How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill estimate-effective-evidence-countBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
estimate-effective-evidence-count
Purpose
Estimate independent evidence count after discounting dependence and correlation.
Input contract
yaml
required: [evidence_paths, dependence_structure]
optional: [correlation_estimates, nominal_count]
constraints: [dependence assumptions and uncertainty must be explicit]Procedure
- Count nominal paths and trace shared dependencies.
- Assign dependence classes or correlations.
- Estimate N_eff and sensitivity to dependence assumptions.
Output contract
yaml
produces: [nominal_count, dependence_map, effective_count, sensitivity_range]
delta_fields: [findings, evidence_updates, uncertainties, decisions]Quality gates
- Report N and N_eff separately; include a range when dependence is uncertain.
Failure and counterexamples
Do not inflate N_eff by counting repeated sources or shared models as independent.
Provenance map
- resolved: independent-convergence-audit
