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Found 1,840 Skills
SIMD intrinsics skill for x86 (SSE/AVX) and ARM (NEON) vectorization. Use when reading auto-vectorization reports, writing SSE2/AVX2/NEON intrinsics, checking CPU feature flags at runtime, choosing between compiler builtins and raw intrinsics, or diagnosing why auto-vectorization failed. Activates on queries about SIMD, SSE2, AVX2, NEON, intrinsics, -fopt-info-vec, auto-vectorization, or vectorization failures.
Provision a dedicated PolarDB-X distributed database instance instantly with no auth required. Each instance is a full 2C4G standard edition with MySQL compatibility, distributed transactions, and vector search.
Debugging toolkit for AI agents. Diagnose symptoms via memory cache -> behavior cache -> codebase search, trace data flow, git-bisect bad commits, and compare output directories.
Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Configure and operate the Neo4j Connector for Kafka (sink + source) and the native Neo4j CDC API. Covers Cypher/Pattern/CUD sink strategies, CDC-based and query-based source, exactly-once semantics, DLQ error handling, Confluent Cloud managed connector, schema registry (Avro/JSON), and native db.cdc.query cursor-loop patterns (Neo4j 5.13+ Enterprise/Aura BC/VDC). Use when streaming Kafka events into Neo4j, streaming Neo4j changes to Kafka, or querying Neo4j change events without Kafka. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle bulk CSV/file import — use neo4j-import-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Scaffolds Remotion project folder structure, base configuration files, and file organization. Focuses ONLY on directory creation, empty file templates, and Remotion configuration. Use when starting a new video project or when asked to "scaffold Remotion project", "create project structure", "setup Remotion folders".
Provides domain knowledge and guidance for the Flare Data Connector (FDC)—attestation types, request flow, Merkle proofs, verifier/DA Layer, and smart contract integration. Use when working with FDC, cross-chain attestations, EVMTransaction, Web2Json, Payment, AddressValidity, XRPPayment, XRPPaymentNonexistence, proof-of-reserves, weather insurance, or Flare Developer Hub FDC guides and starter repos.
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
Test if user signup is open and identify potential abuse vectors in the registration process.
Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.