/competing-hypothesis-matrix
Tactic: Multi-hypothesis management — generate competing hypotheses,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill competing-hypothesis-matrix --agent claude-codeHow it fires
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- Slash command
/competing-hypothesis-matrix
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Tactic: Multi-hypothesis management — generate competing hypotheses,
SKILL.md
competing-hypothesis-matrix.SKILL.mdname: competing-hypothesis-matrix
description: 'Tactic: Multi-hypothesis management — generate competing hypotheses,
design discriminating predictions, build a structured comparison matrix'
version: 1.0.0
category: hypothesis-formation
type: tactic
campaign: hypothesis-formulation
sops:
- competing-hypothesis-generation
- discriminating-prediction-design
- hypothesis-comparison-matrix
dependencies:
sops:
- competing-hypothesis-generation
- discriminating-prediction-design
- hypothesis-comparison-matrix
Competing Hypothesis Matrix
Multi-hypothesis management — systematically generate alternative explanations competing with the primary hypothesis, design key predictions that can distinguish them, and build a structured comparison matrix to avoid confirmation bias.
Orchestration Intent
The most dangerous bias in scientific reasoning is holding only one hypothesis. This tactic forces CC to first systematically construct competing hypotheses before settling on the "best" hypothesis, then design predictions that can distinguish them.
The three steps cannot be reordered: first generate competing hypotheses (skipping not allowed), then design discriminating predictions (not allowed to only compare without testing), and finally build the comparison matrix (not allowed to only enumerate without quantifying). The final output is not "which hypothesis is correct" but "what experiment can distinguish them."
Available SOPs
| SOP | Responsibility | When to call | |-----|------|---------| | competing-hypothesis-generation | Based on the primary hypothesis, generate ≥3 alternative hypotheses competing with it (different mechanisms, same or similar phenomenon prediction range) | Required in all modes, execute first | | discriminating-prediction-design | Design discriminating predictions for each pair of competing hypotheses — find an observable result for which the two hypotheses predict differently | Required in all modes, after competing-hypothesis-generation | | hypothesis-comparison-matrix | Assemble all hypotheses and discriminating predictions into a structured comparison matrix, annotating each hypothesis's expected outcome for each prediction | Required in all modes, execute last |
Orchestration Pattern
**Simplified (S tier, 1 primary hypothesis)**
- Sequentially execute all 3 SOPs; generate ≥3 competing hypotheses; design ≥2 discriminating predictions; build comparison matrix
- Applicable: a single primary hypothesis requiring competitive-thinking scrutiny
**Standard (M tier, 2-3 primary hypotheses)**
- competing-hypothesis-generation generates competing hypotheses independently for each primary hypothesis; discriminating-prediction-design designs discriminating predictions across primary and competing hypotheses; hypothesis-comparison-matrix includes all hypotheses (primary + competing)
- Applicable: multiple candidate hypotheses already exist, requiring unified management and comparison
**Deep (L tier, complex hypothesis set)**
- All 3 SOPs execute; competing-hypothesis-generation additional requirement: at least 1 competing hypothesis comes from a completely different theoretical framework; discriminating-prediction-design additional requirement: each discriminating prediction annotates the required experiment scale and difficulty; hypothesis-comparison-matrix additional output: recommended experiment priority (most discriminating predictions ranked first)
- Applicable: a complex hypothesis space requiring direct input to experiment design
Minimum Yield
- ≥3 competing hypotheses (explaining the same phenomenon as the primary hypothesis but with different mechanisms)
- ≥2 discriminating predictions (each prediction produces different expected outcomes for at least 2 hypotheses)
- Structured comparison matrix: hypotheses × predictions, each cell annotating expected outcome direction (support/contradict/irrelevant)
Yield Report
Report to the calling strategy after execution:
- Number of competing hypotheses / number of discriminating predictions
- Most discriminating prediction (distinguishing the most hypothesis pairs at once)
- Hardest-to-distinguish hypothesis pair (predictions nearly identical, requiring extremely fine experiment design)
- Recommended hypothesis to test first (most easily falsified + most discriminating)
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | competing-hypothesis-generation | SOP: Generate mechanistically distinct competing hypotheses for the same phenomenon | | discriminating-prediction-design | SOP: design key predictions and observation plans that can distinguish competing hypotheses | | hypothesis-comparison-matrix | SOP: Build a multi-dimensional comparison matrix of competing hypotheses |
<!-- END available-tables (generated) -->
Read more
name: competing-hypothesis-matrix description: 'Tactic: Multi-hypothesis management — generate competing hypotheses, design discriminating predictions, build a structured comparison matrix' version: 1.0.0 category: hypothesis-formation type: tactic campaign: hypothesis-formulation sops: - competing-hypothesis-generation - discriminating-prediction-design - hypothesis-comparison-matrix dependencies: sops: - competing-hypothesis-generation - discriminating-prediction-design - hypothesis-comparison-matrix
Competing Hypothesis Matrix
Multi-hypothesis management — systematically generate alternative explanations competing with the primary hypothesis, design key predictions that can distinguish them, and build a structured comparison matrix to avoid confirmation bias.
