SciAgent-Skills MCP — Anwendungsfälle, Installieren & Live-Demo

Warum nutzen

Hauptfunktionen

197 specialized life-science skills
Coverage: RNA-seq, single-cell (scRNA), drug discovery, proteomics, variant calling
BixBench accuracy 92.0% — higher than generic agents
Canonical tool invocation (samtools, bcftools, salmon, scanpy, Seurat, etc.)
Reproducibility patterns (conda/mamba envs, config files)
Powers OmicsHorizon platform

Live-Demo

In der Praxis

sciagent-skill.replay ▶ bereit

0/0

Installieren

Wählen Sie Ihren Client

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "sciagent-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/jaechang-hits/SciAgent-Skills",
        "~/.claude/skills/SciAgent-Skills"
      ],
      "_inferred": true
    }
  }
}

Öffne Claude Desktop → Settings → Developer → Edit Config. Nach dem Speichern neu starten.

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "sciagent-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/jaechang-hits/SciAgent-Skills",
        "~/.claude/skills/SciAgent-Skills"
      ],
      "_inferred": true
    }
  }
}

Cursor nutzt das gleiche mcpServers-Schema wie Claude Desktop. Projektkonfiguration schlägt die globale.

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "sciagent-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/jaechang-hits/SciAgent-Skills",
        "~/.claude/skills/SciAgent-Skills"
      ],
      "_inferred": true
    }
  }
}

Klicken Sie auf das MCP-Servers-Symbol in der Cline-Seitenleiste, dann "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "sciagent-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/jaechang-hits/SciAgent-Skills",
        "~/.claude/skills/SciAgent-Skills"
      ],
      "_inferred": true
    }
  }
}

Gleiche Struktur wie Claude Desktop. Windsurf neu starten zum Übernehmen.

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "sciagent-skill",
      "command": "git",
      "args": [
        "clone",
        "https://github.com/jaechang-hits/SciAgent-Skills",
        "~/.claude/skills/SciAgent-Skills"
      ]
    }
  ]
}

Continue nutzt ein Array von Serverobjekten statt einer Map.

~/.config/zed/settings.json

{
  "context_servers": {
    "sciagent-skill": {
      "command": {
        "path": "git",
        "args": [
          "clone",
          "https://github.com/jaechang-hits/SciAgent-Skills",
          "~/.claude/skills/SciAgent-Skills"
        ]
      }
    }
  }
}

In context_servers hinzufügen. Zed lädt beim Speichern neu.

claude mcp add sciagent-skill -- git clone https://github.com/jaechang-hits/SciAgent-Skills ~/.claude/skills/SciAgent-Skills

Einzeiler. Prüfen mit claude mcp list. Entfernen mit claude mcp remove.

Anwendungsfälle

Praxisnahe Nutzung: SciAgent-Skills

How to run a bulk RNA-seq differential expression analysis

👤 Biologists and bioinformaticians with FASTQ files and a need for DE results ⏱ ~120 min advanced

Wann einsetzen: You have paired-end FASTQ, a reference, and a condition table.

Voraussetzungen

Skill installed — git clone https://github.com/jaechang-hits/SciAgent-Skills ~/.claude/skills/sciagent-skills
Conda/mamba + compute — Use a Linux server or HPC; macOS fine for small datasets

Ablauf

Plan the pipeline

Plan a bulk RNA-seq DE workflow for 2 conditions x 3 reps with salmon + tximport + DESeq2. Output expected files per step.✓ Kopiert

→ Step list with tools and intermediate outputs
Run quantification

Run salmon quant for each sample; produce a script.✓ Kopiert

→ Bash script with salmon invocations
DE analysis

Load quants via tximport, run DESeq2, produce MA + volcano plots and a top-50 gene table.✓ Kopiert

→ R script + output files

Ergebnis: A DE table + plots you can hand to a PI or collaborator.

Fallstricke

Mis-matched reference versus annotation GTF — Check release numbers explicitly; salmon and DESeq2 can silently run on mismatched IDs
Low-count filtering removes biological signal — Use independent filtering or raise threshold gradually; don't blanket filter

Single-cell RNA-seq QC and clustering baseline

👤 Researchers starting scRNA analysis from 10x output ⏱ ~90 min advanced

Wann einsetzen: You have a cellranger output directory and need a first-pass Seurat or Scanpy analysis.

