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Agent skill

Dnanexus Integration

by davila7 in davila7/claude-code-templates

DNAnexus cloud genomics platform. An agent skill from davila7/claude-code-templates.

MITAuto-check passedResearch & Science

Install Dnanexus Integration

skills CLI
$ npx skills add davila7/claude-code-templates --skill dnanexus-integration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates dnanexus-integration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/dnanexus-integration .claude/skills/dnanexus-integration && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dnanexus-integration
GitHub stars
32k
Used in
11 other repos
Token cost
~2.6k tokens
SKILL.md length
893 words
Files
6 (incl. references)
Skills in repo
478
Repo updated
First seen
Licence
MIT

At a glance

DNAnexus cloud genomics platform. An agent skill from davila7/claude-code-templates.

  • Works in 5 steps: App Development → Data Operations → Job Execution → …
  • Tasks that involve Bioinformatics
  • SKILL.md covers Overview, When to Use This Skill, Core Capabilities and Quick Start Examples, plus 6 more sections
  • Calls uv

What it does

Dnanexus Integration is an agent skill from davila7/claude-code-templates. DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/app-development.md`, `references/configuration.md` and `references/data-operations.md`).

It sits in Research & Science, covering Bioinformatics. It works with Python. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Tasks that involve Bioinformatics

Example prompts

  • “/dnanexus-integration”

Requirements

  • Python 3
  • Docker

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. App Development
  2. Data Operations
  3. Job Execution
  4. Python SDK (dxpy)
  5. Configuration and Dependencies

What it can do on your machine

Read from SKILL.md and the folder at commit 46b4d8b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • documentation.dnanexus.com
    • autodoc.dnanexus.com
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dnanexus Integration loads about 2.6k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 893 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 46b4d8b, republished under its MIT licence (© davila7). 893 words, ~2,633 tokens.

Download SKILL.mdSave it as .claude/skills/dnanexus-integration/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
dnanexus-integration
description
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

DNAnexus Integration

Overview

DNAnexus is a cloud platform for biomedical data analysis and genomics. Build and deploy apps/applets, manage data objects, run workflows, and use the dxpy Python SDK for genomics pipeline development and execution.

When to Use This Skill

This skill should be used when:

  • Creating, building, or modifying DNAnexus apps/applets
  • Uploading, downloading, searching, or organizing files and records
  • Running analyses, monitoring jobs, creating workflows
  • Writing scripts using dxpy to interact with the platform
  • Setting up dxapp.json, managing dependencies, using Docker
  • Processing FASTQ, BAM, VCF, or other bioinformatics files
  • Managing projects, permissions, or platform resources

Core Capabilities

The skill is organized into five main areas, each with detailed reference documentation:

1. App Development

Purpose: Create executable programs (apps/applets) that run on the DNAnexus platform.

Key Operations:

  • Generate app skeleton with dx-app-wizard
  • Write Python or Bash apps with proper entry points
  • Handle input/output data objects
  • Deploy with dx build or dx build --app
  • Test apps on the platform

Common Use Cases:

  • Bioinformatics pipelines (alignment, variant calling)
  • Data processing workflows
  • Quality control and filtering
  • Format conversion tools

Reference: See references/app-development.md for:

  • Complete app structure and patterns
  • Python entry point decorators
  • Input/output handling with dxpy
  • Development best practices
  • Common issues and solutions
2. Data Operations

Purpose: Manage files, records, and other data objects on the platform.

Key Operations:

  • Upload/download files with dxpy.upload_local_file() and dxpy.download_dxfile()
  • Create and manage records with metadata
  • Search for data objects by name, properties, or type
  • Clone data between projects
  • Manage project folders and permissions

Common Use Cases:

  • Uploading sequencing data (FASTQ files)
  • Organizing analysis results
  • Searching for specific samples or experiments
  • Backing up data across projects
  • Managing reference genomes and annotations

Reference: See references/data-operations.md for:

  • Complete file and record operations
  • Data object lifecycle (open/closed states)
  • Search and discovery patterns
  • Project management
  • Batch operations
3. Job Execution

Purpose: Run analyses, monitor execution, and orchestrate workflows.

Key Operations:

  • Launch jobs with applet.run() or app.run()
  • Monitor job status and logs
  • Create subjobs for parallel processing
  • Build and run multi-step workflows
  • Chain jobs with output references

Common Use Cases:

  • Running genomics analyses on sequencing data
  • Parallel processing of multiple samples
  • Multi-step analysis pipelines
  • Monitoring long-running computations
  • Debugging failed jobs

Reference: See references/job-execution.md for:

  • Complete job lifecycle and states
  • Workflow creation and orchestration
  • Parallel execution patterns
  • Job monitoring and debugging
  • Resource management
4. Python SDK (dxpy)

Purpose: Programmatic access to DNAnexus platform through Python.

