analogical-discovery
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.
Skill by yogsoth-ai
Install
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.
npx agentmarket install skill/yogsoth-ai-analogical-discovery
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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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Network hosts it talks to 1
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.
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Signals
- 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
- Source
- GitHubgithub-graphql
More from yogsoth-ai
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falsification-first-audit
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Convert a sharp scientific claim into the cheapest decisive falsification program.
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
adjust-abstraction-scope
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Move a scientific object up/down in abstraction or narrow/broaden selected scope dimensions (population, mechanism, context, outcome, timeframe, system…
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
statistical-testing
yogsoth-ai
Apply the pre-declared statistical/estimation procedure and report effect size, uncertainty, model checks, and decision-relevant interpretation.
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
estimate-effective-evidence-count
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Estimate an effective number of independent evidence/reasoning paths after discounting dependence/correlation; report nominal N, dependence structure, and…
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
design-falsification-test
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Specify a concrete observation, computation, intervention, counterexample, or measurement that would count against the sharpened claim, prioritizing cheap…
SkillAFreshness: A★ 505Claude Code +9 - 0.600score
adversarial-deliberation
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Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner.
SkillAFreshness: A★ 505Claude Code +9
Cite this page
analogical-discovery skill. Agent Mart. https://agentmart.dev/skills/yogsoth-ai-analogical-discovery (last updated 2026-10-24)