AI agent skill · by @yogsoth-ai
Assess Evidence Saturation
Estimate marginal information gain of new evidence batches relative to the current corpus and classify continuing/near-saturation/saturated.
Archived 2026-10-23 · Source updated 2026-09-29 · License: Apache-2.0. Check the original repository for the latest version.
SKILL.md instructions
# assess-evidence-saturation ## Purpose Compare the marginal information gain of a new evidence batch with the comparable prior batch and classify acquisition as continuing, near-saturation, or saturated. ## Input contract ```yaml required: [current_corpus, prior_comparable_batch, novelty_schema] optional: [topic_schema, quality_weights, stopping_policy] constraints: [batches must share a declared universe and comparable novelty calculation] ``` ## Procedure 1. Declare the eligible evidence universe, novelty dimensions, and current/prior batch boundaries. 2. Compute new topic, independent-source, condition, or mechanism coverage for each comparable batch. 3. Compare marginal information gain and record the evidence supporting the comparison. 4. Classify continuing, near-saturation, or saturated and state the stopping rationale. ## Output contract ```yaml produces: [declared_universe, batch_comparison, marginal_gain, saturation_state, stopping_rationale] delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions] ``` ## Quality gates - Numerator, denominator, batch increment, source references, direction, and rationale are reproducible. - The comparison uses the same novelty schema for both batches. - Saturation is not declared from corpus size alone. - Saturation describes acquisition only: it states that further retrieval adds little, never that the question the evidence addresses is answered. ## Failure and counterexamples Do not compare incomparable batches, count duplicate sources as new information, or treat a low-quality batch as evidence of saturation. ## Provenance map - `resolved: knowledge-acquisition-saturation-detection`