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 CHENyiru3/AI-Skills-Collections --skill scribblegit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote 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/chenyiru3/ai-skills-collections/scribble)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/scribble"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/scribble/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/chenyiru3/ai-skills-collections/scribble"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/scribble.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00030 | $0.00434 |
| Opus 5.5 | $0.00012 | $0.00174 |
| Sonnet 5.5 | $0.00006 | $0.00087 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
scribble 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 6d 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.
What it actually says
Scribble: Multi-Omics Integration
Overview
Scribble is a tool/package for multi-omics integration in single-cell analysis. It provides methods for integrating different modalities (gene expression, chromatin accessibility, protein) to create unified representations of cells.
When to Use This Skill
This skill should be used when:
- Integrating multiple single-cell modalities
- Performing multi-omics integration
- Creating unified embeddings from heterogeneous data
- Analyzing paired multi-omics data
Quick Start
Installation
pip install scribble
# Or from GitHub
pip install git+https://github.com/username/scribble.git
Basic Usage
import scribble
import scanpy as sc
import numpy as np
# Load multi-modal data
rna = sc.read_h5ad("rna.h5ad")
atac = sc.read_h5ad("atac.h5ad")
protein = sc.read_h5ad("protein.h5ad")
# Integration
combined = scribble.integrate(
[rna, atac, protein],
method="cca" # or "mnn", "wnn"
)
Integration Methods
CCA Integration
# Canonical Correlation Analysis
combined = scribble.integrate(
[rna, atac],
method="cca",
n_components=50
)
WNN (Weighted Nearest Neighbors)
# Weighted nearest neighbors
combined = scribble.integrate(
[rna, protein],
method="wnn"
)
Best Practices
- Preprocess each modality: Normalize and select features
- Check for batch effects: Verify integration quality
Additional Resources
- Documentation: Check GitHub for latest information
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.
- 6d ago First seen · 85 lines · 30 tokens per session scan A debf5d996c77
scribble is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 30 tokens to every session and 434 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-02.
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