Structured methods for finding connections across disciplines. Use when exploring how concepts from one field illuminate another, seeking novel applications, or analyzing structural similarities between domains.
/plugin marketplace add eternnoir/claude-tool/plugin install akashicrecords@claude-toolsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
A methodological toolkit for discovering and articulating connections across disciplines.
Identify structural similarities that transcend domain boundaries.
Process:
Examples:
Output format:
"The structure here is [abstract pattern]. This same structure appears in [Domain B] as [concrete manifestation]. What this reveals: [insight about the deeper principle]."
Use a principle from one field to illuminate another.
Process:
Examples:
Output format:
"In [Domain A], [concept] works by [mechanism]. Applying this lens to [Domain B]: [new interpretation]. This suggests [actionable insight or prediction]."
Transfer solutions or techniques across contexts.
Process:
Caution flags:
Output format:
"[Domain A] solved [problem] using [approach]. [Domain B] faces analogous challenge: [description]. Potential transfer: [adapted solution]. Transfer risk: [what might not hold]."
Find where different frameworks conflict instructively.
Process:
Examples:
Output format:
"[Framework A] says [X]. [Framework B] says [Y]. The tension: [specific conflict]. What A captures that B misses: [insight]. What B captures that A misses: [insight]. Resolution path: [synthesis or scope conditions]."
Analyze user request:
Select mode:
"How does X relate to Y?" -> Isomorphic Patterns or Conceptual Bridges
"Can we apply X to solve Y?" -> Novel Applications
"X says one thing, Y says another" -> Productive Tensions
"Find connections to X" -> Start with Isomorphic Patterns
For Isomorphic Patterns:
For Conceptual Bridges:
For Novel Applications:
For Productive Tensions:
Abstraction Ladder:
Epistemic Marking:
After generating cross-domain insights, consider:
Example integration:
User: "How does natural selection relate to algorithm design?"
Skill workflow:
1. Identifies Conceptual Bridge mode
2. Analyzes natural selection mechanisms
3. Maps to evolutionary algorithms
4. Presents insights with epistemic marking
Optional follow-up:
"Would you like me to save these insights to your knowledge base?"
-> Invokes add-content to capture for future reference
User: "I'm noticing that both social media viral content and disease outbreaks seem to spread similarly. What's the connection?"
Skill response:
The structure here is network propagation with threshold dynamics. In both cases:
- Transmission occurs through connected nodes (people)
- Each transmission has a probability of success
- Critical mass triggers exponential growth
- Network topology determines spread patterns
This same structure appears in:
- Neural activation cascades in the brain
- Bank run panics in financial systems
- Idea adoption in scientific communities
What this reveals: The mathematics of contagion is substrate-independent. Understanding one gives you tools for all. The key variables are: connection density, transmission probability, and recovery/immunity rate.
User: "Engineering uses 'margin of safety' - could that concept help with personal finance decisions?"
Skill response:
Engineering solved the problem of uncertainty in material properties using margin of safety: design for 2-3x expected load to handle unknowns.
Personal finance faces an analogous challenge: income volatility, unexpected expenses, lifestyle inflation.
Potential transfer:
- Instead of spending up to your income limit, maintain a 2x margin
- Emergency fund = "factor of safety" for income disruption
- Budget for 50% of actual income = structural margin for unknowns
Transfer risk: Unlike engineering where failure modes are understood, life has "black swan" events. Engineering margins are calculated; financial margins are estimated. The principle transfers, but calibration requires different methods.
User: "Economists say people are rational actors, but psychologists show we're full of biases. Who's right?"
Skill response:
Rational actor model says: People optimize utility given constraints and information.
Behavioral psychology says: People use heuristics that systematically deviate from optimization.
The tension: Both can't be fully true. If we're rational optimizers, biases shouldn't persist. If we're bias-driven, markets shouldn't work as well as they do.
What rational models capture that behavioral misses: Aggregate market behavior often looks rational even when individuals aren't. Markets select for rational-ish outcomes.
What behavioral captures that rational misses: Individual decision-making, especially under uncertainty or emotional load. Where arbitrage is difficult.
Resolution path: Bounded rationality - people are rational within cognitive constraints. Rational at the margin, biased at the baseline. Different scopes of application, not contradiction.