Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/LigphiDonk/Oh-my--papernpx agentmods add skills/ligphidonk/oh-my--paper/init-analysisWrote 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/ligphidonk/oh-my--paper/init-analysis)<a href="https://agentmods.dev/skills/ligphidonk/oh-my--paper/init-analysis"><img src="https://agentmods.dev/badge/skills/ligphidonk/oh-my--paper/init-analysis.svg" alt="Measured on agentmods" 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 | $0.00096 | $0.01291 |
| Opus 5 | $0.00048 | $0.00646 |
| Sonnet 5 | $0.00019 | $0.00258 |
| Haiku 4.5 | $0.00010 | $0.00129 |
Grade A, and why
init-analysis 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 8d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bioinformatics Initial Data Analysis
Automated 7-step analysis pipeline for high-dimensional single-cell biology data with plain-language report generation.
Supported Data Types
The pipeline auto-detects input data type:
| Data Type | File Formats | Detection Pattern |
|---|---|---|
| scRNA-seq | .h5ad, .h5 (10X), .mtx + barcodes |
Gene names, count matrix |
| CyTOF | .csv, .h5ad |
Phospho-markers (p.ERK, p.AKT, etc.) |
| Flow cytometry | .fcs, .csv |
Surface markers, scatter channels |
Approach 1: Full Pipeline (Recommended)
Run the complete 7-step analysis:
python3 scripts/run_pipeline.py <input_path> \
[--data-type auto|cytof|scrnaseq|flow] \
[--subsample 500] \
[--output-dir ./analysis_output] \
[--report-style clinical|technical]
Arguments:
input_path: Path to data file (.h5ad,.csv,.h5) or directory of CSV files--data-type: Data type override (default:autofor auto-detection)--subsample: Max cells per group for tractable analysis (default: 500)--output-dir: Output directory (default:./analysis_output)--report-style:clinicalfor plain-language medical summaries,technicalfor bioinformatics detail (default:clinical)
Output Files:
analysis_output/
├── figures/ # All generated plots (PNG)
├── processed/
│ └── adata_processed.h5ad # Processed AnnData object
├── report.html # Complete analysis report
└── analysis_summary.json # Machine-readable summary statistics
Approach 2: Modular Steps
For custom workflows, import individual step modules:
from step1_load_data import load_data
from step2_qc import run_qc
from step3_normalize import normalize_data
from step4_dim_reduction import run_dim_reduction
from step5_clustering import run_clustering
from step6_marker_analysis import run_marker_analysis
from step7_report import generate_report
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.
- 8d ago First seen · 111 lines · 96 tokens per session scan A 49ceed864bba
init-analysis is a skill published in the GitHub repository LigphiDonk/Oh-my--paper (721 stars, last pushed 4mo ago), licensed MIT. It adds 96 tokens to every session and 1,291 once invoked, about $0.0005 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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