Key Operations:

  • Work with data object handlers (DXFile, DXRecord, DXApplet, etc.)
  • Use high-level functions for common tasks
  • Make direct API calls for advanced operations
  • Create links and references between objects
  • Search and discover platform resources

Common Use Cases:

  • Automation scripts for data management
  • Custom analysis pipelines
  • Batch processing workflows
  • Integration with external tools
  • Data migration and organization

Reference: See references/python-sdk.md for:

  • Complete dxpy class reference
  • High-level utility functions
  • API method documentation
  • Error handling patterns
  • Common code patterns
5. Configuration and Dependencies

Purpose: Configure app metadata and manage dependencies.

Key Operations:

  • Write dxapp.json with inputs, outputs, and run specs
  • Install system packages (execDepends)
  • Bundle custom tools and resources
  • Use assets for shared dependencies
  • Integrate Docker containers
  • Configure instance types and timeouts

Common Use Cases:

  • Defining app input/output specifications
  • Installing bioinformatics tools (samtools, bwa, etc.)
  • Managing Python package dependencies
  • Using Docker images for complex environments
  • Selecting computational resources

Reference: See references/configuration.md for:

  • Complete dxapp.json specification
  • Dependency management strategies
  • Docker integration patterns
  • Regional and resource configuration
  • Example configurations
Show full SKILL.md (337 more words)Show less

Quick Start Examples

Upload and Analyze Data
python
import dxpy

# Upload input file
input_file = dxpy.upload_local_file("sample.fastq", project="project-xxxx")

# Run analysis
job = dxpy.DXApplet("applet-xxxx").run({
    "reads": dxpy.dxlink(input_file.get_id())
})

# Wait for completion
job.wait_on_done()

# Download results
output_id = job.describe()["output"]["aligned_reads"]["$dnanexus_link"]
dxpy.download_dxfile(output_id, "aligned.bam")
Search and Download Files
python
import dxpy

# Find BAM files from a specific experiment
files = dxpy.find_data_objects(
    classname="file",
    name="*.bam",
    properties={"experiment": "exp001"},
    project="project-xxxx"
)

# Download each file
for file_result in files:
    file_obj = dxpy.DXFile(file_result["id"])
    filename = file_obj.describe()["name"]
    dxpy.download_dxfile(file_result["id"], filename)
Create Simple App
python
# src/my-app.py
import dxpy
import subprocess

@dxpy.entry_point('main')
def main(input_file, quality_threshold=30):
    # Download input
    dxpy.download_dxfile(input_file["$dnanexus_link"], "input.fastq")

    # Process
    subprocess.check_call([
        "quality_filter",
        "--input", "input.fastq",
        "--output", "filtered.fastq",
        "--threshold", str(quality_threshold)
    ])

    # Upload output
    output_file = dxpy.upload_local_file("filtered.fastq")

    return {
        "filtered_reads": dxpy.dxlink(output_file)
    }

dxpy.run()

Workflow Decision Tree

When working with DNAnexus, follow this decision tree:

  1. Need to create a new executable?

    • Yes → Use App Development (references/app-development.md)
    • No → Continue to step 2

  2. Need to manage files or data?

    • Yes → Use Data Operations (references/data-operations.md)
    • No → Continue to step 3

  3. Need to run an analysis or workflow?

    • Yes → Use Job Execution (references/job-execution.md)
    • No → Continue to step 4

  4. Writing Python scripts for automation?

    • Yes → Use Python SDK (references/python-sdk.md)
    • No → Continue to step 5

  5. Configuring app settings or dependencies?

    • Yes → Use Configuration (references/configuration.md)

Often you'll need multiple capabilities together (e.g., app development + configuration, or data operations + job execution).

Installation and Authentication

Install dxpy
bash
uv pip install dxpy
Login to DNAnexus
bash
dx login

This authenticates your session and sets up access to projects and data.