Ablauf

Load + QC

Load 10x data from cellranger output, do QC (pct.mt, counts, features), filter, normalize.✓ Kopiert

→ QC plots + filtered object
Cluster

Run PCA, find neighbors, cluster at resolution 0.5, and UMAP.✓ Kopiert

→ Cluster labels + UMAP plot
Marker genes

Find cluster markers, produce a top-5-per-cluster heatmap.✓ Kopiert

→ Marker table + heatmap

Ergebnis: An annotated UMAP and marker table ready for biological interpretation.

Fallstricke

Over-clustering at the default resolution — Try a sweep 0.2-1.0 and pick based on silhouette + biological plausibility

Germline variant calling from WGS

👤 Genomics teams calling SNPs and indels ⏱ ~180 min advanced

Wann einsetzen: You have aligned BAMs and need a VCF using best-practices (GATK).

Ablauf

Plan pipeline

Plan GATK best-practices germline pipeline from aligned BAMs: BQSR, HaplotypeCaller, joint genotyping with GenomicsDBImport.✓ Kopiert

→ Pipeline outline with expected runtimes
Generate scripts

Produce Snakemake rules for each step.✓ Kopiert

→ Snakefile with rules and config

Ergebnis: A reproducible Snakemake pipeline ready to run on your cluster.

Fallstricke

Skipping BQSR on small cohorts — Still do it — GATK's downstream filters assume recalibrated quality scores

Triage small-molecule hits from a screen

👤 Med-chem groups triaging HTS results ⏱ ~60 min advanced

Wann einsetzen: You have a hit list and want to rank by drug-likeness + novelty.

Ablauf

Filter by Lipinski + PAINS

Compute Lipinski and PAINS flags on this SMILES list, output a filtered table.✓ Kopiert

→ RDKit-based script + filtered CSV
Similarity to known drugs

For remaining hits, compute Tanimoto similarity to ChEMBL approved drugs; flag >0.85 as known-scaffold.✓ Kopiert

→ Similarity table with flags

Ergebnis: A triaged hit list prioritized for follow-up.

Kombinationen

Mit anderen MCPs für 10-fache Wirkung

sciagent-skill + filesystem

Read local FASTQ/BAM files and have SciAgent plan the analysis pipeline on actual data

List the FASTQ files in ~/data/rnaseq/, then use SciAgent to plan a salmon + DESeq2 pipeline for these samples.✓ Kopiert

sciagent-skill + github

Search nf-core or Bioconductor repos for reference implementations, then adapt with SciAgent

Search nf-core/rnaseq on GitHub for how they handle trimming, then use SciAgent to adapt that step for my pipeline.✓ Kopiert

Werkzeuge

Was dieses MCP bereitstellt

Werkzeug	Eingaben	Wann aufrufen	Kosten
Bulk RNA-seq workflow	FASTQ + design	Standard DE analysis	compute
scRNA-seq workflow	10x output	Single-cell baseline	compute
Variant calling	BAMs	WGS/WES cohorts	compute
Proteomics analysis	MS data	MS-based proteomics	compute
Drug discovery triage	SMILES list	Hit triage	compute

Kosten & Limits

Was der Betrieb kostet

API-Kontingent: None at skill level
Tokens pro Aufruf: 10-50k per pipeline design
Kosten in €: Free skills; compute costs depend on dataset size
Tipp: Plan the pipeline as a script first, then run; don't keep Claude in the loop during long compute.

Sicherheit

Rechte, Secrets, Reichweite

Credential-Speicherung: No credentials

Datenabfluss: Pipeline designs and snippets go to Claude. Actual patient data should stay on HIPAA-compliant compute.

Do not paste patient identifiers or raw sequence data that could be re-identified into prompts.
Always review generated variant-calling parameters — wrong caller settings can produce misleading results.

Fehlerbehebung

Häufige Fehler und Lösungen

tximport fails on missing transcript-to-gene mapping

Regenerate tx2gene from the same GTF you used for the salmon index; mismatch is the usual cause.

Seurat object too large for available RAM

Subsample or switch to on-disk storage with BPCells or DelayedArray-backed Seurat

Conda environment conflicts during pipeline setup

Use mamba instead of conda for faster resolution; pin exact versions in environment.yml rather than floating constraints

Prüfen: mamba env create -f environment.yml --dry-run

Generated script references wrong genome build (hg19 vs hg38)

Explicitly state the genome build in your first prompt; SciAgent defaults may not match your data

Alternativen

SciAgent-Skills vs. andere

Alternative	Wann stattdessen	Kompromiss
Galaxy / nf-core	You want audited, community pipelines rather than LLM-generated scripts	Less conversational; slower to customize

Mehr

Ressourcen

📖 Offizielle README auf GitHub lesen

🐙 Offene Issues ansehen

🔍 Alle 400+ MCP-Server und Skills durchsuchen