No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by benseverndev-oss · MCP Server · ★ 132
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is goldenmatch safe to install? View the security audit →
🟡 Golden Suite A polyglot data-quality and entity-resolution toolkit. Polished, opinionated, AI-native. GoldenCheck profiles → GoldenFlow standardizes → GoldenMatch deduplicates → GoldenAnalysis reports, all orchestrated by GoldenPipe. With InferMap for schema mapping, a Rust extension layer for Postgres / DuckDB, and optional WebAssembly acceleration behind the edge-safe TypeScript ports. ⚡ GoldenMatch scales from a CSV on your laptop to 100M+ rows on a Ray cluster — verified: 100,000,000 records deduped recall-complete (correct across any partitioning) in 9.2 min, with a 0.36 GB driver footprint. [ server that implements the Zettelkasten knowledge management methodology, allow
🏛 [UNDER CONSTRUCTION] A (roman) claude plugin marketplace
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goldenmatch is Zero-config entity resolution feeding a durable identity layer: messy records from any source become stable golden entities, a Customer 360 with provenance, merge/split and audit. Fellegi-Sunter beats. It is categorized as a MCP Server with 132 GitHub stars.
goldenmatch is primarily written in Python. It covers topics such as data-cleaning, data-engineering, data-matching.
You can find installation instructions and usage details in the goldenmatch GitHub repository at github.com/benseverndev-oss/goldenmatch. The project has 132 stars and 15 forks, indicating an active community.
goldenmatch is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to goldenmatch on Agent Skills Hub include paiml-mcp-agent-toolkit, zettelkasten-mcp, claude-emporium. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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