fragment-file-validation

fragment-file-validation is a skill for Claude Code, Codex from HolobiomicsLab/asb-skill-collections. It costs 40 tokens per session (1,518 once invoked), scanned A, original, Apache-2.0.

A quality-control step that checks a compressed fragment file created from a coordinate-sorted BAM file. Fragment files store the genomic locations of DNA fragments for single-cell ATAC-seq analysis.

In plain words
What is it for?
Use it after BAM-to-fragment conversion to check file structure, contents, read counts, and duplication-related quality measures.
Why use it?
A conversion can produce a missing, empty, or malformed file, which would cause problems later when building matrices or clustering cells. Validation catches those issues early.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it after BAM-to-fragment conversion to check file structure, contents, read counts, and duplication-related quality measures.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/holobiomicslab/asb-skill-collections/fragment-file-validation
Install

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.

Any agent
npx skills add HolobiomicsLab/asb-skill-collections --skill fragment-file-validation
Clone the repo
git clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collections

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for fragment-file-validation

README.md
[![agentmods](https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/fragment-file-validation/github.svg)](https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/fragment-file-validation)
Your own site
<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/fragment-file-validation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/fragment-file-validation/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.

agentmods 80×15 button for fragment-file-validation

Your own site · 80×15
<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/fragment-file-validation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/fragment-file-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,518 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.01518
Opus 5 $0.00020 $0.00759
Sonnet 5 $0.00008 $0.00304
Haiku 4.5 $0.00004 $0.00152

Measured 9d ago against content hash 19452d84da42, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fragment-file-validation 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 9d 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.

collections/epigenomics/v1/skills/fragment-file-validation/SKILL.md · 97 lines

How it starts

The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.

fragment-file-validation

Summary

Validate that BAM-to-fragment file conversion produced a correctly formatted, non-empty compressed fragment file (BED.gz or .zst) with all required fields and QC metrics. This skill ensures data integrity before downstream analysis in SnapATAC2 single-cell ATAC-seq pipelines.

When to use

After invoking pp.make_fragment_file to convert a coordinate-sorted BAM file (e.g., from 10X ATAC or standard alignment) into a compressed fragment file. Use this skill to confirm the output file exists, is non-empty, contains valid BED format fields, and has computable QC metrics (duplication rate, read counts) before proceeding to matrix generation or downstream clustering.

When NOT to use

  • Input is already a count matrix or peak-by-cell matrix (use directly for clustering/embedding, not fragment validation)
  • Fragment file was generated by an external tool without BAM input (validation logic may differ; consult that tool's QC standards)
  • Input is uncoordinate-sorted or contains unmapped reads (pre-filter or re-sort BAM before calling pp.make_fragment_file)

Inputs

  • coordinate-sorted BAM file (e.g., from 10X ATAC sequencing or standard genomic alignment)
  • SnapATAC2 AnnData object or file path for output destination

Outputs

  • compressed fragment file (BED.gz or .zst format)
  • fragment file with columns: chrom, start, end, barcode, count
  • QC metrics: duplication rate per barcode, read count per barcode

How to apply

Decompress and inspect the output fragment file (BED.gz or .zst format) to verify it contains the expected four or five columns: chrom (chromosome), start (0-based start coordinate), end (end coordinate), barcode (cell barcode), and count (fragment count or quality metric). Check that the file is non-empty (contains at least header or data rows) and that all chromosomes and barcodes are valid. Compute or retrieve QC metrics including duplication rate (fraction of duplicate fragments per barcode) and total read counts per cell barcode, and confirm these values are reasonable (typically duplication rates < 50% for high-quality ATAC libraries). Validate that coordinates are sorted and non-overlapping within expected genomic ranges. If using 10X BAM input, confirm source='10x' parameter was set during pp.make_fragment_file invocation to ensure correct barcode extraction.

Read the full file on GitHub · 97 lines

Changes

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.

  1. 9d ago First seen · 97 lines · 40 tokens per session scan A 19452d84da42

Subscribe to this mod's changes

fragment-file-validation is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 40 tokens to every session and 1,518 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 2026-09-03.

Related

Other skills, from other repositories

gsva-analysis-and-visualization

Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression…

aipoch/medical-research-skills · 88 tokens

medical-research-literature-reader-pro

A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title.…

aipoch/medical-research-skills · 199 tokens

adverse-event-narrative

Generates CIOMS I-compliant ICSR narratives from adverse event case data for FDA and EMA regulatory submission. Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.

aipoch/medical-research-skills · 57 tokens

anatomy-quiz-master

Generate interactive anatomy quizzes for medical education with multiple.

aipoch/medical-research-skills · 17 tokens

decision-curve-analysis

Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or…

aipoch/medical-research-skills · 64 tokens

elastic-net-feature-selection

Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification…

aipoch/medical-research-skills · 83 tokens