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LatchBio Integration

Free

Streamline bioinformatics workflows with Latch.

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Free · Opens the source repo

What LatchBio Integration does

LatchBio Integration is designed for bioinformaticians and developers looking to build, register, and debug workflows using the Latch platform. This skill leverages the Latch SDK, enabling users to create and manage workflows efficiently through Python, Nextflow, and Snakemake. With support for configuring resources such as CPU, memory, and storage, it provides the flexibility needed for various computational tasks in bioinformatics.

The skill simplifies the process of integrating with Latch Data and the Registry, allowing users to read and update projects and records seamlessly. It also facilitates the design of workflow forms and launch plans, making it easier to manage complex workflows. By utilizing Latch MCP, users can launch and monitor workflows programmatically, ensuring that they can track their processes effectively.

For those who are new to Latch or looking to enhance their existing workflows, this skill provides a comprehensive set of references tailored to specific tasks. Whether you need to manage data, configure resources, or debug workflows, the skill offers clear guidance on which reference documents to consult. This targeted approach helps streamline the development process and reduces the learning curve associated with using the Latch platform.

Overall, LatchBio Integration is a valuable tool for anyone involved in bioinformatics workflow development, providing the necessary tools to create robust and efficient pipelines while ensuring operational safety and compliance with best practices.

When to use it

Use this skill when developing or deploying workflows on Latch, particularly when working with Python SDK, Nextflow, or Snakemake.

When not to use it

This skill may not be suitable for workflows outside the Latch ecosystem or for users not working with bioinformatics applications.

What you can build with it

Building a New Workflow

Use LatchBio Integration to create a new bioinformatics workflow using the Latch SDK, ensuring all dependencies are correctly managed.

Debugging Existing Workflows

Leverage the debugging capabilities of this skill to troubleshoot and refine existing workflows on the Latch platform.

Integrating with Latch Registry

Utilize the skill to read and update projects and records in the Latch Registry, facilitating better data management.

How to install LatchBio Integration

View source

1. Install with the skills CLI

npx skills add k-dense-ai/scientific-agent-skills/latchbio-integration --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by k-dense-ai

LatchBio Integration

Current Baseline

This skill targets Latch SDK 2.76.8, released July 10, 2026. The package metadata supports Python 3.9–3.12 and declares Python 3.9+.

Treat the installed package and its changelog as authoritative when a guide disagrees with the SDK. Some Latch guides retain older Python ranges or compatibility-specific pre-release pins, especially the Snakemake v2 tutorial. Never combine commands or imports from different tracks without checking their version requirements.

When to Use

Use this skill to:

  • Create or maintain Python SDK workflows and task graphs
  • Package and register Python, Nextflow, or Snakemake pipelines
  • Configure task CPU, memory, storage, GPU, caching, retries, and timeouts
  • Work with Latch Data through LPath, LatchFile, LatchDir, or the CLI
  • Read or update Latch Registry projects, tables, and records
  • Design workflow forms, launch plans, samplesheets, messages, and result links
  • Stage and debug workflow images with latch register --staging and latch develop
  • Launch and monitor workflows through Python or Latch MCP
  • Discover and use ready-to-run Latch workflows

Route to the Right Reference

Read only the references needed for the task:

NeedReference
Python workflows, tasks, maps, conditions, cachingreferences/workflow-creation.md
LPath, legacy file types, Latch URLs, data CLIreferences/data-management.md
Registry reads, transactions, samplesheetsreferences/registry.md
CPU, memory, storage, GPU, dynamic resourcesreferences/resource-configuration.md
Nextflow and Snakemake packagingreferences/nextflow-snakemake.md
Metadata, forms, launch plans, messages, automationsreferences/ui-and-automation.md
Registration, development, execution, monitoringreferences/operations-and-debugging.md
Ready-to-use workflows and latch.verifiedreferences/verified-workflows.md
Remote MCP setup and tool workflowreferences/latch-mcp.md

Before relying on a symbol, run scripts/inspect_latch_sdk.py against the target SDK version. It performs local imports only and does not authenticate or make network requests.

Installation and Authentication

For a reproducible environment:

uv venv --python 3.12
source .venv/bin/activate
uv pip install "latch==2.76.8"

On Windows, use WSL for the documented Linux workflow tooling.

