Module Curation

SkillDocs & knowledge

Curate, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Module Curation skill

What this skill tells your AI

The instructions your AI receives, as published by ai4curation/ai-gene-review in .claude/skills/module-curation/SKILL.md and read by ahel’s review.

Use this skill for modules/*.yaml work and for project pages that describe module/pathway curation. The goal is a reusable, defensible biological module, not a flat list of annotations or a species-specific note disguised as a module.

Workflow

  1. Read the local context before editing:
    • modules/README.md
    • the target modules/<module>.yaml
    • any adjacent <module>-deep-research-*.md
    • related projects/** batch page(s), if the task is pathway-batch work
    • relevant gene reviews and UniProt/GOA files for concrete members
  2. Decide the module boundary before filling YAML:
    • What biological process, complex, reaction chain, or reusable motif is the module?
    • Which genes/proteins are core members, and which are activation context, substrate supply, regulation, upstream/downstream biology, or separate modules?
    • Is this a concrete species/pathway instance or an abstract reusable motif?
    • Does the boundary have at least two substantive parts/roles/steps? If it collapses to one gene, one enzyme, or one reaction, do not create or retain a standalone ModuleReview; record it as pathway/gene curation and fold it into a broader module later.
  3. Model the structure:
    • Put module-level process/complex/context terms on module.concepts and module.context.
    • Put molecular functions on leaf annotons[].function, not on a biological-process module just because the member protein has that MF.
    • Use parts for required steps or roles, variant_sets for alternatives, and connections for ordered/causal links.
  4. Ground claims with checkable provenance:
    • Use exact UniProt, GO, Rhea, ChEBI, PANTHER PTHR, and PAINT PTN ids.
    • Never guess PTNs or identifiers. Resolve PTNs from local PANTHER/PAINT data or GOA WITH/FROM evidence.
    • Use representative members to orient family-level claims without turning a reusable module into a species-specific member list.
  5. Validate and render before committing:
    • uv run linkml-validate -s src/ai_gene_review/schema/gene_review.yaml -C ModuleReview modules/<module>.yaml
    • uv run python -m ai_gene_review.validation.module_validator modules/<module>.yaml
    • just render-module modules/<module>.yaml
    • render any touched project page with uv run ai-gene-review render-projects ...
    • run git diff --check

Reference Files

Load only the reference needed for the decision at hand:

  • references/modeling.md — module-vs-annoton placement, parts, variants, locations, exemplars, PTNs, and common anti-patterns.
  • references/research-and-grounding.md — module deep research, species/pathway satisfiability checks, gene review integration, and provenance rules.
  • references/validation-and-pr.md — validation, rendering, derived QC, cache noise, and PR checklist.

Curation Rules

  • Do not create or retain one-part modules. A standalone module needs at least two substantive parts/roles/steps at the modeled boundary; otherwise keep the work as pathway/gene curation or fold it into a broader multi-part module.
  • Avoid redundant parent/child context such as both cytoplasm and cytosol unless the distinction is intentional and explained.
  • Do not use broad parent processes as the module core when a specific process or reaction step is available. Keep broad terms as prose/context if needed.
  • Keep species-specific notes in evidence, notes, project pages, or concrete instantiations; reusable modules should stay species-neutral unless the scope is deliberately concrete.
  • Treat deep research as retrieval support, not authority. Every structural claim must still be grounded in identifiers, gene reviews, GOA/UniProt, GO-CAM, literature, or explicit curator judgment.

Signals

GitHub stars
24
Forks
4
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
module-curation
Source
github.com/ai4curation/ai-gene-review