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Generate structured concept maps from academic texts automatically
Passed 21 of 23 test cases with the skill, and 16 of 23 without it.
and 4 more in the full report
5 fixed16 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
In a social science research paper concept map connecting theoretical constructs to empirical metrics, what standard linking phrase represents operationalization between a construct and its metric?
| Checked | Without skill | With skill |
|---|---|---|
| Uses the explicit linking phrase is measured by for operationalization. | Missed | Passed |
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A skill for automatically generating structured concept maps from academic texts, lecture notes, and research papers. Covers concept extraction using NLP techniques, relationship identification, hierarchical organization, and export to visual formats. Concept maps differ from mind maps in that they explicitly label relationships between concepts, making them more suitable for representing scientific knowledge.
A concept map consists of three elements: concepts (nodes), linking phrases (labeled edges), and propositions (concept-link-concept triples that form meaningful statements).
Concept Map Elements:
Concept: A perceived regularity or pattern designated by a label.
Examples: "DNA replication", "enzyme", "natural selection"
Representation: boxes or ovals containing short noun phrases
Linking Phrase: Words that connect two concepts to form a proposition.
Examples: "is catalyzed by", "requires", "leads to", "is a type of"
Representation: labeled arrows between concept nodes
Proposition: A meaningful statement formed by two concepts and a link.
Example: [DNA replication] --requires--> [DNA polymerase]
This reads: "DNA replication requires DNA polymerase"
Cross-links: Connections between concepts in different domains or
branches of the map, showing integrative understanding.
Feature Concept Map Mind Map
-------------- ------------------ ------------------
Structure Network (graph) Tree (hierarchical)
Relationships Labeled explicitly Implied by proximity
Root node May have multiple Single central topic
Cross-links Encouraged Rare
Best for Deep understanding Brainstorming
Scientific use Knowledge modeling Idea generation
test cases passed, vs 16 / 23 without
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