timescaledb
MANDATORY when working with time-series data, hypertables, continuous aggregates, or compression - enforces TimescaleDB 2.24.0 best practices including lightning-fast recompression, UUIDv7 continuous aggregates, and Direct Compress
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
epsilon-yajna
Structured node compression ceremony for converting verbose memories to high-epiplexity patterns. Use when batch-migrating nodes to improve reconstructability (ε).
image-enhancement-suite
Batch image processing: resize, crop, watermark, color correction, format conversion, compression. Quality presets for web, print, and social media.
pdf-toolkit
Comprehensive PDF manipulation - merge, split, rotate, extract pages, add watermarks, compress, and encrypt PDFs programmatically.
octave-ultra-mythic
Ultra-high density compression using mythological atoms and semantic shorthand. Preserves soul and constraints at 60% compression for identity transmission, binding protocols, and extreme token scarcity.
web-performance-optimization
Optimize web application performance using code splitting, lazy loading, caching, compression, and monitoring. Use when improving Core Web Vitals and user experience.
api-response-optimization
Optimize API response times through caching, compression, and efficient payloads. Improve backend performance and reduce network traffic.
octave-mythology
Functional mythological compression for OCTAVE documents. Semantic shorthand for LLM audiences, not prose decoration
octave-ultra-mythic
Ultra-high density compression using mythological atoms and semantic shorthand. Preserves soul and constraints at 60% compression for identity transmission, binding protocols, and extreme token scarcity.
setup-timescaledb-hypertables
Step-by-step instructions for designing table schemas and setting up TimescaleDB with hypertables, indexes, compression, retention policies, and continuous aggregates. Instructions for selecting: partition columns, segment_by columns, order_by columns, chunk time interval, real-time aggregation.