Authenticate through the supported OAuth flow; do not read, print, copy, or parse ~/.latch/token manually:

latch login
latch workspace

Select a workspace non-interactively when its numeric ID is already known:

latch workspace --id 12345

latch login credentials are for the SDK and CLI. Latch MCP uses a separate OAuth authorization and its credentials cannot be reused for general SDK access.

Fast Path

Create and remotely register the maintained subprocess template:

latch init covid-wf --template subprocess
latch register --yes --open covid-wf

Remote image building is the default. Use --no-remote only when a local Docker daemon is available and a local build is intentional.

Minimal Python Workflow

Keep workflow bodies declarative: invoke tasks and return their promises. Perform computation and side effects inside tasks.

from latch import small_task, workflow


@small_task
def reverse_complement(sequence: str) -> str:
    table = str.maketrans("ACGTacgt", "TGCAtgca")
    return sequence.translate(table)[::-1]


@workflow
def reverse_complement_workflow(sequence: str) -> str:
    """Return the reverse complement of a DNA sequence."""
    return reverse_complement(sequence=sequence)

Use @workflow(metadata) when the generated interface needs custom labels, sections, validation rules, samplesheets, or documentation links. Use LatchFile or LatchDir for automatic task input staging and output upload; use LPath for imperative remote path operations.

Recommended Development Lifecycle

  1. Inspect compatibility

    • Confirm the installed SDK and Python version.
    • Identify whether the project is Python, Nextflow, the legacy Snakemake flag path, or the separately pinned Snakemake v2 tutorial track.
  2. Define a typed interface

    • Annotate every workflow and task input and output.
    • Keep module import time free of network calls, data mutations, and secret retrieval. Isolate documented exceptions such as workflow_reference, which resolves the active workspace when its decorator is evaluated.
    • Use dataclasses and enums for structured parameters.
  3. Configure metadata and resources

    • Match metadata parameter keys to the workflow signature.
    • Start with named task decorators, then use custom_task only when measured requirements justify it.
  4. Validate in the execution image

    Fresh Nextflow and Snakemake projects must generate their version-compatible Python entrypoint before staging. In SDK 2.76.8, the staging branch does not generate one from --nf-script or --snakefile.

    latch register --staging .
    latch develop .
    

    Re-run staging registration after changing the Dockerfile or dependencies. Edits made inside the development container are not synced back.

  5. Register deliberately

    latch register --yes --open .
    

    Useful controls:

    latch register --workspace-id 12345 .
    latch register --mark-as-release .
    latch register --workflow-module wf.custom_entrypoint .
    

    Duplicate registration exits with status 2; it is not the same as a build failure.

  6. Launch only after reviewing cost and parameters

    • Prefer the Console or Latch MCP for interactive operation.
    • Prefer latch_cli.services.launch.launch_v2 for Python automation.
    • Do not use the deprecated latch launch CLI as a new integration pattern.
  7. Monitor and verify

    • Check terminal status, task logs, result links, and scientific outputs.
    • Treat successful orchestration as necessary but not sufficient scientific validation.

Operational Safety

  • Ask for confirmation before launching paid compute, especially GPU or large batch runs.
  • Ask for confirmation before LPath.rmr, latch rmr, Registry deletion, or overwriting shared destinations.
  • Never log secrets, SDK tokens, signed URLs, or secret values.
  • Call get_secret() only inside a task, use the returned value only for its intended service, and never return it as workflow output.
  • Do not pass untrusted strings through shell commands. Prefer argument lists with subprocess.run(..., check=True).
  • Pin the SDK and workflow dependencies for releases. Upgrade only after reviewing the changelog and re-running staging tests.
  • Treat generated files as generated: customize the documented extension file rather than editing output that the CLI will overwrite.

Inspect the Installed SDK

From this skill directory:

uv run --no-project --python 3.12 --with "latch==2.76.8" \
  python scripts/inspect_latch_sdk.py

Use JSON output for automated comparisons:

uv run --no-project --python 3.12 --with "latch==2.76.8" \
  python scripts/inspect_latch_sdk.py --json

Authoritative Sources

Frequently asked questions about LatchBio Integration

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