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.
npx skills add TianGzlab/OmicsClaw --skill scrna-trajectory-inferencegit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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.
[](https://agentmods.dev/skills/tiangzlab/omicsclaw/scrna-trajectory-inference)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/scrna-trajectory-inference"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/scrna-trajectory-inference/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.
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/scrna-trajectory-inference"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/scrna-trajectory-inference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00007 | $0.03181 |
| Opus 5 | $0.00003 | $0.01590 |
| Sonnet 5 | $0.00001 | $0.00636 |
| Haiku 4.5 | $0.00001 | $0.00318 |
Grade A, and why
Single-Cell Trajectory Inference 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 10d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Single-Cell Trajectory Inference
When to Use This Skill
Use when you have preprocessed scRNA-seq data and want to:
- ✅ Order cells along a differentiation or disease trajectory (pseudotime)
- ✅ Identify branching points and terminal cell fates
- ✅ Discover genes driving cell state transitions
- ✅ Visualize RNA velocity (direction of cell state change)
- ✅ Compute cell fate probabilities with CellRank
- ✅ Chain from
scrnaseq-scanpy-core-analysisoutput
Do NOT use when:
- ❌ Data is not yet preprocessed (use
scrnaseq-scanpy-core-analysisfirst) - ❌ You have bulk RNA-seq (use
disease-progression-longitudinalinstead) - ❌ Cells are terminally differentiated with no trajectory (e.g., resting PBMCs)
- ❌ Fewer than 200 cells
Installation
pip install scanpy anndata scvelo cellrank numpy pandas matplotlib seaborn scipy statsmodels reportlab
| Package | Version | License | Commercial Use | Notes |
|---|---|---|---|---|
| scanpy | ≥1.9 | BSD-3 | ✅ Permitted | Core trajectory (PAGA, DPT) |
| anndata | ≥0.8 | BSD-3 | ✅ Permitted | Data container |
| scvelo | ≥0.2.5 | BSD-3 | ✅ Permitted | RNA velocity (optional but recommended) |
| cellrank | ≥2.0 | BSD-3 | ✅ Permitted | Fate mapping (optional) |
| matplotlib | ≥3.4 | PSF | ✅ Permitted | Plotting |
| seaborn | ≥0.11 | BSD-3 | ✅ Permitted | Statistical plotting, heatmaps |
| scipy | ≥1.7 | BSD-3 | ✅ Permitted | Statistics |
| statsmodels | ≥0.13 | BSD-3 | ✅ Permitted | FDR correction |
| reportlab | ≥3.6 | BSD | ✅ Permitted | PDF report (optional) |
Graceful degradation: Core analysis (PAGA + pseudotime) requires only scanpy. scVelo and CellRank are optional — scripts detect availability and skip gracefully.
Inputs
Required:
- Preprocessed AnnData (
.h5ad) with PCA, UMAP, and cluster annotations- Output from
scrnaseq-scanpy-core-analysis(adata_processed.h5ad) works directly - Must have ≥200 cells, ≥100 genes, cluster labels in
.obs
- Output from
What ships with it
9 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.
- references/cellrank-guide.md 2.9 KB
- references/rna-velocity-guide.md 3.1 KB
- references/trajectory-methods-guide.md 3.6 KB
- references/troubleshooting.md 4.3 KB
- scripts/export_results.py 13 KB runs code
- scripts/generate_all_plots.py 25 KB runs code
- scripts/generate_report.py 17 KB runs code
- scripts/load_example_data.py 5.6 KB runs code
- scripts/run_trajectory_analysis.py 17 KB runs code
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.
- 10d ago First seen · 256 lines · 7 tokens per session scan A ec4e1bf4c7b6
Single-Cell Trajectory Inference is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 7 tokens to every session and 3,181 once invoked, about $0.0000 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 2026-08-30.
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