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 retentioneering · MCP Server · ★ 918
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is retentioneering-tools safe to install? View the security audit →
What is Retentioneering? Retentioneering is an open-source Python toolkit, MCP server, and collection of agent skills for reproducible product analytics on clickstream and event log data. Instead of relying on one-off scripts generated for a single question, analysts and AI agents can use tested analytical primitives and reusable workflows to inspect customer journeys, explore graph-based user flows, discover behavioral segments, evaluate experiments, and cross-che
| Stars | 918 |
| Forks | 137 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 71.6030115974742/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-05 |
| Created | 2019-07-02 |
| Platforms | browser, cli, mcp, python |
| Est. Tokens | ~16k |
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retentioneering-tools is Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product ana. It is categorized as a MCP Server with 918 GitHub stars.
retentioneering-tools is primarily written in Python. It covers topics such as agent-skills, analytics-mcp, behaviour-analysis.
You can find installation instructions and usage details in the retentioneering-tools GitHub repository at github.com/retentioneering/retentioneering-tools. The project has 918 stars and 137 forks, indicating an active community.
retentioneering-tools is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to retentioneering-tools on Agent Skills Hub include prompttools, tools, samples. 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.
Sources & who's responsible: