HolobiomicsLab 
ASB Metabolomics — composite workflow router
This is the goal-level entry point for the ASB Metabolomics collection. Where the
leaf router (metabolomics-collection-router) finds ONE atomic skill, this router selects
an end-to-end composite workflow super-skill — an ordered pipeline of stages, each
delegating to vetted leaf skills, with grounding and a gradable workflow.yaml.
Use it in three steps: select → run → ground.
1. Select — match the goal to a workflow
Prefer semantic retrieval (P2): run
python bin/semantic_search.py --query "<the user's goal>" \
--collection . --target workflows [--technique LC-MS] --k 3
It uses meaning-based ranking (text-embedding-3-large — the same model Perspicacité uses)
when an embedding backend is available, and falls back to a keyword index search
automatically when it is not, so it always works offline. The result's mode field tells
you which ran. The same tool works at the leaf level with --target skills (Step 2).
If you prefer manual lookup, load workflows_index.json (one row per composite workflow:
slug, name, description, techniques, stages, member_tools) and match the goal,
in order of precision:
- Technique — what platform is the data?
LC-MS,GC-MS,MS-imaging,ion-mobility-MS,NMR. Filtertechniquesfirst. - Goal phrasing — match the user's intent against each row's
description.
Available workflows (this staged set):
| workflow | technique | what it does |
|---|---|---|
untargeted-lcmsms-annotation |
LC-MS | raw mzML → preprocess → network → library-match → SIRIUS → fuse → master table |
lipidomics-lcms-annotation |
LC-MS | class/species-level lipid annotation + stats |
gcms-deconvolution-and-identification |
GC-MS | EI deconvolution → library match → retention index → stats |
ms-imaging-spatial-metabolomics |
MS-imaging | imzML → spatial annotation (FDR) → segmentation → region stats |
statistics-and-biomarker-discovery |
LC-MS | normalize → multivariate → differential → pathway → biomarkers |
sirius-denovo-structure-elucidation |
LC-MS | formula → structure → class → confidence filter (no library needed) |
masst-repository-scale-search |
LC-MS | reverse metabolomics: where else does this molecule occur in public data |
ion-mobility-4d-annotation |
ion-mobility-MS | 4D feature extraction → CCS calibration → CCS-aware annotation |
nmr-metabolomics-profiling |
NMR | spectra → preprocess → identify (chemical shift) → quantify → stats |
pathway-functional-analysis |
LC-MS | m/z feature list → mummichog → pathway/enrichment → interpretation |
If no workflow fits the goal, fall back to the leaf router
(metabolomics-collection-router) and assemble steps from atomic skills.
2. Run — execute the workflow's stages
Read the chosen workflows/<slug>/SKILL.md and follow its Stages in order. Each stage
carries: a goal, candidate leaf skills (primary first), the tools to install/invoke, and
its typed inputs/outputs. The machine-readable workflows/<slug>/workflow.yaml is the DAG
(after, inputs_from) and is gradable by asb solve-workflow. Honor the I/O contract:
each stage consumes the prior stage's declared outputs. Optional stages are marked.
For a stage's leaf skills, read each skills/<leaf-slug>/SKILL.md for the procedure, or
use the leaf router to pick among the candidates for your exact data.
3. Ground — verify each stage against its source papers
Before trusting a parameter or default, ground the stage's leaves against the papers they
were distilled from. Each stage's grounding.kb_slugs/dois (in workflow.yaml) point at
the asb-paper-<doi> KBs. Use the collection's /ground command or
bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
These workflows are staged (not yet released). Bindings were chosen by semantic retrieval (
text-embedding-3-large) + deterministic selection.derived_from_workflowsin each frontmatter is the eval-ablation set.