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Claude Skills by 24601
github.com/2460111 skills0 installs8 views
- SurrealdbExpert SurrealDB 3 skill. Use when working with SurrealDB, SurrealQL queries, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, graph traversal, vector search, security and permissions, deployment and operations, performance tuning, SDK integration, Surrealism WASM extensions, SurrealMCP, Surrealist IDE, Surreal-Sync migrations, SurrealFS, SurrealKit, or ecosystem integrations such as n8n and CodeMirror.Votes: 0GitHub stars: 34
- Surreal SyncData migration and synchronization to SurrealDB from MongoDB, PostgreSQL, MySQL, Neo4j, Kafka, and JSONL. Full and incremental CDC sync. Part of the surreal-skills collection.Votes: 0GitHub stars: 34
- SurrealfsSurrealFS virtual filesystem for AI agents. Rust core + Python agent (Pydantic AI). Persistent file operations backed by SurrealDB. Part of the surreal-skills collection.Votes: 0GitHub stars: 34
- SurrealismSurrealDB Surrealism WASM extension development. Write Rust functions, compile to WASM, deploy as database modules. Part of the surreal-skills collection.Votes: 0GitHub stars: 34
- SurrealkitSurrealKit schema sync, rollout migrations, seeding, and declarative testing for SurrealDB apps. Part of the surreal-skills collection.Votes: 0GitHub stars: 34
- SurrealmcpSurrealDB MCP for AI agents: built-in server (SurrealDB 3.1+ `surreal mcp`) and standalone surrealmcp (surrealdb/surrealmcp v0.4.0). Part of the surreal-skills collection.Votes: 0GitHub stars: 34
- surrealdbExpert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v...Votes: 0GitHub stars: 34
- AugustusFind, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.Votes: 0GitHub stars: 11
- Augustus TrainBuilds and improves task-specific decision artifacts from application requirements and labeled records. Use for data assembly, primitive and base-model selection, fitting, export/reload, inference policy, or bounded data/model/program improvement. Includes rules and no-training outcomes; does not require a rung ladder or training a general Jev.Votes: 0GitHub stars: 11
- Augustus TrainBuilds and improves task-specific decision artifacts from application requirements and labeled records. Use for data assembly, primitive and base-model selection, fitting, export/reload, inference policy, or bounded data/model/program improvement. Includes rules and no-training outcomes; does not require a rung ladder or training a general Jev.Votes: 0GitHub stars: 11
- AugustusFind, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.Votes: 0GitHub stars: 11