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Add Glama score badge to FantasyLab-ai/aurora entry

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@@ -1539,7 +1539,7 @@ Integrations and tools designed to simplify data exploration, analysis and enhan
- [Daichi-Kudo/llm-advisor-mcp](https://github.com/Daichi-Kudo/llm-advisor-mcp) [![Daichi-Kudo/llm-advisor-mcp MCP server](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp) 📇 ☁️ 🍎 🪟 🐧 - Real-time LLM/VLM model comparison with benchmarks, pricing, and personalized recommendations from 5 data sources. No API key required. - [Daichi-Kudo/llm-advisor-mcp](https://github.com/Daichi-Kudo/llm-advisor-mcp) [![Daichi-Kudo/llm-advisor-mcp MCP server](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp/badges/score.svg)](https://glama.ai/mcp/servers/Daichi-Kudo/llm-advisor-mcp) 📇 ☁️ 🍎 🪟 🐧 - Real-time LLM/VLM model comparison with benchmarks, pricing, and personalized recommendations from 5 data sources. No API key required.
- [DataEval/dingo](https://github.com/DataEval/dingo) 🎖️ 🐍 🏠 🍎 🪟 🐧 - MCP server for the Dingo: a comprehensive data quality evaluation tool. Server Enables interaction with Dingo's rule-based and LLM-based evaluation capabilities and rules&prompts listing. - [DataEval/dingo](https://github.com/DataEval/dingo) 🎖️ 🐍 🏠 🍎 🪟 🐧 - MCP server for the Dingo: a comprehensive data quality evaluation tool. Server Enables interaction with Dingo's rule-based and LLM-based evaluation capabilities and rules&prompts listing.
- [datalayer/jupyter-mcp-server](https://github.com/datalayer/jupyter-mcp-server) 🐍 🏠 - Model Context Protocol (MCP) Server for Jupyter. - [datalayer/jupyter-mcp-server](https://github.com/datalayer/jupyter-mcp-server) 🐍 🏠 - Model Context Protocol (MCP) Server for Jupyter.
- [FantasyLab-ai/aurora](https://github.com/FantasyLab-ai/aurora) 🐍 🏠 🍎 🪟 🐧 - Glass-box statistical analysis: 19 research-grade methods, cited findings, integrity-hashed bundles, and measured detector false-fire rates shipped as a calibration corpus. Agents cite real math instead of inventing it. `uvx aurora-mcp`. - [FantasyLab-ai/aurora](https://github.com/FantasyLab-ai/aurora) [![FantasyLab-ai/aurora MCP server](https://glama.ai/mcp/servers/FantasyLab-ai/aurora/badges/score.svg)](https://glama.ai/mcp/servers/FantasyLab-ai/aurora) 🐍 🏠 🍎 🪟 🐧 - Glass-box statistical analysis: 19 research-grade methods, cited findings, integrity-hashed bundles, and measured detector false-fire rates shipped as a calibration corpus. Agents cite real math instead of inventing it. `uvx aurora-mcp`.
- [growthbook/growthbook-mcp](https://github.com/growthbook/growthbook-mcp) 🎖️ 📇 🏠 🪟 🐧 🍎 — Tools for creating and interacting with GrowthBook feature flags and experiments. - [growthbook/growthbook-mcp](https://github.com/growthbook/growthbook-mcp) 🎖️ 📇 🏠 🪟 🐧 🍎 — Tools for creating and interacting with GrowthBook feature flags and experiments.
- [gpartin/WaveGuardClient](https://github.com/gpartin/WaveGuardClient) [![WaveGuard MCP server](https://glama.ai/mcp/servers/WaveGuard/badges/score.svg)](https://glama.ai/mcp/servers/WaveGuard) 🐍 ☁️ 🍎 🪟 🐧 - Physics-based anomaly detection via MCP. Uses Klein-Gordon wave equations on GPU to detect anomalies with high precision (avg 0.90). 9 tools: scan, fingerprint, compare, token risk, wallet profiling, volume check, price manipulation detection. - [gpartin/WaveGuardClient](https://github.com/gpartin/WaveGuardClient) [![WaveGuard MCP server](https://glama.ai/mcp/servers/WaveGuard/badges/score.svg)](https://glama.ai/mcp/servers/WaveGuard) 🐍 ☁️ 🍎 🪟 🐧 - Physics-based anomaly detection via MCP. Uses Klein-Gordon wave equations on GPU to detect anomalies with high precision (avg 0.90). 9 tools: scan, fingerprint, compare, token risk, wallet profiling, volume check, price manipulation detection.
- [haiiibin/data-profiler-mcp](https://github.com/haiiibin/data-profiler-mcp) [![haiiibin/data-profiler-mcp MCP server](https://glama.ai/mcp/servers/haiiibin/data-profiler-mcp/badges/score.svg)](https://glama.ai/mcp/servers/haiiibin/data-profiler-mcp) 🐍 🏠 🍎 🪟 🐧 - Profiles tabular data files (CSV, TSV, Parquet, Excel, JSON) for LLM agents: one-call dataset overview, per-column statistics, a data-quality audit (missing values, duplicates, mixed types, outliers), and memory-saving dtype suggestions. Pure Python (pandas); files are read locally and nothing leaves your machine. `pip install data-profiler-mcp`. - [haiiibin/data-profiler-mcp](https://github.com/haiiibin/data-profiler-mcp) [![haiiibin/data-profiler-mcp MCP server](https://glama.ai/mcp/servers/haiiibin/data-profiler-mcp/badges/score.svg)](https://glama.ai/mcp/servers/haiiibin/data-profiler-mcp) 🐍 🏠 🍎 🪟 🐧 - Profiles tabular data files (CSV, TSV, Parquet, Excel, JSON) for LLM agents: one-call dataset overview, per-column statistics, a data-quality audit (missing values, duplicates, mixed types, outliers), and memory-saving dtype suggestions. Pure Python (pandas); files are read locally and nothing leaves your machine. `pip install data-profiler-mcp`.