OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
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 PharMolix/OpenBioMed --skill single-cell-atac-seq-qc-processinggit clone --depth 1 https://github.com/PharMolix/OpenBioMedWrote 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/pharmolix/openbiomed/single-cell-atac-seq-qc-processing)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-atac-seq-qc-processing"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-atac-seq-qc-processing/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/pharmolix/openbiomed/single-cell-atac-seq-qc-processing"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-atac-seq-qc-processing.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 | $0.00000 | $0.03488 |
| Opus 5 | $0.00000 | $0.01744 |
| Sonnet 5 | $0.00000 | $0.00698 |
| Haiku 4.5 | $0.00000 | $0.00349 |
Grade A, and why
single-cell-atac-seq-qc-processing scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
for read in bam.fetch(row["chr"], start_win, end_win): How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ATAC-seq QC and Preprocessing
Trim adapters, align reads, remove duplicates and mitochondrial contamination, and evaluate chromatin accessibility data quality before calling peaks.
This is Step 1 of the bulk ATAC-seq pipeline — all downstream steps (peak calling, differential accessibility, TF analysis) require a clean, QC-passed BAM file from this step.
What it does
- Trims Nextera transposase adapters with Trim Galore (paired-end, quality ≥ 20)
- Aligns to reference genome with Bowtie2 (very sensitive local, paired-end mode)
- Filters to properly paired, primary alignments (MAPQ ≥ 30)
- Removes PCR duplicates with Picard MarkDuplicates
- Filters out mitochondrial reads (chr chrM) which dominate ATAC-seq libraries
- Shifts read positions +4 bp (forward strand) and −5 bp (reverse strand) to center on Tn5 cut site
- Computes TSS enrichment score (target: ≥ 7 for high-quality data)
- Plots fragment size distribution to confirm mono/di/tri-nucleosomal banding
- Computes FRiP score, NRF (non-redundant fraction), and NFR (nucleosome-free region) ratio
Why this exists
If you ask a general AI to "preprocess my ATAC-seq data," it will:
- Align with default Bowtie2 parameters (not paired-end mode), producing poor concordant alignment rates
- Skip the Tn5 cut site shift (+4/-5 bp), causing systematic peak position offsets
- Not filter mitochondrial reads — in ATAC-seq, chrM typically accounts for 30–80% of all reads
- Not compute TSS enrichment score, missing the single most informative quality metric for ATAC-seq
- Confuse FRiP with total mapped reads — FRiP (fraction of reads in peaks) requires called peaks to compute
This skill encodes the correct methodological decisions:
- Applies the exact +4/-5 bp Tn5 offset correction before any downstream analysis
- Filters chrM reads which are massively over-represented in ATAC-seq (not relevant to chromatin accessibility)
- Computes TSS enrichment using a ±2kb window around annotated TSSs — the gold-standard ATAC-seq QC metric
- Checks nucleosomal banding pattern: mono (~200 bp), di (~400 bp), tri (~600 bp) peaks confirm successful nucleosome depletion
- Reports NRF ≥ 0.9 and NFR/mono-nucleosome ratio ≥ 0.5 as passing thresholds
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 · 319 lines · 0 tokens per session scan A 211cb0e26aba
single-cell-atac-seq-qc-processing is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,488 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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