Verify Installation
bash
dx --version
dx whoami

Common Patterns

Pattern 1: Batch Processing

Process multiple files with the same analysis:

python
# Find all FASTQ files
files = dxpy.find_data_objects(
    classname="file",
    name="*.fastq",
    project="project-xxxx"
)

# Launch parallel jobs
jobs = []
for file_result in files:
    job = dxpy.DXApplet("applet-xxxx").run({
        "input": dxpy.dxlink(file_result["id"])
    })
    jobs.append(job)

# Wait for all completions
for job in jobs:
    job.wait_on_done()
Pattern 2: Multi-Step Pipeline

Chain multiple analyses together:

python
# Step 1: Quality control
qc_job = qc_applet.run({"reads": input_file})

# Step 2: Alignment (uses QC output)
align_job = align_applet.run({
    "reads": qc_job.get_output_ref("filtered_reads")
})

# Step 3: Variant calling (uses alignment output)
variant_job = variant_applet.run({
    "bam": align_job.get_output_ref("aligned_bam")
})
Pattern 3: Data Organization

Organize analysis results systematically:

python
# Create organized folder structure
dxpy.api.project_new_folder(
    "project-xxxx",
    {"folder": "/experiments/exp001/results", "parents": True}
)

# Upload with metadata
result_file = dxpy.upload_local_file(
    "results.txt",
    project="project-xxxx",
    folder="/experiments/exp001/results",
    properties={
        "experiment": "exp001",
        "sample": "sample1",
        "analysis_date": "2025-10-20"
    },
    tags=["validated", "published"]
)

Best Practices

  1. Error Handling: Always wrap API calls in try-except blocks
  2. Resource Management: Choose appropriate instance types for workloads
  3. Data Organization: Use consistent folder structures and metadata
  4. Cost Optimization: Archive old data, use appropriate storage classes
  5. Documentation: Include clear descriptions in dxapp.json
  6. Testing: Test apps with various input types before production use
  7. Version Control: Use semantic versioning for apps
  8. Security: Never hardcode credentials in source code
  9. Logging: Include informative log messages for debugging
  10. Cleanup: Remove temporary files and failed jobs

Resources

This skill includes detailed reference documentation:

references/
  • app-development.md - Complete guide to building and deploying apps/applets
  • data-operations.md - File management, records, search, and project operations
  • job-execution.md - Running jobs, workflows, monitoring, and parallel processing
  • python-sdk.md - Comprehensive dxpy library reference with all classes and functions
  • configuration.md - dxapp.json specification and dependency management

Load these references when you need detailed information about specific operations or when working on complex tasks.

Getting Help

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in cli-tool/components/skills/scientific/dnanexus-integration of davila7/claude-code-templates.

  • SKILL.md
  • references/app-development.md
  • references/configuration.md
  • references/data-operations.md
  • references/job-execution.md
  • references/python-sdk.md

Open the folder on GitHubat commit 46b4d8b

Used in 11 other repositories

We found 20 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 11 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Singlecell Qcxuzhougeng/wisp-science1k—~1.6kAutomated safety check: PassAGPL-3.0
Trackplotygidtu/trackplot109—~1.9kAutomated safety check: PassBSD-3-Clause
Spatial TranscriptomicsQING1105/ezST101—~1.4kAutomated safety check: PassMIT

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Works with

Categories

Questions about Dnanexus Integration

What does Dnanexus Integration do?

DNAnexus cloud genomics platform. An agent skill from davila7/claude-code-templates. Dnanexus Integration is an agent skill from davila7/claude-code-templates. DNAnexus cloud genomics platform.

When should I use Dnanexus Integration?

Dnanexus Integration fits situations like: tasks that involve Bioinformatics.

How do I install Dnanexus Integration in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill dnanexus-integration -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/dnanexus-integration in davila7/claude-code-templates) into .claude/skills/dnanexus-integration in your project. Claude Code loads it when a task matches its description.

How do I install Dnanexus Integration in Codex?

Run `npx skills add davila7/claude-code-templates --skill dnanexus-integration -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/dnanexus-integration in davila7/claude-code-templates) into .agents/skills/dnanexus-integration in your project. Codex loads it when a task matches its description.

Can I use Dnanexus Integration in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill dnanexus-integration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dnanexus-integration, .gemini/skills/dnanexus-integration, .github/skills/dnanexus-integration and .opencode/skills/dnanexus-integration in your project.

What does Dnanexus Integration need to run?

Going by SKILL.md and its folder, Dnanexus Integration needs the command-line tools its instructions call (uv). Our summary lists: Python 3; Docker.

Does Dnanexus Integration access the network?

SKILL.md names 3 domains. As links in the text: documentation.dnanexus.com, autodoc.dnanexus.com and github.com. This is read from the text; nothing was executed.

Is Dnanexus Integration safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dnanexus Integration use?

Dnanexus Integration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dnanexus Integration use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Dnanexus Integration?

Skills that share tags, products or a category with Dnanexus Integration: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Singlecell Qc (xuzhougeng/wisp-science, 1k stars) and Trackplot (ygidtu/trackplot, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dnanexus Integration?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,483 GitHub stars. The repository holds 478 skills in this directory. The repository was last updated on October 9, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.