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…
Tactic: Inductive/abductive path — describe anomalous phenomena, generate
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill anomaly-driven-abduction --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/anomaly-driven-abductionContext preview
The summary Claude sees to decide when to auto-load this skill.
Tactic: Inductive/abductive path — describe anomalous phenomena, generate
name: anomaly-driven-abduction description: 'Tactic: Inductive/abductive path — describe anomalous phenomena, generate candidate explanations, rank by plausibility' version: 1.0.0 category: hypothesis-formation type: tactic campaign: hypothesis-formulation sops: - anomaly-characterization - explanation-generation - plausibility-ranking dependencies: sops: - anomaly-characterization - explanation-generation - plausibility-ranking
Inductive/abductive path — precisely describe anomalous phenomena that existing theory cannot explain, generate multiple candidate explanations, rank by plausibility, and provide a structured basis for abductive hypotheses.
The starting point of abduction is "surprise" — an observed phenomenon inconsistent with existing theoretical predictions. This tactic forces CC to first precisely describe the anomaly (no vagueness allowed), then systematically generate explanations (not allowed to think of only one), and finally rank by plausibility (no subjective preference allowed).
None of the three steps can be omitted: imprecise description means explanations cannot be focused; insufficient explanations make ranking meaningless; ranking without basis turns hypothesis selection into guesswork.
| SOP | Responsibility | When to call | |-----|------|---------| | anomaly-characterization | Precisely describe the anomalous phenomenon: what was observed, deviation from expectation, conditions of occurrence, excluded trivial explanations | Required in all modes, execute first | | explanation-generation | Generate multiple candidate explanations (abductive hypotheses); each explanation must fully account for the anomaly | Required in all modes, after anomaly-characterization | | plausibility-ranking | Rank candidate explanations by plausibility criteria (prior probability, explanatory power, parsimony, testability) | Required in all modes, execute last |
**Simplified (S tier, single anomaly)**
**Standard (M tier, 1-3 related anomalies)**
**Deep (L tier, complex anomaly cluster)**
Report to the calling strategy after execution:
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | anomaly-characterization | SOP: Describe and classify anomalous phenomena that existing theory cannot explain | | explanation-generation | SOP: generate a list of candidate explanations for an anomalous phenomenon | | plausibility-ranking | SOP: rank candidate explanations by plausibility using multi-dimensional weighted scoring |
<!-- 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
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…
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.…
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…
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…
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…
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…