Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/data-wise/claude-plugins/cross-disciplinary-ideationnpx skills add Data-Wise/claude-plugins --skill cross-disciplinary-ideationgit clone --depth 1 https://github.com/Data-Wise/claude-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/data-wise/claude-plugins/cross-disciplinary-ideation)<a href="https://agentmods.dev/skills/data-wise/claude-plugins/cross-disciplinary-ideation"><img src="https://agentmods.dev/badge/skills/data-wise/claude-plugins/cross-disciplinary-ideation.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00016 | $0.04003 |
| Opus 5 | $0.00008 | $0.02001 |
| Sonnet 5 | $0.00003 | $0.00801 |
| Haiku 4.5 | $0.00002 | $0.00400 |
Grade A, and why
cross-disciplinary-ideation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 567 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Disciplinary Ideation
Systematic framework for discovering statistical innovations through cross-field connections
Use this skill when: brainstorming new methods, seeking novel approaches to statistical problems, looking for inspiration from other fields (physics, CS, biology, economics), or wanting to apply techniques from one domain to another.
The Cross-Disciplinary Innovation Framework
Why Cross-Disciplinary?
Many statistical breakthroughs originated elsewhere:
| Statistical Method | Origin Field | Transfer |
|---|---|---|
| MCMC | Physics (Metropolis) | Statistical computation |
| Boosting | Machine learning | Ensemble methods |
| Lasso | Signal processing | Sparse regression |
| Optimal transport | Mathematics | Distribution comparison |
| Neural networks | Neuroscience/CS | Flexible function estimation |
| Causal graphs | Philosophy/AI | Causal inference |
The Innovation Cycle
Problem in Statistics → Abstract Structure → Search Other Fields
↑ ↓
Validate/Adapt ←── Identify Analogues ←── Find Connections
Machine Learning Connections
Deep Learning for Causal Mediation
| ML Method | Statistical Application | Transfer Opportunity |
|---|---|---|
| Double ML | Debiased mediation effects | Nuisance parameter estimation |
| Causal Forests | Heterogeneous mediation | Effect modification detection |
| Neural Networks | Flexible g-computation | Nonparametric mediation |
| VAEs | Latent mediator modeling | Measurement error correction |
| Transformers | Sequential mediation | Temporal pattern learning |
| GNNs | Network mediation | Spillover effect estimation |
# Double ML for mediation effect estimation
library(DoubleML)
# Estimate nuisance parameters with ML
estimate_dml_mediation <- function(Y, A, M, X) {
# First stage: E[M|A,X]
mediator_model <- cv.glmnet(cbind(A, X), M)
M_hat <- predict(mediator_model, cbind(A, X))
# Second stage: E[Y|A,M,X]
outcome_model <- cv.glmnet(cbind(A, M, X), Y)
# Debiased estimation
residuals_M <- M - M_hat
list(
direct = coef(outcome_model)["A"],
indirect_component = residuals_M
)
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 567 lines · 16 tokens per session scan A 510b431731e1
cross-disciplinary-ideation is a skill published in the GitHub repository Data-Wise/claude-plugins (7 stars, last pushed 8d ago), licensed MIT. It adds 16 tokens to every session and 4,003 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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