/adaptive-pair-selection
Iteratively select maximally informative pairs, execute comparisons,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill adaptive-pair-selection --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/adaptive-pair-selection
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The summary Claude sees to decide when to auto-load this skill.
Iteratively select maximally informative pairs, execute comparisons,
SKILL.md
adaptive-pair-selection.SKILL.mdname: adaptive-pair-selection
description: Iteratively select maximally informative pairs, execute comparisons,
update ratings, and check convergence until ranking stabilizes.
execution: tactic
dependencies:
sops:
- comparison-executor
- convergence-check
- pair-selector
- rating-update
Adaptive Pair Selection
Select the next comparison pair by information gain, execute the comparison, update ratings, and check for convergence. Repeats until the ranking stabilizes or the comparison budget is exhausted.
Stages
1. **Select** — pair-selector identifies the pair whose comparison would most reduce uncertainty 2. **Compare** — comparison-executor produces a judgment with confidence and reasoning 3. **Update** — rating-update incorporates the new judgment into the rating model 4. **Check** — convergence-check determines if ranking has stabilized
Loop stages 1-4 until convergence or budget exhaustion.
Available SOPs
| Stage | SOP | Input | Output | |-------|-----|-------|--------| | Select | pair-selector | current_ratings, comparison_history | next_pairs[] | | Compare | comparison-executor | pair, context | judgment | | Update | rating-update | judgment, current_ratings, method | updated_ratings | | Check | convergence-check | rating_history | converged, stability_score |
Execution Guidance
- Start with high-uncertainty pairs (largest sigma or most uncertain boundary)
- For small N: may complete all pairs in first pass, then focus on inconsistencies
- For large N: prioritize pairs near rank boundaries (positions k and k+1)
- Track comparison count against budget; exit gracefully if budget hit
- Pass full rating_history to convergence-check (not just latest snapshot)
Minimum Yield
- Global ranking + confidence intervals + convergence curve
- Global ranking with confidence intervals for each position
- Convergence curve showing stability score over iterations
- Comparison log with all judgments made
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | comparison-executor | Execute a pairwise comparison between two candidates, producing a judgment with winner, confidence, and reasoning. | | convergence-check | Evaluate whether the ranking has stabilized by analyzing rating history and computing stability metrics. | | pair-selector | Select the next comparison pairs that maximize information gain given current ratings and comparison history. | | rating-update | Incorporate a new judgment into the rating model and return updated ratings for all candidates. |
<!-- END available-tables (generated) -->
Read more
name: adaptive-pair-selection description: Iteratively select maximally informative pairs, execute comparisons, update ratings, and check convergence until ranking stabilizes. execution: tactic dependencies: sops: - comparison-executor - convergence-check - pair-selector - rating-update
Adaptive Pair Selection
Select the next comparison pair by information gain, execute the comparison, update ratings, and check for convergence. Repeats until the ranking stabilizes or the comparison budget is exhausted.
Stages
1. **Select** — pair-selector identifies the pair whose comparison would most reduce uncertainty 2. **Compare** — comparison-executor produces a judgment with confidence and reasoning 3. **Update** — rating-update incorporates the new judgment into the rating model 4. **Check** — convergence-check determines if ranking has stabilized
Loop stages 1-4 until convergence or budget exhaustion.
Available SOPs
| Stage | SOP | Input | Output | |-------|-----|-------|--------| | Select | pair-selector | current_ratings, comparison_history | next_pairs[] | | Compare | comparison-executor | pair, context | judgment | | Update | rating-update | judgment, current_ratings, method | updated_ratings | | Check | convergence-check | rating_history | converged, stability_score |
Execution Guidance
- Start with high-uncertainty pairs (largest sigma or most uncertain boundary)
- For small N: may complete all pairs in first pass, then focus on inconsistencies
- For large N: prioritize pairs near rank boundaries (positions k and k+1)
- Track comparison count against budget; exit gracefully if budget hit
- Pass full rating_history to convergence-check (not just latest snapshot)
Minimum Yield
- Global ranking + confidence intervals + convergence curve
- Global ranking with confidence intervals for each position
- Convergence curve showing stability score over iterations
- Comparison log with all judgments made
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | comparison-executor | Execute a pairwise comparison between two candidates, producing a judgment with winner, confidence, and reasoning. | | convergence-check | Evaluate whether the ranking has stabilized by analyzing rating history and computing stability metrics. | | pair-selector | Select the next comparison pairs that maximize information gain given current ratings and comparison history. | | rating-update | Incorporate a new judgment into the rating model and return updated ratings for all candidates. |
<!-- END available-tables (generated) -->
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

