# Masst Repository Scale Search Workflow

> Use when you have a spectrum or feature of interest and want to know where else it occurs across all public metabolomics data — query preparation, repository-scale fastMASST search, specialized microbe/plant/food MASST for ecological context, and co-occurrence analysis.

- Skill: `holobiomicslab/masst-repository-scale-search-workflow` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add holobiomicslab/masst-repository-scale-search-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/masst-repository-scale-search-workflow/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/masst-repository-scale-search-workflow

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# MASST Repository-Scale Spectral Search (Reverse Metabolomics)

## Summary

End-to-end reverse metabolomics: take one molecule and find its public-dataset footprint with MASST, then add ecological context and co-occurrence interpretation.


## When to use

Use when you have a spectrum or feature of interest and want to know where else it occurs across all public metabolomics data — query preparation, repository-scale fastMASST search, specialized microbe/plant/food MASST for ecological context, and co-occurrence analysis.


## When NOT to use

- The data is not LC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).

## Stages

### Stage 1 — spectrum_prep

**Goal:** prepare a query MS/MS spectrum / USI for search

**EDAM operation:** operation_3215

**Inputs:** mgf · **Outputs:** tsv

**Candidate leaf skills:** `usi-spectrum-retrieval-and-loading` (primary), `spectral-data-loading-from-repository`, `usi-namespace-parsing`, `usi-string-parsing-and-resolution`, `usi-spectrum-identifier-encoding`

**Tools (primary):** spectrum_utils, Python, matplotlib, GNPS public library, ProteomeXchange (PXD datasets)

**Other candidate tools:** NumPy, GNPS Molecular Networking, MassBank, MetaboLights, Metabolomics Workbench, MS2LDA, GNPS Spectral Libraries, ProteoXchange Repository, QR Code Generation Library, USI Resolver and Displayer

**Grounding:** 2 KB(s); DOIs: 10.1021/acs.analchem.9b04884, 10.1101/2020.05.09.086066

### Stage 2 — masst_search

**Goal:** repository-scale spectral search (fastMASST)

**EDAM operation:** operation_3631

**Inputs:** tsv · **Outputs:** tsv

**Candidate leaf skills:** `spectral-database-query-execution` (primary), `spectral-match-result-consolidation`, `mass-spectrometry-database-search`, `mass-spectrometry-reference-database-integration`, `spectral-match-interpretation`

**Tools (primary):** MASST, GNPS, MASST+, CLUSTERING+, PAIRING+

**Other candidate tools:** metadataMASST, microbeMASST, plantMASST, tissueMASST, microbiomeMASST, foodMASST, Fast Search API, GNPS_MASST, GNPS Molecular Networking, MZmine

**Grounding:** 3 KB(s); DOIs: 10.1038/s41538-022-00137-3, 10.1038/s41564-023-01575-9, 10.1038/s41587-023-01985-4

### Stage 3 — specialized_masst  [OPTIONAL]

**Goal:** (optional) ecological context via microbe/plant/food MASST

**EDAM operation:** operation_3631

**Inputs:** tsv · **Outputs:** tsv

**Candidate leaf skills:** `domain-specific-spectrum-search-implementation` (primary), `masst-output-visualization`, `multi-domain-search-result-aggregation`, `metadata-harmonization-across-sources`

**Tools (primary):** microbeMASST, metadataMASST, GNPS_MASST, plantMASST, tissueMASST, microbiomeMASST, foodMASST, Fast Search API, MZmine


**Grounding:** 2 KB(s); DOIs: 10.1038/s41538-022-00137-3, 10.1038/s41564-023-01575-9

### Stage 4 — cooccurrence

**Goal:** co-occurrence / reverse-metabolomics interpretation

**EDAM operation:** operation_3659

**Inputs:** tsv · **Outputs:** tsv

**Candidate leaf skills:** `metabolite-metadata-integration` (primary), `sample-centric-metabolite-annotation`, `tandem-mass-spectrometry-metadata-standardization`, `ms-ms-spectral-library-matching`, `compound-database-matching`

**Tools (primary):** msFeaST, pandas, jupyter-notebook

**Other candidate tools:** ENPKG, MZmine, enpkg_mn_isdb_taxo, enpkg_sirius_canopus, enpkg_meta_analysis, SIRIUS, Open Tree of Life, Wikidata, NPClassifier, ChEMBL, matchms, pubchempy, RDKit, Python, masscube, TandemMatch, Mirador, PeakQC, Spectra, MetFrag, R, PubChem, COCONUT

**Grounding:** 6 KB(s); DOIs: 10.1021/acscentsci.3c00800, 10.1021/jasms.4c00146, 10.1038/s41467-025-60640-5, 10.1093/bioinformatics/btae584 …

## Grounding

Each stage carries the `kb_slugs`/`dois` of the leaves it draws on. Ground any stage against its source paper with the collection's `/ground` command or `bin/perspicacite_kb_bind.py` (Perspicacité KB; serverless local-clone fallback).

## Verification contract

`workflow.yaml` is gradable by `asb solve-workflow` (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.

## Provenance

Generated by `compose_workflows.py` (semantic binding + EDAM-aware primary selection). `derived_from_workflows` lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via `release_gate.py`.