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analogical-discovery

Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.

Skill by yogsoth-ai

IndexedApache-2.0★ 505
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.

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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.

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  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/analogical-discovery

What's inside

Skill name
analogical-discovery
Description
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.
License
Apache-2.0
Instructions
35 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# analogical-discovery
## Purpose
Transfer a validated relational structure from a source domain into a target research problem.
## Input contract
```yaml
required: [target_problem, source_domain]
optional: [candidate_sources, transfer_constraints]
constraints: [source and target roles must be explicit]
```
## Execution protocol
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

1. You MUST load skill `abstract-structure` to abstract the relational structure.
2. You MUST load skill `map-analogy` to map source relations to the target.
3. You MUST load skill `instantiate-transfer` to instantiate and test the transferred mechanism.
   If the analogy should be expanded across a typed combination space, consider `explore-dimensional-space`. If biological mechanisms are the relevant source domain, consider `biomimetic-transfer`. If several source structures must be composed, consider `conceptual-blending`. If the claimed mapping requires a formal preservation audit, `audit-structural-equivalence` may be the better next tactic.
Deviation: skip source search only when a supplied source is structurally specified; never skip mapping or transfer validation.
## Output contract
```yaml
produces: [abstract_structure, structural_mapping, transfer_candidate]
delta_fields: [findings, hypothesis_updates, uncertainties, decisions, open_questions]
```
## Thresholds and quality gates
- B: every transfer records source/target correspondences, unmapped relations, and a depth check; surface similarity alone is insufficient.
## Failure and counterexamples
Reject transfers whose causal/relational roles do not map, whose target constraints are violated, or whose claimed mechanism is only lexical resemblance.
## Provenance map
- `creative-ideation/cross-domain-discovery`, `analogical-transfer`, `design-by-analogy`, `functional-analogy`, `analogy-extraction`, `bridge-validation`: resolved where exact v3 node exists; campaign/strategy labels remain concept provenance.
- Status: all six exact names resolved against the v3 source graph.
## Preserved source criteria ledger
- Preserve deep structural correspondence and transfer viability; do not collapse to keyword similarity.
## Context checkpoint / Delta notes
Append source relations, mapping gaps, transfer assumptions, and validation findings.

From yogsoth-ai/de-anthropocentric-research-engine, Apache-2.0.

More from yogsoth-ai/de-anthropocentric-research-engine

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Stars
505
Forks
43
Open issues
1
Last commit (this folder)
Sep 25, 2026 (1 month ago)
Last release
Oct 23, 2026 (5 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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analogical-discovery skill. Agent Mart. https://agentmart.dev/skills/yogsoth-ai-analogical-discovery (last updated 2026-10-24)