// This skill should be used when creating, editing, or validating GO-CAM (Gene Ontology Causal Activity Model) models. It provides comprehensive annotation guidelines for molecular functions, biological processes, cellular components, and causal relationships following GO Consortium standards.
Search GO bioentities (genes, gene products, complexes) and their GO annotations directly from the GO database. Use to resolve MGI/MOD/ComplexPortal IDs, cross-check GO annotations independently of gocam search, or find MOD-specific gene product IDs needed in Noctua models.
Independent blind evaluation of GO-CAM claims documents using Claude Explore subagents to read the PDFs. Reads expert_validation_claims.md and verifies every claim against primary PDFs from scratch, producing a structured evaluation report. Use after gocam-claims-pipeline has generated its output. Always run in a fresh session — never in the same session as the pipeline. For a Gemini-powered alternative, see /blind-review-gemini.
Independent blind evaluation of GO-CAM claims documents using Gemini. One Gemini instance per primary PDF reads the paper directly and returns PASS/FLAG/FAIL verdicts on every claim citing it. Use after gocam-claims-pipeline has generated its output. Always run in a fresh session — never in the same session as the pipeline. For a Claude-engine alternative, see /blind-review-claude.
Generate a .drawio pathway diagram from a GO-CAM claims document. Use after completing the annotation pipeline (Phase 7) to produce agent-output/pathway_sketch.drawio. Reads shape styles and the Shape Palette page from workflow/network_template_draw.drawio.
Launches two parallel Gemini instances per paper to review GO-CAM claims from two complementary perspectives — a peer reviewer (scientific accuracy) and a GO-CAM curator (annotation correctness). Each receives the full claim in claims_template format and responds as an expert with OK/WRONG/UNCERTAIN plus a corrected claim where needed. Collects both outputs into peer_review_claims.md alongside the original claim.
Run the full GO-CAM literature-backed annotation pipeline. Reads GO-CAM models (OWL/TTL), verifies all claims against primary research PDFs, and produces expert validation documents. Use when starting or continuing a GO-CAM curation project folder.
| name | gocam-best-practice |
| description | This skill should be used when creating, editing, or validating GO-CAM (Gene Ontology Causal Activity Model) models. It provides comprehensive annotation guidelines for molecular functions, biological processes, cellular components, and causal relationships following GO Consortium standards. |
This skill provides expert guidance for creating and editing GO-CAM (Gene Ontology Causal Activity Model) models using the barista command-line tool. GO-CAM models represent biological knowledge as networks of causal relationships between molecular activities.
Use this skill when:
Every GO-CAM model consists of:
An activity unit is the fundamental building block of GO-CAM models:
The .claude/skills/gocam-best-practice/references/ directory contains detailed guidelines for specific annotation scenarios. Load these files when working with the corresponding activity types.
A comprehensive overview of all GO-CAM guidelines is in .claude/skills/gocam-best-practice/SP-GOCAM-guidelines-2025-02-06.md — read this first for unfamiliar annotation scenarios.
Choose the appropriate causal relation:
Use 'has input' to specify:
Important: For receptors, 'has input' specifies the downstream effector, NOT the ligand.
Three approaches based on knowledge:
Always include:
barista add-individual to create activity nodesbarista add-fact to create causal relationships.claude/skills/gocam-best-practice/references/When annotating, identify the molecular function type involved and load the corresponding reference file from the list above.
Before finalizing a GO-CAM model, verify:
# Create model
barista create-model --title "MAPK signaling example"
# Add receptor activity
barista add-individual --model $MODEL_ID --class GO:0004888 --assign receptor
# Add kinase activity
barista add-individual --model $MODEL_ID --class GO:0004674 --assign kinase
# Connect with causal relationship
barista add-fact --model $MODEL_ID \
--subject receptor --object kinase \
--predicate RO:0002413 # directly positively regulates
# Add transcription activator activity
barista add-individual --model $MODEL_ID \
--class GO:0001228 --assign tf_activity # DNA-binding transcription activator
# Specify the target gene as input
barista add-fact --model $MODEL_ID \
--subject tf_activity \
--object <gene-id> \
--predicate RO:0002233 # has input
# Add the target gene's molecular function
barista add-individual --model $MODEL_ID \
--class <target-mf> --assign target_activity
# Connect TF to target activity (indirect regulation)
barista add-fact --model $MODEL_ID \
--subject tf_activity \
--object target_activity \
--predicate RO:0002407 # indirectly positively regulates
.claude/skills/gocam-best-practice/references/barista list-modelsLoad these files from .claude/skills/gocam-best-practice/references/ as needed: