assess-evidence-saturation
SkillDev toolsLets your agent judge whether new research evidence is still adding new information or whether the topic is fully covered.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the assess-evidence-saturation skill
About this skill
Estimate marginal information gain of new evidence batches relative to the current corpus and classify continuing/near-saturation/saturated.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/assess-evidence-saturation/SKILL.md and read by ahel’s review.
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
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
- Declare the eligible evidence universe, novelty dimensions, and current/prior batch boundaries.
- Compute new topic, independent-source, condition, or mechanism coverage for each comparable batch.
- Compare marginal information gain and record the evidence supporting the comparison.
- Classify continuing, near-saturation, or saturated and state the stopping rationale.
Output contract
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.
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
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
assess-evidence-saturation- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine