/coherence-diagnosis
Strategy for auditing preference consistency using Consistency Ratio,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill coherence-diagnosis --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 →
- You can call itInvoke it directly when you want it.
- Slash command
/coherence-diagnosis
Context preview
The summary Claude sees to decide when to auto-load this skill.
Strategy for auditing preference consistency using Consistency Ratio,
SKILL.md
coherence-diagnosis.SKILL.mdname: coherence-diagnosis
description: Strategy for auditing preference consistency using Consistency Ratio,
cycle enumeration, and mElo to detect and resolve intransitivities.
dependencies:
tactics:
- consistency-audit-loop
sops:
- convergence-check
- ranking-synthesis
- rating-update
Coherence Diagnosis
Purpose
Audit an existing comparison matrix for logical consistency. Detect cycles, quantify transitivity violations, localize problematic judgments, and recommend corrections. Essential as a quality gate before finalizing any ranking.
When to use
- Existing comparison data needs validation
- Suspicion of inconsistent judgments
- Pre-finalization quality gate
- AHP consistency ratio check required
- Debugging unexpected ranking results
Budget
| Resource | Allocation | |----------|-----------| | Cycle detection | Full matrix scan | | Consistency metric | CR < 0.1 (AHP) or transitivity > 90% | | Repair comparisons | ≤ 20% of original comparisons | | Iterations | 1-3 audit-repair cycles |
State Ledger
comparison_matrix: {} # pair → winner
candidates: []
consistency_metrics: {cr: null, transitivity: null, cycles_count: 0}
cycles: [] # [[a>b, b>c, c>a], ...]
problematic_pairs: [] # pairs involved in most cycles
repair_log: [] # [{pair, old_judgment, new_judgment, reason}]
iteration: 0Available Tactics
- **consistency-audit-loop** — detect, localize, repair, recompute
Available SOPs
- cycle-detection
- inconsistency-localization
- comparison-executor (for re-comparison)
- rating-update (for recomputation)
- convergence-check
- ranking-synthesis
Execution Guidance
1. Run cycle-detection on full comparison matrix 2. Compute consistency metrics (CR, transitivity score) 3. If consistent (CR < 0.1): proceed to ranking-synthesis 4. If inconsistent: run inconsistency-localization 5. Re-compare problematic pairs via comparison-executor 6. Recompute ratings and re-check consistency 7. Repeat until consistent or repair budget exhausted 8. Document all repairs in audit trail
Output Format
diagnosis:
consistency_ratio: 0.04
transitivity_score: 0.97
cycles_found: 1
cycles_resolved: 1
repairs_made: 2
audit_trail:
- {pair: ["b", "d"], original: "b", revised: "d", reason: "..."}
final_ranking_valid: true
method: consistency-ratio<!-- BEGIN available-tables (generated) -->
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | consistency-audit-loop | Detect preference cycles, localize inconsistent judgments, request corrections, and recompute ratings until consistency threshold is met. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | convergence-check | Evaluate whether the ranking has stabilized by analyzing rating history and computing stability metrics. | | ranking-synthesis | Produce the final ranking artifact from converged ratings and consistency report. | | rating-update | Incorporate a new judgment into the rating model and return updated ratings for all candidates. |
<!-- END available-tables (generated) -->
Read more
name: coherence-diagnosis description: Strategy for auditing preference consistency using Consistency Ratio, cycle enumeration, and mElo to detect and resolve intransitivities. dependencies: tactics: - consistency-audit-loop sops: - convergence-check - ranking-synthesis - rating-update
Coherence Diagnosis
Purpose
Audit an existing comparison matrix for logical consistency. Detect cycles, quantify transitivity violations, localize problematic judgments, and recommend corrections. Essential as a quality gate before finalizing any ranking.
When to use
- Existing comparison data needs validation
- Suspicion of inconsistent judgments
- Pre-finalization quality gate
- AHP consistency ratio check required
- Debugging unexpected ranking results
Budget
| Resource | Allocation | |----------|-----------| | Cycle detection | Full matrix scan | | Consistency metric | CR < 0.1 (AHP) or transitivity > 90% | | Repair comparisons | ≤ 20% of original comparisons | | Iterations | 1-3 audit-repair cycles |
State Ledger
comparison_matrix: {} # pair → winner
candidates: []
consistency_metrics: {cr: null, transitivity: null, cycles_count: 0}
cycles: [] # [[a>b, b>c, c>a], ...]
problematic_pairs: [] # pairs involved in most cycles
repair_log: [] # [{pair, old_judgment, new_judgment, reason}]
iteration: 0Available Tactics
- **consistency-audit-loop** — detect, localize, repair, recompute
Available SOPs
- cycle-detection
- inconsistency-localization
- comparison-executor (for re-comparison)
- rating-update (for recomputation)
- convergence-check
- ranking-synthesis
Execution Guidance
1. Run cycle-detection on full comparison matrix 2. Compute consistency metrics (CR, transitivity score) 3. If consistent (CR < 0.1): proceed to ranking-synthesis 4. If inconsistent: run inconsistency-localization 5. Re-compare problematic pairs via comparison-executor 6. Recompute ratings and re-check consistency 7. Repeat until consistent or repair budget exhausted 8. Document all repairs in audit trail
Output Format
diagnosis:
consistency_ratio: 0.04
transitivity_score: 0.97
cycles_found: 1
cycles_resolved: 1
repairs_made: 2
audit_trail:
- {pair: ["b", "d"], original: "b", revised: "d", reason: "..."}
final_ranking_valid: true
method: consistency-ratio<!-- BEGIN available-tables (generated) -->
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | consistency-audit-loop | Detect preference cycles, localize inconsistent judgments, request corrections, and recompute ratings until consistency threshold is met. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | convergence-check | Evaluate whether the ranking has stabilized by analyzing rating history and computing stability metrics. | | ranking-synthesis | Produce the final ranking artifact from converged ratings and consistency report. | | 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

