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Found 148 Skills
Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.
Analyzes and designs batch and streaming data pipelines with contracts, lineage, reliability, and cost controls. Use for ingestion and transformation systems. NOT for ad-hoc analysis or schema design.
Extract vendor, date, items, amounts, and total from receipt images using OCR and pattern matching with structured JSON output.
Explains core Apache Beam programming model concepts including PCollections, PTransforms, Pipelines, and Runners. Use when learning Beam fundamentals or explaining pipeline concepts.
Batch create professional certificates with customizable templates for courses, achievements, and awards. Supports CSV input and PDF export.
Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.
Polish raw screenshots into LP-ready heroes.
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).
Provides strategies for efficiently transforming large text files (thousands to millions of lines) using text editors like Vim, sed, or awk. This skill should be used when tasks involve bulk text transformations, CSV manipulation at scale, pattern-based edits across massive files, or when keystroke/operation efficiency is constrained. Applicable to tasks requiring macros, regex substitutions, or batch processing of structured text data.
FFmpeg video and audio processing patterns. Use when transcoding video/audio, extracting clips, adding filters, merging media, creating thumbnails, or batch processing media files.
GPU-accelerated frame extraction for Movie_F dashcam videos. This skill should be used when the user needs to extract frames from Movie_F category dashcam videos placed in the Desktop CARDV folder. Extracts 3 frames per video (BEGIN, MIDDLE, END) using NVIDIA CUDA acceleration with automatic gap analysis, parallel processing, and strict error handling. This is specifically designed for Movie_F category only.
Use when asked to scan, decode, read, or extract data from QR codes or barcodes in images.