Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/synthetic-sciences/openscience/latchbio-integrationnpx skills add synthetic-sciences/openscience --skill latchbio-integrationgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/latchbio-integration)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/latchbio-integration"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/latchbio-integration.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00045 | $0.02279 |
| Opus 5 | $0.00023 | $0.01140 |
| Sonnet 5 | $0.00009 | $0.00456 |
| Haiku 4.5 | $0.00005 | $0.00228 |
Grade A, and why
latchbio-integration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
6 near-identical copies found in the catalogue:
- latchbio-integration — 94% identical, 6 lines differ
- latchbio-integration — 94% identical, 6 lines differ
- latchbio-integration — 91% identical, 7 lines differ
- latchbio-integration — 91% identical, 5 lines differ
- latchbio-integration — 91% identical, 5 lines differ
- latchbio-integration — 89% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
# 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
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)
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 353 lines · 45 tokens per session scan A 2317affc2727
latchbio-integration is a skill published in the GitHub repository synthetic-sciences/openscience (3,432 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 2,279 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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