estimate-effective-evidence-count SKILL.md skillA
estimate-effective-evidence-count from yogsoth-ai/de-anthropocentric-research-engine is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add yogsoth-ai/de-anthropocentric-research-engine/estimate-effective-evidence-count@git:20260923.cbc38aaPins git:20260923.cbc38aa in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- yogsoth-ai/de-anthropocentric-research-engine
- Path
skills/estimate-effective-evidence-count/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260923.cbc38aa, committed Sep 23, 2026
- Size
- 1,176 bytes
- Repository stars
- 508
- License
- Apache-2.0
Estimate an effective number of independent evidence/reasoning paths after discounting dependence/correlation; report nominal N, dependence structure, and N_eff.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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 1. Count nominal paths and trace shared dependencies. 2. Assign dependence classes or correlations. 3. 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
Read the whole file at its exact version.
How to install
mdr add yogsoth-ai/de-anthropocentric-research-engine/estimate-effective-evidence-count@sha256:d3214fce95ac7c33The line at the top of this page pins the label git:20260923.cbc38aa, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_w67zl6gottsyqfrp)
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Versions
The registry has recorded 1 version of this file, committed on Sep 23, 2026.
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Source
yogsoth-ai/de-anthropocentric-research-engine · 508 stars · license Apache-2.0 · pushed 2026-10-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_w67zl6gottsyqfrp GET https://markdownregistry.com/api/v1/resolve?ref=yogsoth-ai/de-anthropocentric-research-engine/estimate-effective-evidence-count GET https://markdownregistry.com/api/v1/blob/d3214fce95ac7c33007bb4183cbd6c873a56b01bb570dc66ca0280fce95788cc
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