nahisaho Metabolomics
Metabolomics skill. LC-MS/GC-MS data processing, metabolite identification, pathway mapping, biomarker discovery, and metabolomics statistical analysis pipelines.
Use This Skill When
- LC-MS/GC-MS data processing.
- Metabolite identification.
- Pathway mapping.
- Biomarker discovery.
- Metabolomics statistical analysis pipelines.
Required Inputs
- Research objective, decision target, or hypothesis.
- Available data, source constraints, and domain assumptions.
- Required outputs, success metrics, and deadline or reproducibility constraints.
Workflow
- Confirm scope, assumptions, and the exact artifact set to save.
- Apply the narrowest domain method that answers the request with defensible evidence.
- Save code, tables, figures, and intermediate outputs to files instead of chat-only output.
- State limitations, uncertainty, and any validation or sensitivity checks performed.
- Append skill selection, handoff I/O, and file writes to
logs/process-log.jsonl.
Deliverables
report.md: concise method, results, interpretation, and file inventory in the user's language.results/: structured outputs, metrics, model artifacts, or extracted findings.figures/: English-only charts, diagrams, or panels when visual output is needed.data/: processed or derived datasets when transformation occurs.
Quality Gates
- The selected method matches the scientific question and stated assumptions.
- Outputs are reproducible, saved to files, and traceable from inputs to conclusions.
- Missing data, uncertainty, bias, and hard limits are made explicit.
report.mdandlogs/process-log.jsonlreference the generated artifacts.