Agent skill

Latchbio Integration

by davila7 in davila7/claude-code-templates

Latch platform for bioinformatics workflows. An agent skill from davila7/claude-code-templates.

MITAuto-check passedResearch & Science

Install Latchbio Integration

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

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

GitHub CLI
$ gh skill install davila7/claude-code-templates latchbio-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/latchbio-integration .claude/skills/latchbio-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
latchbio-integration
GitHub stars
32k
Used in
11 other repos
Token cost
~2.4k tokens
SKILL.md length
789 words
Files
5 (incl. references)
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Latch platform for bioinformatics workflows. An agent skill from davila7/claude-code-templates.

  • Works in 4 steps: Workflow Creation and Deployment → Data Management → Resource Configuration → …
  • Tasks that involve Reproducible research
  • SKILL.md covers Overview, Core Capabilities, Quick Start and When to Use This Skill, plus 6 more sections
  • Calls python3

What it does

Latchbio Integration is an agent skill from davila7/claude-code-templates. Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/data-management.md`, `references/resource-configuration.md` and `references/verified-workflows.md`).

It sits in Research & Science, covering Reproducible research, Bioinformatics and Serverless. It works with Nextflow and 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 Reproducible research
  • Tasks that involve Bioinformatics
  • Tasks that involve Serverless

Example prompts

  • “/latchbio-integration”

Requirements

  • Python 3
  • Docker

Workflow steps

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

  1. Workflow Creation and Deployment
  2. Data Management
  3. Resource Configuration
  4. Verified Workflows

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. 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:

    • python3

    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):

    • docs.latch.bio
    • github.com
    • blog.latch.bio

    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

Latchbio Integration loads about 2.4k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 52 tokens; SKILL.md has 789 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

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 14680ec, republished under its MIT licence (© davila7). 789 words, ~2,439 tokens.

Download SKILL.mdSave it as .claude/skills/latchbio-integration/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
latchbio-integration
description
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

LatchBio Integration

Overview

Latch is a Python framework for building and deploying bioinformatics workflows as serverless pipelines. Built on Flyte, create workflows with @workflow/@task decorators, manage cloud data with LatchFile/LatchDir, configure resources, and integrate Nextflow/Snakemake pipelines.

Core Capabilities

The Latch platform provides four main areas of functionality:

1. Workflow Creation and Deployment
  • Define serverless workflows using Python decorators
  • Support for native Python, Nextflow, and Snakemake pipelines
  • Automatic containerization with Docker
  • Auto-generated no-code user interfaces
  • Version control and reproducibility
2. Data Management
  • Cloud storage abstractions (LatchFile, LatchDir)
  • Structured data organization with Registry (Projects → Tables → Records)
  • Type-safe data operations with links and enums
  • Automatic file transfer between local and cloud
  • Glob pattern matching for file selection
3. Resource Configuration
  • Pre-configured task decorators (@small_task, @large_task, @small_gpu_task, @large_gpu_task)
  • Custom resource specifications (CPU, memory, GPU, storage)
  • GPU support (K80, V100, A100)
  • Timeout and storage configuration
  • Cost optimization strategies
4. Verified Workflows
  • Production-ready pre-built pipelines
  • Bulk RNA-seq, DESeq2, pathway analysis
  • AlphaFold and ColabFold for protein structure prediction
  • Single-cell tools (ArchR, scVelo, emptyDropsR)
  • CRISPR analysis, phylogenetics, and more

Quick Start

Installation and Setup
bash
# Install Latch SDK
python3 -m uv pip install latch

# Login to Latch
latch login

# Initialize a new workflow
latch init my-workflow

# Register workflow to platform
latch register my-workflow

Prerequisites:

  • Docker installed and running
  • Latch account credentials
  • Python 3.8+
Basic Workflow Example
python
from latch import workflow, small_task
from latch.types import LatchFile

@small_task
def process_file(input_file: LatchFile) -> LatchFile:
    """Process a single file"""
    # Processing logic
    return output_file

@workflow
def my_workflow(input_file: LatchFile) -> LatchFile:
    """
    My bioinformatics workflow

    Args:
        input_file: Input data file
    """
    return process_file(input_file=input_file)

When to Use This Skill

This skill should be used when encountering any of the following scenarios:

Workflow Development:

  • "Create a Latch workflow for RNA-seq analysis"
  • "Deploy my pipeline to Latch"
  • "Convert my Nextflow pipeline to Latch"
  • "Add GPU support to my workflow"
  • Working with @workflow, @task decorators

Data Management:

  • "Organize my sequencing data in Latch Registry"
  • "How do I use LatchFile and LatchDir?"
  • "Set up sample tracking in Latch"
  • Working with latch:/// paths

