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 agentmods add skills/yulianuzhnenko/bioinformatics-agent-skills/scanpy-sc-analyzernpx skills add YuliaNuzhnenko/bioinformatics-agent-skills --skill scanpy-sc-analyzergit clone --depth 1 https://github.com/YuliaNuzhnenko/bioinformatics-agent-skillsWhat 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 | $0.00034 | $0.00550 |
| Opus 5 | $0.00017 | $0.00275 |
| Sonnet 5 | $0.00007 | $0.00110 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
scanpy-sc-analyzer 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 yesterday.
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.
What it actually says
Agent Skill: Single-Cell RNA-Seq & Harmony Integration Skill
📌 Description
Autonomous single-cell RNA-seq quality control filtering, Harmony batch-effect correction, Leiden clustering, UMAP visualization, and marker gene annotation.
🤖 Agent Execution Protocol
When an AI Agent is tasked with scanpy-sc-analyzer:
- Input Validation: Verify that the required input files or coordinates are supplied.
- Environment Check: Ensure dependencies (
Scanpy, Harmony, AnnData, Plotly) are installed. - Execution: Run the protocol pipeline snippet below.
- Output Generation: Produce actionable Markdown/JSON summaries with publication figures.
💻 Protocol Code Snippet
import scanpy as sc
import numpy as np
def run_sc_pipeline(h5ad_path):
adata = sc.read_h5ad(h5ad_path)
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.pp.pca(adata, n_comps=30)
sc.pp.neighbors(adata, n_neighbors=15)
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)
return adata
📥 Input & Output Specifications
Input Contract
- Target Files: Valid input data matching domain formats.
- Parameters: Quality thresholds and cutoffs.
Output Contract
- Results Table: Structured summary dataframe or matrix.
- Visualization: Rendered SVG/PNG figures.
📄 License
Distributed under the MIT License. See LICENSE for details.
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.
- yesterday First seen · 68 lines · 34 tokens per session scan A 60a9f61bcf8a
scanpy-sc-analyzer is a skill published in the GitHub repository YuliaNuzhnenko/bioinformatics-agent-skills (8 stars, last pushed 23d ago), licensed MIT. It adds 34 tokens to every session and 550 once invoked, about $0.0002 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 2026-08-31.
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