extract-structural-mapping
SkillDev toolsLets your agent build an explicit element-by-element mapping between two structures to check a claimed equivalence.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the extract-structural-mapping skill
About this skill
Construct an explicit source↔target mapping over objects, relations, operations, constraints, and invariants for a claimed structural equivalence.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/extract-structural-mapping/SKILL.md and read by ahel’s review.
Purpose
Construct an explicit source-to-target mapping over objects, relations, operations, constraints, and invariants.
Input contract
required: [source_structure, target_structure, mapping_claim]
optional: [required_objects, required_relations, invariants]
constraints: [mapping must preserve identity and provenance of mapped elements]
Procedure
- Enumerate source and target elements.
- Map objects, relations, operations, constraints, and invariants.
- Mark unmatched, ambiguous, and many-to-one mappings.
If source-target correspondences are explicit, consider test-structure-preservation as the next tactic.
Output contract
produces: [structural_mapping, unmatched_elements, ambiguity_report, invariant_inventory]
delta_fields: [findings, evidence_updates, uncertainties]
Quality gates
- Every claimed preserved element has an explicit mapping or is marked absent.
Failure and counterexamples
Do not infer mapping from labels or surface resemblance.
Provenance map
- resolved: isomorphism-falsification
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
extract-structural-mapping- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine