abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Generate diverse candidate research fields/subfields consistent with current context while permitting deliberate boundary-crossing.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill generate-candidate-directions -a windsurf -y
name: generate-candidate-directions description: "Generate diverse candidate research fields/subfields consistent with current context while permitting deliberate boundary-crossing."
Generate diverse candidate research fields or subfields consistent with context while permitting deliberate boundary crossing.
required: [research_intent, scope_anchor] optional: [seed_evidence, actor_profile, constraints, neighboring_domains] constraints: [each candidate must state fit, boundary crossing, and evidence needs]
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.
produces: [candidate_directions, fit_rationales, boundary_crossings, evidence_questions] delta_fields: [findings, hypothesis_updates, uncertainties, open_questions, recommended_jumps]
Do not equate popularity with fit or generate a list of fields without a boundary rationale.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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