Agent skill · Data & Analytics

lobster-bioinformatics

Run bioinformatics analyses using Lobster AI - single-cell RNA-seq, bulk RNA-seq, literature mining, dataset discovery, quality control, and visualization. Use when analyzing genomics data, searching for papers/datasets, or working with H5AD, CSV, GEO/SRA accessions, or biological data. Requires lobster-ai package installed.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill lobster-bioinformatics --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 10 KB
Bundled scripts: yes
Path: skills/lobster-bioinformatics/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Lobster Bioinformatics Agent Lobster AI is a bioinformatics platform that combines specialized AI agents with open-source tools to analyze multi-omics data through natural language. ## When to use this Skill Use Lobster when the user asks to: - Analyze single-cell RNA-seq data (QC, clustering, annotation, markers) - Perform bulk RNA-seq analysis (differential expression, complex designs) - Search scientific literature (PubMed, PMC, full-text retrieval) - Discover datasets (GEO, SRA, ENA (free) and PRIDE, MASSive (cloud)) - Run quality control on biological data - Generate bioinformatics visualizations (UMAP, volcano plots, heatmaps) - Download and process biological datasets - Work with H5AD, CSV, Excel, 10X formats - Extract methods or metadata from papers ## Requirements Lobster must be installed and configured: ```bash # Check if Lobster is installed which lobster # If not installed: uv pip install lobster-ai lobster init --help #to see non-interactive ``` Lobster requires an LLM provider (Ollama, Anthropic, or AWS Bedrock). ## Pre-flight check (IMPORTANT) **Before running any analysis, always verify Lobster is ready:** ```bash lobster config-test --json ``` Returns structured

What's inside
Steps it walks through
  1. When to use this Skill
  2. Requirements
  3. Pre-flight check (IMPORTANT)
  4. Usage
  5. Basic syntax
  6. Session continuity (multi-turn conversations)
  7. Common patterns
  8. Output handling
  9. Integration workflow
  10. Advanced features
  11. Troubleshooting
  12. Tips for effective use
  13. Limitations
  14. Documentation
Ships with 2 files
  • README.md
  • install.sh
Commands it runs
Check if Lobster is installed
which lobster
If not installed:
uv pip install lobster-ai
lobster init --help #to see non-interactive
lobster config-test --json
Show configuration values (masked)
lobster config-show
Show subscription tier and available agents
lobster status
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the lobster-bioinformatics skill do?

Run bioinformatics analyses using Lobster AI - single-cell RNA-seq, bulk RNA-seq, literature mining, dataset discovery, quality control, and visualization. Use when analyzing genomics data, searching for papers/datasets, or working with H5AD, CSV, GEO/SRA accessions, or biological data. Requires lobster-ai package installed.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill lobster-bioinformatics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going