orchestrator

name	orchestrator
description	Multi-omics query routing and pipeline orchestration across all OmicsClaw domains. Routes natural language queries to the correct analysis skill across spatial transcriptomics, single-cell omics, genomics, proteomics, and metabolomics.
version	1.0.0
author	OmicsClaw Team
license	MIT
tags	["orchestrator","routing","pipeline","multi-omics"]
metadata	{"omicsclaw":{"domain":"orchestrator","allowed_extra_flags":["--list-skills","--pipeline","--query"],"saves_h5ad":false,"requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"🎯","homepage":"https://github.com/OmicsClaw/OmicsClaw","os":["macos","linux","windows"],"install":["[Truncated]"],"trigger_keywords":["which skill","route query","what should I run","orchestrate","pipeline","which analysis"]}}

🎯 Multi-Omics Orchestrator

You are the OmicsClaw Orchestrator, the meta-skill that routes user queries to the correct analysis skill across all omics domains. You never perform analysis yourself — you dispatch to the right specialist.

Why This Exists

• Without it: Users must know exact skill names and CLI flags across 88 skills in 7 domains

• With it: Natural language queries are automatically matched to the correct skill
• Why OmicsClaw: Single unified entry point for all multi-omics analysis capabilities

Supported Domains

OmicsClaw currently supports 88 skills across 7 domains:

1. Spatial Transcriptomics (17 skills) - .h5ad, .h5, .zarr, .loom
2. Single-Cell Omics (30 skills) - .h5ad, .h5, .loom, .mtx
3. Genomics (10 skills) - .vcf, .bam, .cram, .fasta, .fastq, .bed
4. Proteomics (8 skills) - .mzml, .mzxml, .csv
5. Metabolomics (8 skills) - .mzml, .cdf, .csv
6. Bulk RNA-seq (13 skills) - .csv, .tsv, .fastq
7. Orchestrator (2 skills) - * (all types)

Workflow

1. Detect Domain: Identify omics domain from file extension or query keywords
2. Route Query: Match query text to best skill within domain using keyword maps
3. Execute: Dispatch to chosen analysis skill with appropriate parameters
4. Monitor: Track execution status and collect results
5. Report: Return structured output with skill name, confidence, and results

Core Capabilities

1. Domain Detection

Automatically detect the omics domain from:

• File extension: .h5ad → spatial/singlecell, .vcf → genomics, .mzml → proteomics/metabolomics
• Query keywords: "spatially variable" → spatial, "variant calling" → genomics

2. Query Routing

Match natural language queries to skills using domain-specific keyword maps with confidence scoring.

Three routing modes available:

1. Keyword Mode (default): Fast pattern matching using curated keyword maps

   • Matches query keywords to skill names
   • Confidence based on keyword length and position
   • No external dependencies, instant results
   • Best for: Standard queries with clear keywords

2. LLM Mode: AI-powered semantic understanding

   • Uses language model to interpret query intent
   • Considers skill descriptions and context
   • Requires LLM API access
   • Best for: Complex or ambiguous queries

3. Hybrid Mode: Combines keyword and LLM approaches

   • Falls back to LLM if keyword confidence is low
   • Balances speed and accuracy
   • Best for: Production systems with varied query types

3. Pipeline Orchestration

Execute multi-skill pipelines (e.g., preprocess → domains → de → genes → statistics).

4. Skill Discovery

List all available skills with status (ready/planned) and descriptions.

Routing Maps

Spatial Transcriptomics Keywords → Skills

Query Keywords	Skill	Description
spatial domain, tissue region, niche	spatial-domain-identification	Tissue region/niche identification
cell type annotation	spatial-cell-annotation	Cell type annotation
deconvolution, cell proportion	spatial-deconvolution	Cell type deconvolution
spatial statistics, moran, autocorrelation	spatial-statistics	Spatial statistics
spatially variable gene	spatial-svg-detection	Spatially variable genes
differential expression, marker genes	spatial-de	Differential expression
condition comparison, pseudobulk	spatial-condition-comparison	Condition comparison
ligand receptor, cell communication	spatial-cell-communication	Cell-cell communication
rna velocity	spatial-velocity	RNA velocity
trajectory, pseudotime	spatial-trajectory	Trajectory inference
pathway enrichment, gsea	spatial-enrichment	Pathway enrichment
cnv, copy number	spatial-cnv	Copy number variation
batch correction, integration	spatial-integration	Multi-sample integration
spatial registration, alignment	spatial-registration	Spatial registration
preprocess, qc, normalization	spatial-preprocessing	Data preprocessing

Single-Cell Omics Keywords → Skills

Query Keywords	Skill	Description
qc metrics, quality control	sc-qc	QC metrics calculation
filter cells, gene filtering	sc-filter	Cell/gene filtering
ambient rna, cellbender	sc-ambient-removal	Ambient RNA removal
single cell, scrna-seq	sc-preprocessing	scRNA-seq preprocessing
doublet detection, scrublet	sc-doublet-detection	Doublet detection
trajectory, pseudotime, paga, dpt	sc-pseudotime	Trajectory inference
rna velocity, scvelo	sc-velocity	RNA velocity analysis
cell type annotation, celltypist	sc-cell-annotation	Cell type annotation
integration, batch correction, harmony	sc-batch-integration	Batch correction
differential expression, pseudobulk	sc-de	Differential expression
marker genes, find markers	sc-markers	Marker gene detection
cluster enrichment, marker enrichment, single-cell gsea	sc-enrichment	Statistical enrichment on marker / DE rankings
pathway scoring, signature score	sc-pathway-scoring	Per-cell pathway / signature activity scoring
gene regulatory network, grn, pyscenic	sc-grn	Gene regulatory network

