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cc-thinking-skills vs graph-engineering

18 mental models as Claude Code skills — First Principles, Bayesian reasoning, Systems Thinking, OODA, Pre-Mortem and more — invoked when a problem needs a thinking framework. — versus — Graph engineering as a Claude skill: SEU's 9-stage knowledge-graph course (translated) plus task-graph orchestration patterns — with a teaching mode and nine paste-ready /kg workflows.

The curated verdict

Both install thinking frameworks as skills rather than tools: 18 general mental models there, one deep domain discipline (graph construction and orchestration) here.

cc-thinking-skillsgraph-engineering
Stars881144
Forks12719
LanguageJavaScript
LicenseMITMIT
Last activityyesterday4 days ago
Topicsskillsskills
Curated connections23

cc-thinking-skills — the curator's take

Decision frameworks as installable capability: instead of hoping the model reasons well, you hand it the explicit framework the situation calls for — pre-mortem before a launch, Bayesian updating on flaky evidence, OODA under time pressure. Cheap to adopt, zero infrastructure. The honest ceiling: a framework prompt shapes reasoning, it doesn't guarantee it — the model can still pattern-match its way past the discipline; treat the outputs as structured drafts for YOUR judgment.

graph-engineering — the curator's take

A discipline-in-a-repo: the thesis that agents are steered by topology — knowledge graphs for what they remember, task graphs for how they work — backed by an actual graduate curriculum (SEU's 4.4k-star course, translated from Chinese) rather than vibes. The teaching mode is the standout: /kg-tutor walks the whole pipeline drawing diagrams from YOUR domain, and eight chained workflows go from scoping to GraphRAG. NOT proven at scale: 144 stars and young — adopt it as excellent structured knowledge, not battle-tested tooling; the graphs you build with it still need the engineering the course itself teaches (fusion and provenance are where naive KG projects drown).