Resource Configuration:

  • "Configure GPU for AlphaFold on Latch"
  • "My task is running out of memory"
  • "How do I optimize workflow costs?"
  • Working with task decorators

Verified Workflows:

  • "Run AlphaFold on Latch"
  • "Use DESeq2 for differential expression"
  • "Available pre-built workflows"
  • Using latch.verified module

Detailed Documentation

This skill includes comprehensive reference documentation organized by capability:

references/workflow-creation.md

Read this for:

  • Creating and registering workflows
  • Task definition and decorators
  • Supporting Python, Nextflow, Snakemake
  • Launch plans and conditional sections
  • Workflow execution (CLI and programmatic)
  • Multi-step and parallel pipelines
  • Troubleshooting registration issues

Key topics:

  • latch init and latch register commands
  • @workflow and @task decorators
  • LatchFile and LatchDir basics
  • Type annotations and docstrings
  • Launch plans with preset parameters
  • Conditional UI sections
references/data-management.md

Read this for:

  • Cloud storage with LatchFile and LatchDir
  • Registry system (Projects, Tables, Records)
  • Linked records and relationships
  • Enum and typed columns
  • Bulk operations and transactions
  • Integration with workflows
  • Account and workspace management

Key topics:

  • latch:/// path format
  • File transfer and glob patterns
  • Creating and querying Registry tables
  • Column types (string, number, file, link, enum)
  • Record CRUD operations
  • Workflow-Registry integration
references/resource-configuration.md

Read this for:

  • Task resource decorators
  • Custom CPU, memory, GPU configuration
  • GPU types (K80, V100, A100)
  • Timeout and storage settings
  • Resource optimization strategies
  • Cost-effective workflow design
  • Monitoring and debugging

Key topics:

  • @small_task, @large_task, @small_gpu_task, @large_gpu_task
  • @custom_task with precise specifications
  • Multi-GPU configuration
  • Resource selection by workload type
  • Platform limits and quotas
Show full SKILL.md (300 more words)Show less
references/verified-workflows.md

Read this for:

  • Pre-built production workflows
  • Bulk RNA-seq and DESeq2
  • AlphaFold and ColabFold
  • Single-cell analysis (ArchR, scVelo)
  • CRISPR editing analysis
  • Pathway enrichment
  • Integration with custom workflows

Key topics:

  • latch.verified module imports
  • Available verified workflows
  • Workflow parameters and options
  • Combining verified and custom steps
  • Version management

Common Workflow Patterns

Complete RNA-seq Pipeline
python
from latch import workflow, small_task, large_task
from latch.types import LatchFile, LatchDir

@small_task
def quality_control(fastq: LatchFile) -> LatchFile:
    """Run FastQC"""
    return qc_output

@large_task
def alignment(fastq: LatchFile, genome: str) -> LatchFile:
    """STAR alignment"""
    return bam_output

@small_task
def quantification(bam: LatchFile) -> LatchFile:
    """featureCounts"""
    return counts

@workflow
def rnaseq_pipeline(
    input_fastq: LatchFile,
    genome: str,
    output_dir: LatchDir
) -> LatchFile:
    """RNA-seq analysis pipeline"""
    qc = quality_control(fastq=input_fastq)
    aligned = alignment(fastq=qc, genome=genome)
    return quantification(bam=aligned)
GPU-Accelerated Workflow
python
from latch import workflow, small_task, large_gpu_task
from latch.types import LatchFile

@small_task
def preprocess(input_file: LatchFile) -> LatchFile:
    """Prepare data"""
    return processed

@large_gpu_task
def gpu_computation(data: LatchFile) -> LatchFile:
    """GPU-accelerated analysis"""
    return results

@workflow
def gpu_pipeline(input_file: LatchFile) -> LatchFile:
    """Pipeline with GPU tasks"""
    preprocessed = preprocess(input_file=input_file)
    return gpu_computation(data=preprocessed)
Registry-Integrated Workflow
python
from latch import workflow, small_task
from latch.registry.table import Table
from latch.registry.record import Record
from latch.types import LatchFile

@small_task
def process_and_track(sample_id: str, table_id: str) -> str:
    """Process sample and update Registry"""
    # Get sample from registry
    table = Table.get(table_id=table_id)
    records = Record.list(table_id=table_id, filter={"sample_id": sample_id})
    sample = records[0]

    # Process
    input_file = sample.values["fastq_file"]
    output = process(input_file)

    # Update registry
    sample.update(values={"status": "completed", "result": output})
    return "Success"

@workflow
def registry_workflow(sample_id: str, table_id: str):
    """Workflow integrated with Registry"""
    return process_and_track(sample_id=sample_id, table_id=table_id)

