cross-disciplinary-bridge-finder
Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams. Identifies synergies between scientific disciplines, matches researchers with complementary expertise, and facilitates cross-domain collaborations. Supports interdisciplinary grant applications and innovative research team formation.
npx skills add majiayu000/claude-skill-registry --skill cross-disciplinary-bridge-finder --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cross-Disciplinary Research Collaboration Finder ## When to Use This Skill - identifying collaboration opportunities across fields - finding experts in complementary disciplines - translating methodologies between scientific domains - building interdisciplinary research teams - discovering funding for interdisciplinary projects - mapping knowledge transfer pathways ## Quick Start ```python from scripts.interdisciplinary import CollaborationFinder finder = CollaborationFinder() # Find collaborators in different field collaborators = finder.find_experts( my_expertise="machine_learning", target_field="immunology", collaboration_type="co_authorship", min_publications=10, h_index_threshold=15 ) if not collaborators: print("No collaborators found — try lowering min_publications or h_index_threshold.") else: # Validate quality before proceeding: only consider complementarity_score > 0.7 qualified = [e for e in collaborators if e.complementarity_score > 0.7] print(f"Found {len(collaborators)} candidates; {len(qualified)} meet quality threshold (score > 0.7):") for expert in qualified[:5]: print(f" - {expert.name} ({expert.institution})") print(f" Research: {expert.research_focus}") print
- When to Use This Skill
- Quick Start
- Command Line Usage
- Handling Poor Results
- References
python scripts/main.py --my-field machine_learning --target-field immunology --find-collaborators --output matches.json
What does the cross-disciplinary-bridge-finder skill do?
Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams. Identifies synergies between scientific disciplines, matches researchers with complementary expertise, and facilitates cross-domain collaborations. Supports interdisciplinary grant applications and innovative research team formation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cross-disciplinary-bridge-finder --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.