfigures/cellrank_gene_trends.pngfigures/trajectory_cluster_summary.pngfigures/trajectory_diffmap.pngfigures/trajectory_entropy_distribution.pngfigures/trajectory_fate_probability_distribution.pngfigures/trajectory_genes_barplot.pngfigures/trajectory_pseudotime_distribution.pngfigures/trajectory_pseudotime_embedding.pngfigures/trajectory_pseudotime_spatial.pngprocessed.h5adreport.mdresult.jsonsaves_h5ad) — adds obs: dpt_pseudotime, traj_terminal_state, traj_fate_max_prob, traj_fate_entropy, palantir_pseudotime, palantir_entropy; obsm: palantir_branch_probs; uns: iroot, palantir_waypoints, palantir_branch_prob_columns--input) or build a demo. Auto-detect --cluster-key from candidates if not passed.--root-cell <barcode> or auto-pick via expression-rank; write uns["iroot"] (_lib/trajectory.py:273).dpt: sc.tl.dpt(adata, n_dcs=...) → obs["dpt_pseudotime"].cellrank: build kernel, GPCCA macrostates, terminal states, fate probabilities, driver genes.palantir: waypoint sampling, multi-scale Markov, branch probabilities.processed.h5ad + report.--cluster-key is auto-detected from a candidate list. _lib/trajectory.py:25-31 (_CLUSTER_KEY_CANDIDATES) tries leiden → cell_type → celltype → annotation → cluster → clusters and the auto-detect at _lib/trajectory.py:62-67 returns None silently when no candidate column has ≥ 2 unique values — cluster summaries then run with cluster_key = None. By contrast, an explicit --cluster-key X whose column is missing raises ValueError at spatial_trajectory.py:1434. Pass an explicit key when you want a hard failure on a typo.dpt and cellrank write obs["dpt_pseudotime"]. _lib/trajectory.py:255-280 populates it via sc.tl.dpt; CellRank reuses the same call (_lib/trajectory.py:341). Palantir writes obs["palantir_pseudotime"] instead — don't expect dpt_pseudotime from a Palantir run._lib/trajectory.py:531-533 writes obsm["palantir_branch_probs"] (numeric matrix) AND uns["palantir_branch_prob_columns"] (terminal-state column names) ONLY when branch_probs is non-empty (if not branch_probs.empty: at _lib/trajectory.py:531). Single-terminal-state runs leave both keys absent. The cells × terminals matrix is also exported as tables/palantir_branch_probs.csv when present.traj_* keys are CellRank-only. spatial_trajectory.py:236-238 writes obs["traj_terminal_state"] / obs["traj_fate_max_prob"] / obs["traj_fate_entropy"] only when the CellRank branch executes — DPT and Palantir runs leave those keys absent.uns["iroot"] is an integer index, not a barcode. _lib/trajectory.py:273 writes the integer position into obs_names. Downstream tools that reload the AnnData and expect a string barcode need adata.obs_names[adata.uns["iroot"]].# Demo
python omicsclaw.py run spatial-trajectory --demo --output /tmp/traj_demo
# DPT (default)
python omicsclaw.py run spatial-trajectory \
--input preprocessed.h5ad --output results/ \
--method dpt --cluster-key leiden --dpt-n-dcs 10
# CellRank with explicit root cell
python omicsclaw.py run spatial-trajectory \
--input preprocessed.h5ad --output results/ \
--method cellrank --root-cell BARCODE_42 \
--cellrank-n-states 5 --cellrank-frac-to-keep 0.3
# Palantir
python omicsclaw.py run spatial-trajectory \
--input preprocessed.h5ad --output results/ \
--method palantir --palantir-num-waypoints 1200 --palantir-knn 30references/parameters.md — every CLI flag, per-method tunablesreferences/methodology.md — when each backend winsreferences/output_contract.md — per-method obs / obsm / uns keysspatial-preprocess (upstream), spatial-domains (upstream — provides obs["leiden"]), spatial-velocity (parallel — RNA-velocity-driven dynamics), sc-pseudotime (parallel — non-spatial), spatial-condition (downstream — DE between trajectory branches)© TianGzlab, 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 8 other files (references) in skills/spatial/spatial-trajectory of TianGzlab/OmicsClaw.
Open the folder on GitHubat commit 6fbd79f
Spatial Trajectory 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 |
|---|---|---|---|---|---|---|
| Spatial Trajectory this skillTianGzlab/OmicsClaw | 161 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Scanpy Single-Cell Analysisdavila7/claude-code-templates | 32k | 16 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 | 12 repos | ~4k | Automated safety check: Pass | MIT | |
| Anndatadavila7/claude-code-templates | 32k | 12 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 removing batch effects from a multi-cohort bulk RNA-seq dataset using ComBat (R or Python implementation).
TianGzlab/OmicsClaw
Load when discovering gene co-expression modules and hub genes in a bulk RNA-seq cohort via WGCNA-style soft-thresholded networks.
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.
TianGzlab/OmicsClaw
Load when converting gene identifiers between Ensembl, Entrez, and HGNC symbol in a bulk RNA-seq count matrix.
Works with
Categories
Load when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint…. Spatial Trajectory is an agent skill from TianGzlab/OmicsClaw. Load when inferring pseudotime / lineage trajectories on a preprocessed spatial AnnData via DPT (default — diffusion pseudotime), CellRank (terminal-state + fate-probability), or Palantir (waypoint branch probabilities).
Spatial Trajectory fits situations like: tasks that involve Bioinformatics.
Run `npx skills add TianGzlab/OmicsClaw --skill spatial-trajectory -a claude-code`. Or copy the skill folder (skills/spatial/spatial-trajectory in TianGzlab/OmicsClaw) into .claude/skills/spatial-trajectory in your project. Claude Code loads it when a task matches its description.
Run `npx skills add TianGzlab/OmicsClaw --skill spatial-trajectory -a codex`. Or copy the skill folder (skills/spatial/spatial-trajectory in TianGzlab/OmicsClaw) into .agents/skills/spatial-trajectory 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 spatial-trajectory -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/spatial-trajectory, .gemini/skills/spatial-trajectory, .github/skills/spatial-trajectory and .opencode/skills/spatial-trajectory in your project.
Going by SKILL.md and its folder, Spatial Trajectory needs Python and R 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.
Spatial Trajectory is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
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 3.8k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Spatial Trajectory: 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 95 skills in this directory. The repository was last updated on July 28, 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.