Skip to main content
Manus에서 모든 스킬 실행
원클릭으로
GitHub 저장소

agent-skills

agent-skills에는 surrealdb에서 수집한 skills 8개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.

수집된 skills
8
Stars
19
업데이트
2026-06-16
Forks
2
직업 범위
직업 카테고리 2개 · 100% 분류됨
저장소 탐색

이 저장소의 skills

surrealdb-js
소프트웨어 개발자

Using SurrealDB from JavaScript and TypeScript with the official surrealdb SDK, covering connecting (WebSocket/HTTP and embedded engines), authentication, CRUD, parameterized queries, and live queries. Use when connecting to SurrealDB from Node, Deno, Bun, or the browser, using the surrealdb npm package, or performing CRUD and real-time operations from JS/TS code. Triggers: surrealdb JS, surrealdb.js, new Surreal(), db.query, db.create, db.live, TypeScript SDK, npm i surrealdb.

2026-06-16
surrealkit
소프트웨어 개발자

Use SurrealKit, SurrealDB's schema-management and migration CLI, to scaffold projects from templates, sync schema in development, plan and execute production rollouts (with rollback), generate JSON/TypeScript types from a live database, and write declarative TOML test suites for schemas, permissions, and API endpoints. Use this skill whenever users set up, migrate, type, or test a SurrealDB schema with SurrealKit.

2026-06-16
surrealql-functions
소프트웨어 개발자

Discover and use SurrealDB's built-in SurrealQL functions with accurate, version-current signatures by running the SurrealQL language server (LSP) and tree-sitter grammar, with a namespace catalog linking every function group to its docs. Use when looking up which built-in function to use, its exact signature/arguments, getting editor completions/hover/signature-help for .surql files, or confirming a function exists in the installed SurrealDB version. Triggers: SurrealQL function, built-in function, function signature, string::, array::, math::, type::, LSP, language server, completions, surrealql-language-server.

2026-06-16
surrealql-performance
소프트웨어 개발자

Optimize SurrealDB performance through record ID and key design, indexing strategy, and computed/derived fields. Use when queries are slow, when designing record IDs for locality and range scans, choosing between standard/unique/full-text/vector indexes, verifying index usage with EXPLAIN, or deciding whether to precompute values with computed fields, views, or events. Triggers: slow query, performance, record id design, range scan, DEFINE INDEX, EXPLAIN, computed field, FUTURE, DEFINE TABLE AS SELECT.

2026-06-16
surrealql
소프트웨어 개발자

Generate and modify SurrealQL queries to interact with SurrealDB databases. This includes creating and retrieving records, designing and managing schemas, establishing and querying graph relationships, performing live (real-time) queries, and leveraging all unique SurrealQL features for advanced database workflows. Use this skill whenever users need to write, adapt, or troubleshoot SurrealQL statements.

2026-06-16
surrealdb-cli
네트워크·컴퓨터 시스템 관리자

Use the `surreal` command-line binary to run a SurrealDB server (in-memory, RocksDB, SurrealKV, or TiKV), open an interactive or piped SQL REPL, import and export databases, check server readiness, upgrade the binary, repair storage, and manage SurrealML models. Use this skill whenever users run, connect to, back up, restore, or operate SurrealDB from the terminal. Triggers: surreal start, surreal sql, surreal import, surreal export, surreal isready, surreal upgrade, surreal fix, surreal ml, SurrealDB CLI, surreal server.

2026-06-15
surrealdb-python
소프트웨어 개발자

Using SurrealDB with the Python SDK, covering both client/server mode (WebSocket) and embedded mode (in-memory or file-based). Use when connecting to SurrealDB from Python, using the surrealdb Python package, running SurrealDB embedded without a server, or performing CRUD operations from Python code. Triggers: surrealdb Python, Surreal(), AsyncSurreal(), Python SDK, embedded SurrealDB, mem://, file://.

2026-04-08
surrealdb-vector
소프트웨어 개발자

Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.

2026-04-08