Mmcp.market

latchbio-integration skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Build, register, debug, and operate bioinformatics workflows on Latch using the Python SDK, CLI, Latch Data and Registry, Nextflow, Snakemake, programmatic execution, and Latch MCP. Use when authoring or deploying Latch workflows, configuring resources or interfaces, moving data, integrating Registry, or launching and monitoring runs.

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Install the latchbio-integration skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/latchbio-integration ~/.claude/skills/latchbio-integration
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

Before relying on a symbol, run scripts/inspectlatchsdk.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

flag path, or the separately pinned Snakemake v2 tutorial track.

  • Confirm the installed SDK and Python version.
  • Identify whether the project is Python, Nextflow, the legacy Snakemake
  1. Define a typed interface

retrieval. Isolate documented exceptions such as workflow_reference, which resolves the active workspace when its decorator is evaluated.

  • Annotate every workflow and task input and output.
  • Keep module import time free of network calls, data mutations, and secret
  • Use dataclasses and enums for structured parameters.
  1. Configure metadata and resources

requirements justify it.

  • Match metadata parameter keys to the workflow signature.
  • Start with named task decorators, then use custom_task only when measured
  1. 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.

  1. 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.

  1. Launch only after reviewing cost and parameters
  • Prefer the Console or Latch MCP for interactive operation.
  • Prefer latchcli.services.launch.launchv2 for Python automation.
  • Do not use the deprecated latch launch CLI as a new integration pattern.
  1. Monitor and verify

validation.

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

Operational Safety

batch runs.

  • Ask for confirmation before launching paid compute, especially GPU or large

overwriting shared destinations.

  • Ask for confirmation before LPath.rmr, latch rmr, Registry deletion, or

intended service, and never return it as workflow output.

  • Never log secrets, SDK tokens, signed URLs, or secret values.
  • Call get_secret() only inside a task, use the returned value only for its

with subprocess.run(..., check=True).

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