SciAgent-Skills — Installer & Démo en direct

Pourquoi l'utiliser

Fonctionnalités clés

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

Démo en direct

Aperçu en pratique

sciagent-skill.replay ▶ prêt

0/0

Installation

Dépôt de l’auteur · Documentation officielle des Skills Claude

Cas d'usage

Usages concrets : 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

Quand l'utiliser : You have paired-end FASTQ, a reference, and a condition table.

Déroulement

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.✓ Copié

→ Step list with tools and intermediate outputs
Run quantification

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

→ Bash script with salmon invocations
DE analysis

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

→ R script + output files

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

Pièges

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

Quand l'utiliser : You have a cellranger output directory and need a first-pass Seurat or Scanpy analysis.

Déroulement

Load + QC

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

→ QC plots + filtered object
Cluster

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

→ Cluster labels + UMAP plot
Marker genes

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

→ Marker table + heatmap

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

Pièges

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

Quand l'utiliser : You have aligned BAMs and need a VCF using best-practices (GATK).

Déroulement

Plan pipeline

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

→ Pipeline outline with expected runtimes
Generate scripts

Produce Snakemake rules for each step.✓ Copié

→ Snakefile with rules and config

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

Pièges

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

Quand l'utiliser : You have a hit list and want to rank by drug-likeness + novelty.

Déroulement

Filter by Lipinski + PAINS

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

→ 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.✓ Copié

→ Similarity table with flags

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

Combinaisons

Associez-le à d'autres MCPs pour un effet X10

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.✓ Copié

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.✓ Copié

Outils

Ce que ce MCP expose

Outil	Entrées	Quand appeler	Coût
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

Coût et limites

Coût d'exécution

Quota d'API: None at skill level
Tokens par appel: 10-50k per pipeline design
Monétaire: Free skills; compute costs depend on dataset size
Astuce: Plan the pipeline as a script first, then run; don't keep Claude in the loop during long compute.

Sécurité

Permissions, secrets, portée

Stockage des identifiants : No credentials

Sortie de données : 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.

Dépannage

Erreurs courantes et correctifs

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

Vérifier : 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

Alternatives

SciAgent-Skills vs autres

Alternative	Quand l'utiliser	Compromis
Galaxy / nf-core	You want audited, community pipelines rather than LLM-generated scripts	Less conversational; slower to customize

Plus

Ressources

📖 Lire le README officiel sur GitHub

🐙 Voir les issues ouvertes

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