Best Practices

Workflow Design
  1. Use type annotations for all parameters
  2. Write clear docstrings (appear in UI)
  3. Start with standard task decorators, scale up if needed
  4. Break complex workflows into modular tasks
  5. Implement proper error handling
Data Management
  1. Use consistent folder structures
  2. Define Registry schemas before bulk entry
  3. Use linked records for relationships
  4. Store metadata in Registry for traceability
Resource Configuration
  1. Right-size resources (don't over-allocate)
  2. Use GPU only when algorithms support it
  3. Monitor execution metrics and optimize
  4. Design for parallel execution when possible
Development Workflow
  1. Test locally with Docker before registration
  2. Use version control for workflow code
  3. Document resource requirements
  4. Profile workflows to determine actual needs

Troubleshooting

Common Issues

Registration Failures:

  • Ensure Docker is running
  • Check authentication with latch login
  • Verify all dependencies in Dockerfile
  • Use --verbose flag for detailed logs

Resource Problems:

  • Out of memory: Increase memory in task decorator
  • Timeouts: Increase timeout parameter
  • Storage issues: Increase ephemeral storage_gib

Data Access:

  • Use correct latch:/// path format
  • Verify file exists in workspace
  • Check permissions for shared workspaces

Type Errors:

  • Add type annotations to all parameters
  • Use LatchFile/LatchDir for file/directory parameters
  • Ensure workflow return type matches actual return

Additional Resources

Support

For issues or questions:

  1. Check documentation links above
  2. Search GitHub issues
  3. Ask in Slack community
  4. Contact support@latch.bio

© 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 4 other files (references) in cli-tool/components/skills/scientific/latchbio-integration of davila7/claude-code-templates.

  • SKILL.md
  • references/data-management.md
  • references/resource-configuration.md
  • references/verified-workflows.md
  • references/workflow-creation.md

Open the folder on GitHubat commit 14680ec

Used in 11 other repositories

We found 15 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

Latchbio Integration next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Latchbio Integration compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Latchbio Integration this skilldavila7/claude-code-templates32k11 repos~2.4kAutomated safety check: PassMIT
Latchbio IntegrationK-Dense-AI/scientific-agent-skills48k1 repos~2.5kAutomated safety check: NotesMIT
PacsomaticK-Dense-AI/scientific-agent-skills48k1 repos~1.6kAutomated safety check: PassMIT
Dnanexus IntegrationK-Dense-AI/scientific-agent-skills48k1 repos~3.1kAutomated safety check: PassMIT
Repro EnforcerClawBio/ClawBio1.2k3 repos~413Automated safety check: PassMIT
Nfcore Rnaseq WrapperClawBio/ClawBio1.2k1 repos~8.9kAutomated safety check: PassMIT

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Works with

Questions about Latchbio Integration

What does Latchbio Integration do?

Latch platform for bioinformatics workflows. An agent skill from davila7/claude-code-templates. Latchbio Integration is an agent skill from davila7/claude-code-templates. Latch platform for bioinformatics workflows.

When should I use Latchbio Integration?

Latchbio Integration fits situations like: tasks that involve Reproducible research; tasks that involve Bioinformatics; tasks that involve Serverless.

How do I install Latchbio Integration in Claude Code?

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

How do I install Latchbio Integration in Codex?

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

Can I use Latchbio 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 latchbio-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/latchbio-integration, .gemini/skills/latchbio-integration, .github/skills/latchbio-integration and .opencode/skills/latchbio-integration in your project.

What does Latchbio Integration need to run?

Going by SKILL.md and its folder, Latchbio Integration needs the command-line tools its instructions call (python3). Our summary lists: Python 3; Docker.

Does Latchbio Integration access the network?

SKILL.md names 3 domains. As links in the text: docs.latch.bio, github.com and blog.latch.bio. This is read from the text; nothing was executed.

Is Latchbio 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 Latchbio Integration use?

Latchbio 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 Latchbio Integration use?

About 2.4k tokens (SKILL.md is roughly 9.8k 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 9k tokens, read only when the agent opens those files.

What are the alternatives to Latchbio Integration?

Skills that share tags, products or a category with Latchbio Integration: Latchbio Integration (K-Dense-AI/scientific-agent-skills, 48k stars), Pacsomatic (K-Dense-AI/scientific-agent-skills, 48k stars), Dnanexus Integration (K-Dense-AI/scientific-agent-skills, 48k stars) and Repro Enforcer (ClawBio/ClawBio, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Latchbio Integration?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 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.