Results for “shard-replica”

Re-platforms a site to AEM Edge Delivery (or any clean front end) while keeping its current design near pixel-perfect. Recreates key pages as clean HTML/CSS, verifies each against the live site with a measured source-fidelity gate, then hands off for site-wide delivery.

Run replication, robustness, and specification-sensitivity workflows for Stata projects. Use when a researcher wants to reproduce a result, rerun a pipeline, compare specifications, audit a do-file sequence, or check whether a claim is stable.

Chunked, compressed N-dimensional arrays for cloud storage with Zarr — parallel I/O, S3/GCS integration, and NumPy/Dask/Xarray compatibility. Use when storing or reading large N-D scientific arrays, streaming chunked data to/from cloud object stores, or building large-scale scientific computing pipelines. Part of the AlterLab Academic Skills suite.

Guides scaling Qdrant vector search with time-based data rotation using shard rotation, collection rotation, or filter-and-delete strategies.

High-fidelity website/app clone design pipeline. Use when asked to clone, replicate, copy, or recreate any existing website or app screen. Captures real assets (images, SVGs, fonts, colors), extracts exact design tokens, generates the clone in Stitch, composites real assets into the output, then runs section-by-section visual comparison against the reference. Produces 90%+ accurate clones by combining Stitch generation with real asset injection.

Designs Elasticsearch indexes and mappings, tunes queries, sizes clusters, and handles operations like shard/replica strategy, ILM, monitoring, troubleshooting, and safe reindexing or upgrades.

Composite skill — safely refactor a module end-to-end with sequencing, parallel implementation, post-refactor cleanup, and rationale capture. Chains refactor-plan (phased plan + rollback) → three-man-team (architect/builder/reviewer in parallel) → fix-the-suite post-refactor → adr-write → docs-sync. Use for non-trivial refactors that need both careful sequencing and durable record.

Land and deploy workflow. (gstack)

Retrieval-Augmented Generation pipelines — ingestion, chunking, embedding, vector stores, retrieval, evaluation. Use when building a RAG pipeline, choosing chunking strategies or embedding models, debugging retrieval quality or hallucinations, evaluating an existing RAG system, or scaling/migrating vector stores.

This skill should be used when a SKILL.md file needs compression, deduplication, or token reduction. It provides an embedding-based compression pipeline that detects and removes redundant chunks within SKILL.md files using local embeddings (all-MiniLM-L6-v2). Two-pass approach: (1) free intra-skill deduplication via cosine similarity clustering, (2) optional LLM-judged graded eval to detect pretraining overlap. Typical result: 25-46% token reduction with zero quality loss. This skill is not intended for editing skill content, creating new skills, routing optimization, or cross-skill deduplication.