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LatchBio Integration · lingxling | DecimalAI

scienceby lingxling

LatchBio Integration

lingxling/latchbio-integration

Source lingxling/awesome-skills-cn ↗License MIT

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

SkillScore92Maintained

Automatic

Data Engineer pack

Last verified 8/28/2026

What it changes

Passed 22 of 24 test cases with the skill, and 19 of 24 without it.

Without skill79%

With skill92%

Checks it passed that the model alone missed

Decorates the task using @large_gpu_task.
Imports large_gpu_task from the latch package.
Documents workflow input parameters within the function docstring.

and 2 more in the full report

4 fixed18 passed either way1 still failing1 broke

Pass-rate lift: +13 pp
Tokens: +125%
Agent turns: 0%
Response time: -18%

Model gemini-3.6-flashMeasured 8/28/2026v122 of 24 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/lingxling-latchbio-integration/SKILL.md https://app.decimal.ai/s/lingxling-latchbio-integration/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add lingxling-latchbio-integration, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/lingxling-latchbio-integration/ and pins it in .decimal/skills.lock

decimalai skills add lingxling-latchbio-integration

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/lingxling-latchbio-integration/SKILL.md https://app.decimal.ai/s/lingxling-latchbio-integration/SKILL.md

Raw file: /s/lingxling-latchbio-integration/SKILL.md ↗ · machine-readable summary at /s/lingxling-latchbio-integration.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 4

Prompt

I am writing a Python task for high-throughput GPU-accelerated deep learning inference on molecular structures. I want to decorate the task function using the platform's standard pre-configured decorator for heavy GPU compute workloads rather than standard CPU decorators. How should the task function be decorated?

What was checked, without and with the skill
Checked	Without skill	With skill
Decorates the task using @large_gpu_task.	Missed	Passed
Imports large_gpu_task from the latch package.	Missed	Passed

case-03 · gemini-3.6-flash · 8/28/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md342 lines5 files

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

Track record

Benchmark

22 / 24

test cases passed, vs 19 / 24 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

lingxling / awesome-skills-cn

claude-scientific-skills/skills/latchbio-integration/SKILL.md

View on GitHub ↗

License UnknownImported 8/3/2026Version v1Bundled 4 files

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Ready to supercharge your AI agents?

See it in action

What's inside

LatchBio Integration

Overview

Core Capabilities

1. Workflow Creation and Deployment

2. Data Management

3. Resource Configuration

4. Verified Workflows

Quick Start

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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