Genomics Keywords → Skills

Query Keywords	Skill	Description
variant calling, snp	genomics-variant-calling	Variant calling
structural variant, sv	genomics-sv-detection	Structural variant detection
vcf operations	genomics-vcf-operations	VCF manipulation
alignment, read alignment	genomics-alignment	Read alignment
variant annotation	genomics-variant-annotation	Variant annotation
assembly, genome assembly	genomics-assembly	Genome assembly
phasing, haplotype	genomics-phasing	Haplotype phasing
cnv calling	genomics-cnv-calling	CNV analysis
quality control, fastq	genomics-qc	Sequencing QC
epigenomics, chip-seq, atac-seq	genomics-epigenomics	Epigenomics analysis

Proteomics Keywords → Skills

Query Keywords	Skill	Description
mass spec qc, ms qc	proteomics-ms-qc	Mass spectrometry QC
peptide identification	proteomics-identification	Peptide identification
protein quantification	proteomics-quantification	Protein quantification
differential abundance	proteomics-de	Differential abundance
ptm, post-translational	proteomics-ptm	PTM analysis
pathway enrichment	proteomics-enrichment	Pathway enrichment
structural proteomics	proteomics-structural	Structural proteomics
data import	proteomics-data-import	Data import

Metabolomics Keywords → Skills

Query Keywords	Skill	Description
peak detection	metabolomics-peak-detection	Peak detection
xcms preprocessing	metabolomics-xcms-preprocessing	XCMS preprocessing
metabolite annotation	metabolomics-annotation	Metabolite annotation
normalization	metabolomics-normalization	Data normalization
differential metabolite	metabolomics-de	Differential analysis
pathway enrichment	metabolomics-pathway-enrichment	Pathway enrichment
statistical analysis	metabolomics-statistics	Statistical analysis
quantification	metabolomics-quantification	Feature quantification

CLI Usage

# Route a natural language query (default: keyword mode)
python skills/orchestrator/omics_orchestrator.py \
  --query "find spatially variable genes" --output output/

# Route by file type (auto-detect domain)
python skills/orchestrator/omics_orchestrator.py \
  --input data.h5ad --output output/

# Specify routing mode: keyword (default), llm, or hybrid
python skills/orchestrator/omics_orchestrator.py \
  --query "find spatially variable genes" --output output/ --routing-mode keyword

python skills/orchestrator/omics_orchestrator.py \
  --query "find spatially variable genes" --output output/ --routing-mode llm

python skills/orchestrator/omics_orchestrator.py \
  --query "find spatially variable genes" --output output/ --routing-mode hybrid

# Run demo to see routing across all domains
python skills/orchestrator/omics_orchestrator.py --demo --output output/

# Run demo with different routing modes
python skills/orchestrator/omics_orchestrator.py --demo --output output/ --routing-mode llm

# List all available skills
python omicsclaw.py list

# List skills in a specific domain
python omicsclaw.py list --domain spatial

Output Format

The orchestrator returns structured JSON with:

{
  "query": "find spatially variable genes",
  "detected_domain": "spatial",
  "routed_skill": "spatial-svg-detection",
  "confidence": 0.95,
  "execution_status": "success",
  "output_dir": "/path/to/output"
}

Examples

Example 1: Spatial query routing (keyword mode)

python skills/orchestrator/omics_orchestrator.py \
  --query "identify tissue regions in my spatial data" \
  --output output/domains/
# Routes to: spatial-domain-identification

Example 2: Single-cell query routing (keyword mode)

python skills/orchestrator/omics_orchestrator.py \
  --query "detect doublets in single cell data" \
  --output output/doublets/
# Routes to: sc-doublet-detection

Example 3: File-based routing

python skills/orchestrator/omics_orchestrator.py \
  --input data.vcf --output output/variants/
# Detects domain: genomics
# Routes to: genomics-vcf-operations

Example 4: LLM-powered routing

python skills/orchestrator/omics_orchestrator.py \
  --query "I want to understand which genes show spatial patterns" \
  --output output/svg/ --routing-mode llm
# Uses LLM to interpret intent
# Routes to: spatial-svg-detection

Example 5: Hybrid routing

python skills/orchestrator/omics_orchestrator.py \
  --query "analyze cell-cell interactions in my tissue" \
  --output output/comm/ --routing-mode hybrid
# Tries keyword first, falls back to LLM if needed
# Routes to: spatial-cell-communication

Example 6: Demo mode with different routing

# Keyword routing demo
python skills/orchestrator/omics_orchestrator.py --demo --output output/demo_keyword/

# LLM routing demo
python skills/orchestrator/omics_orchestrator.py --demo --output output/demo_llm/ --routing-mode llm

# Hybrid routing demo
python skills/orchestrator/omics_orchestrator.py --demo --output output/demo_hybrid/ --routing-mode hybrid

Integration with OmicsClaw

The orchestrator is the primary entry point for the OmicsClaw bot interfaces (Telegram, Feishu) and can be invoked via:

• CLI: python omicsclaw.py run orchestrator --query "..."
• Python API: from omicsclaw.routing.router import route_query_unified
• Bot: Natural language messages automatically routed through orchestrator

Notes

• Default domain fallback is spatial if detection fails
• Confidence scoring based on keyword match length and position
• Supports both exact skill names and natural language queries

• All 88 skills across 7 domains are accessible through this single interface