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Skill

/generate-candidate-directions

Generate diverse candidate research fields/subfields consistent with current context while permitting deliberate boundary-crossing.

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de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions --agent claude-code

How 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/generate-candidate-directions

Context preview

The summary Claude sees to decide when to auto-load this skill.

Generate diverse candidate research fields/subfields consistent with current context while permitting deliberate boundary-crossing.

SKILL.md

generate-candidate-directions.SKILL.md
name: generate-candidate-directions
description: "Generate diverse candidate research fields/subfields consistent with current context while permitting deliberate boundary-crossing."

generate-candidate-directions

Purpose

Generate diverse candidate research fields or subfields consistent with context while permitting deliberate boundary crossing.

Input contract

required: [research_intent, scope_anchor]
optional: [seed_evidence, actor_profile, constraints, neighboring_domains]
constraints: [each candidate must state fit, boundary crossing, and evidence needs]

Procedure

1. Extract the problem mechanism, outcome, and scope anchor. 2. Generate candidates within the anchor and from adjacent domains. 3. Record the transfer or boundary-crossing rationale for each candidate. 4. Remove duplicates while preserving materially different mechanisms.

If candidate fields are diverse enough for comparative characterization, consider `synthesize-field-panorama` as the next tactic.

Output contract

produces: [candidate_directions, fit_rationales, boundary_crossings, evidence_questions]
delta_fields: [findings, hypothesis_updates, uncertainties, open_questions, recommended_jumps]

Quality gates

  • Candidates are distinct by mechanism or evidence opportunity.
  • Speculative candidates are labeled as such.

Failure and counterexamples

Do not equate popularity with fit or generate a list of fields without a boundary rationale.

Provenance map

  • `resolved: generate-candidate-fields`
Read more
Ships withde-anthropocentric-research-engine

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.

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Python
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Apache-2.0
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5h ago
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7mo ago
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Repo: yogsoth-ai/de-anthropocentric-research-engine