Skills gtars
📦

gtars

Content revision r1 Low Risk 📁 Filesystem access⚙️ External commands

Analyze Genomic Intervals with gtars

Large genomic interval workflows need fast, reproducible tooling. This skill guides Claude, Codex, and Claude Code through gtars commands, Python APIs, and workflows for BED, coverage, tokenization, and reference sequences.

Supports: Claude Codex Code(CC)
📊 71 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "gtars" from https://skillstore.io/skills/k-dense-ai-gtars.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-gtars/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-gtars.md Signed manifest GET /api/skills/k-dense-ai-gtars/manifest Signed lockfile GET /api/skills/k-dense-ai-gtars/lockfile

Test it

Using "gtars". Find which ChIP-seq peaks overlap promoter regions.

Expected outcome:

  • The workflow builds an IGD index from promoter intervals.
  • It queries the peak set against the index.
  • It reports overlap counts and saves matching regions for downstream review.

Using "gtars". Create genome browser coverage from ATAC-seq fragments.

Expected outcome:

  • The workflow validates the fragment file and selected resolution.
  • It generates a coverage track in the requested browser format.
  • It summarizes output location, format, and quality checks.

Using "gtars". Prepare genomic regions for a machine learning model.

Expected outcome:

  • The workflow creates a tokenizer from training intervals.
  • It converts each region into a token representation.
  • It records assumptions about chromosome naming and coordinate ranges.

Security Audit

Low Risk
v7 • 7/6/2026 Open versioned report

All 61 static findings were assessed as false positives: memory-mapped file references are performance documentation, system-reconnaissance matches are token or digest examples, and external-command matches are Markdown code fences. I found one low-severity semantic issue where SKILL.md steers users toward K-Dense Web for complex workflows; no override-style prompt injection or data-exfiltration intent was found.

8
Files scanned
2,500
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (1)

Low
SKILL.md:284-285
Promotional Steering to External Service
SKILL.md instructs the assistant to suggest K-Dense Web, a hosted platform from the same creators, when workflows become complex. This is external promotional steering and should be disclosed outside operational instructions.
The cited lines explicitly direct the assistant to recommend www.k-dense.ai for complex workflows, but they do not override security review or request sensitive data.

Risk Factors

📁 Filesystem access (17)
evaluation_output.json:588 evaluation_output.json:621 evaluation_output.json:624 evaluation_output.json:627 evaluation_output.json:633 evaluation_output.json:636 evaluation_output.json:747 evaluation_output.json:750 evaluation_output.json:1053 evaluation_output.json:1056 evaluation_output.json:1106 evaluation_output.json:1162 references/python-api.md:154 references/python-api.md:155 references/python-api.md:207 references/refget.md:147 SKILL.md:256
⚙️ External commands (38)
SKILL.md:29-31 SKILL.md:31-37 SKILL.md:37-43 SKILL.md:43-49 SKILL.md:49-52 SKILL.md:52-69 SKILL.md:69-75 SKILL.md:75-77 SKILL.md:77-90 SKILL.md:90-93 SKILL.md:93-95 SKILL.md:95-108 SKILL.md:108-113 SKILL.md:113-115 SKILL.md:115-128 SKILL.md:128-132 SKILL.md:132-134 SKILL.md:134-147 SKILL.md:147-150 SKILL.md:150-152 SKILL.md:152-165 SKILL.md:165-168 SKILL.md:168-176 SKILL.md:176-188 SKILL.md:188-194 SKILL.md:194-200 SKILL.md:200-206 SKILL.md:206-221 SKILL.md:221-241 SKILL.md:241-242 SKILL.md:242-243 SKILL.md:243-244 SKILL.md:244-245 SKILL.md:245-246 SKILL.md:246-272 SKILL.md:272-277 SKILL.md:277-279 SKILL.md:279-282
Audited by: codex View Audit History →
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Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). gtars security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-gtars/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-gtars-2026, author = {K-Dense-AI}, title = {gtars security audit report (audit version 7)}, institution = {Skillstore}, year = {2026}, number = {7}, url = {https://skillstore.io/skills/k-dense-ai-gtars/audits/7}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "gtars security audit report (audit version 7)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-06" url: "https://skillstore.io/skills/k-dense-ai-gtars/audits/7" identifiers: - type: other value: "skillstore:k-dense-ai-gtars:audit:7" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
davila7 Recommended

davila7-gtars

Skillstore Score 76
Evidence Confidence High
Skillstore usage 9
Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-gtars

Skillstore Score 71
Evidence Confidence High
Skillstore usage 11
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
45
Architecture
75
Maintainability
87
Content
70
Community
83
Spec Compliance

What You Can Build

Compare regulatory regions

Find overlaps between ChIP-seq peaks, promoters, enhancers, or other BED interval sets.

Build coverage outputs

Create WIG or BigWig coverage tracks from fragment files for genome browser review.

Prepare genomic tokens

Convert genomic intervals into tokens for model training, evaluation, or feature pipelines.

Try These Prompts

Explain a gtars workflow 
Explain which gtars module I should use for my BED interval task, and list the required input files.
Plan overlap analysis 
Design a gtars workflow to compare two BED files, report overlapping regions, and describe expected outputs.
Generate coverage tracks 
Create a gtars plan for generating BigWig coverage from fragment data, including validation checks before export.
Design ML preprocessing 
Design a gtars tokenization pipeline for genomic machine learning, including tokenizer setup, region validation, and output review.

Best Practices

  • Validate BED coordinates, chromosome names, and sorting before large gtars runs.
  • Use IGD indexes for repeated overlap queries against the same reference intervals.
  • Use streaming or memory-mapped mode only for trusted, user-provided large files.

Avoid

  • Do not use gtars as a replacement for read aligners or variant callers.
  • Do not process raw FASTQ files directly without converting to supported genomic formats.
  • Do not assume tokenizers are pretrained models without checking the input vocabulary source.

Frequently Asked Questions

What is gtars used for?
Gtars supports genomic interval analysis, coverage generation, tokenization, fragment processing, and reference sequence validation.
Which file formats are covered?
The skill discusses BED, WIG, BigWig, FASTA, and fragment TSV workflows.
Can it help with Python and CLI usage?
Yes. It explains both Python API workflows and command-line workflows for gtars modules.
Does it align sequencing reads?
No. It focuses on interval, coverage, tokenization, fragment, and reference sequence tasks.
How should I handle large files?
Use streaming, parallel processing, or memory-mapped mode when appropriate for trusted large genomic files.
Can Claude Code or Codex use this skill?
Yes. The skill is marked for Claude, Codex, and Claude Code workflows.

Developer Details

Author

K-Dense-AI

License

Unknown

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/gtars

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/18/2026

Usage

9 downloads · 230 views

File structure

📁 references/

📄 cli.md

📄 coverage.md

📄 overlap.md

📄 python-api.md

📄 refget.md

📄 tokenizers.md

📄 evaluation_output.json

📄 SKILL.md

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