Orchestration Intent
The most dangerous bias in scientific reasoning is holding only one hypothesis. This tactic forces CC to first systematically construct competing hypotheses before settling on the "best" hypothesis, then design predictions that can distinguish them.
The three steps cannot be reordered: first generate competing hypotheses (skipping not allowed), then design discriminating predictions (not allowed to only compare without testing), and finally build the comparison matrix (not allowed to only enumerate without quantifying). The final output is not "which hypothesis is correct" but "what experiment can distinguish them."
Available SOPs
| SOP | Responsibility | When to call | |-----|------|---------| | competing-hypothesis-generation | Based on the primary hypothesis, generate ≥3 alternative hypotheses competing with it (different mechanisms, same or similar phenomenon prediction range) | Required in all modes, execute first | | discriminating-prediction-design | Design discriminating predictions for each pair of competing hypotheses — find an observable result for which the two hypotheses predict differently | Required in all modes, after competing-hypothesis-generation | | hypothesis-comparison-matrix | Assemble all hypotheses and discriminating predictions into a structured comparison matrix, annotating each hypothesis's expected outcome for each prediction | Required in all modes, execute last |
Orchestration Pattern
**Simplified (S tier, 1 primary hypothesis)**
- Sequentially execute all 3 SOPs; generate ≥3 competing hypotheses; design ≥2 discriminating predictions; build comparison matrix
- Applicable: a single primary hypothesis requiring competitive-thinking scrutiny
**Standard (M tier, 2-3 primary hypotheses)**
- competing-hypothesis-generation generates competing hypotheses independently for each primary hypothesis; discriminating-prediction-design designs discriminating predictions across primary and competing hypotheses; hypothesis-comparison-matrix includes all hypotheses (primary + competing)
- Applicable: multiple candidate hypotheses already exist, requiring unified management and comparison
**Deep (L tier, complex hypothesis set)**
- All 3 SOPs execute; competing-hypothesis-generation additional requirement: at least 1 competing hypothesis comes from a completely different theoretical framework; discriminating-prediction-design additional requirement: each discriminating prediction annotates the required experiment scale and difficulty; hypothesis-comparison-matrix additional output: recommended experiment priority (most discriminating predictions ranked first)
- Applicable: a complex hypothesis space requiring direct input to experiment design
Minimum Yield
- ≥3 competing hypotheses (explaining the same phenomenon as the primary hypothesis but with different mechanisms)
- ≥2 discriminating predictions (each prediction produces different expected outcomes for at least 2 hypotheses)
- Structured comparison matrix: hypotheses × predictions, each cell annotating expected outcome direction (support/contradict/irrelevant)
Yield Report
Report to the calling strategy after execution:
- Number of competing hypotheses / number of discriminating predictions
- Most discriminating prediction (distinguishing the most hypothesis pairs at once)
- Hardest-to-distinguish hypothesis pair (predictions nearly identical, requiring extremely fine experiment design)
- Recommended hypothesis to test first (most easily falsified + most discriminating)
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | competing-hypothesis-generation | SOP: Generate mechanistically distinct competing hypotheses for the same phenomenon | | discriminating-prediction-design | SOP: design key predictions and observation plans that can distinguish competing hypotheses | | hypothesis-comparison-matrix | SOP: Build a multi-dimensional comparison matrix of competing hypotheses |
<!-- 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

