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Diagnosing Vector Database Search Quality

qdrant-search-quality-diagnosis

github/awesome-copilot

Provides a systematic guide for diagnosing and improving poor search quality in vector databases like Qdrant. This guide covers troubleshooting issues such as low recall, when approximate search degrades, selecting optimal embedding models, and correctly tuning HNSW parameters, quantization, and filtering strategies. Essential for optimizing RAG and semantic search pipelines.

Advanced Snowflake Performance Debugging

snowflake-advanced-troubleshooting

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for deep debugging complex Snowflake issues. This guide covers advanced techniques including detailed query profiling (spill analysis), diagnosing lock contention and transaction conflicts, analyzing metadata operations, and optimizing partition usage. Use it when standard troubleshooting fails to address slow queries or warehouse performance bottlenecks.

TileDB-VCF Genomic Variant Storage

K-Dense-AI/scientific-agent-skills

Store and query genomic variant data with TileDB sparse arrays. Ingest VCF/BCF files, add samples incrementally, run parallel region queries, and export subsets for population genomics workflows.

Arbitrary Precision Math Calculator

Code-and-Sorts/awesome-copilot-agents

This utility evaluates complex arithmetic expressions using arbitrary-precision decimal mathematics. It is specifically designed for scientific, financial, or engineering calculations where standard floating-point arithmetic causes unacceptable rounding errors (e.g., 0.1 + 0.2). Supports addition, subtraction, multiplication, division, exponents, and parentheses.

Expert Steganography Analysis Playbook

steganography-techniques

yaklang/hack-skills

A comprehensive playbook for digital forensic experts. It details systematic techniques and tools for detecting and extracting hidden data embedded across various media types, including images (LSB, PNG chunks, JPEG DCT, EXIF metadata), audio (spectrogram, DTMF), and files (polyglots, appended data). Essential for deep-dive forensic investigations.

R Visuals Integration for Power BI

data-goblin/power-bi-agentic-development

A comprehensive guide and workflow for embedding R scripts, primarily using the ggplot2 library, into Power BI Reports (PBIR). This tool enables the creation of high-quality, static, publication-ready statistical visualizations directly within the report canvas by executing R code on the defined dataset. It covers the full lifecycle: adding the visual, writing the script, injecting the code, and validating the output.

Standardize Power BI Naming Conventions

standardize-naming-conventions

data-goblin/power-bi-agentic-development

An interactive workflow for auditing and standardizing naming conventions (tables, columns, measures) in Power BI semantic models (TMDL). It ensures names are human-readable, consistent, and aligned with core business terminology, addressing technical issues like abbreviations, mixed casing, and inappropriate technical prefixes.

Last 30 Days Sentiment Research

last-30-days-research

mohitagw15856/pm-claude-skills

This skill systematically researches real-time public sentiment and user opinions on any topic by querying Reddit, X/Twitter, and the broader web. It bypasses curated SEO content to provide a structured report detailing consensus points, recurring pain points, genuine praise, and signal confidence, offering actionable insights into current market sentiment.

Spectral Vector Search with ArrowSpace

sickn33/antigravity-awesome-skills

A spectral vector search tool that enhances nearest-neighbor search with graph Laplacian features, capturing both semantic similarity and structural roles in embedding spaces.

Ingest Market Data for Pattern Search

This tool ingests raw market data (OHLCV) for a specified symbol. It normalizes the data into relative OHLCV vectors, converts these vectors into high-dimensional embeddings, and then stores and indexes them using HNSW. This process prepares the data for fast, similarity-based pattern detection and advanced financial time series analysis.

Claude Memory Bridge for Unified Search

This memory bridge ingests Claude Code's local auto-memory markdown files and structures them into AgentDB. It generates high-fidelity ONNX embeddings, handles deduplication using cosine similarity, and indexes the memories in a scalable namespace. This enables seamless, unified semantic search across all historical sessions and projects, significantly enhancing RAG capabilities.

Cluster Vectors by Graph Community Detection

This skill helps discover natural groupings within a large collection of semantic embeddings using the `ruvector` tool. It employs graph-based algorithms (Spectral/Louvain) to cluster code or data, revealing underlying themes, identifying outliers, and organizing vast vector sets. Ideal for analyzing codebases or large datasets to understand relationships between entries.

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