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retentioneering-tools

Skill · GitHub · ★ 943 · graded 2026-10-23Source linked

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 analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation

No outside reports on file

Use this tool only with trusted, non-sensitive event data, keep dependencies patched, and never let the AI agent share raw data or models outside your organization.

Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee.

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What it can access

  • Local filesNo local folder access notedLOW
  • Command executionNo shell command runner notedLOW
  • Network reachConnects to outside service APINOTED
  • CredentialsNo API token requiredLOW
  • Package sourceGitHub (retentioneering/retentioneering-tools)PUBLIC

Inferred from this listing’s public package details and description. Check the project’s source before installing.

Install

No npm or PyPI package is listed. Install from source: github.com/retentioneering/retentioneering-tools ↗

All setup options ↓

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Everything known

We found nothing about this project in the sources we read (last checked 2026-10-30). That does not mean it has no problems.

0 incidents · 0 advisories · 0 news articles · 0 community threads · checked 2026-10-30

Popularity grade: C (from public signals such as stars and downloads, not a code review). Graded 2026-10-23.

Sources we read: OSV, GitHub Advisory Database, CISA Known Exploited Vulnerabilities, 44 security news feeds and our incident library. Last checked 2026-10-30.

Check a specific version →

About Retentioneering Tools

Retentioneering-tools is a Python library that helps analyze user behavior data like click sequences and event logs. It builds models of how users move through products, finds patterns in their actions, and can simulate what might happen next using math-based methods.

Who it’s for

Data scientists, product analysts, and AI agents who need to understand user journeys and test product changes. It's designed for teams building reproducible, auditable analytics rather than one-off reports.

Alternatives

  • Mixpanel / Amplitude: Commercial platforms for event analytics and user journey tracking (cloud-based, not open-source).
  • PostHog: Open-source product analytics platform with event tracking and user cohorts built in.
  • mParticle / Segment: Data integration platforms that route and analyze event streams (commercial).
  • Apache Spark MLlib: General-purpose machine learning library that can model user sequences at scale (less specialized for product analytics).

Setup

Check these steps against the project’s own instructions before running anything.

Follow the setup steps in the project’s official README: https://github.com/retentioneering/retentioneering-tools (opens external site)

Why this grade

CC — Moderate risk signals

Signal-based grade (3/7 points): well starred on GitHub, public source code. This reflects adoption and transparency, not a code review.

Popularity and upkeep

32/100
GitHub stars
943
Listing last refreshed
2026-10-30

These are public popularity signals, not a maintenance check. Look at the repository’s commit history and open issues.

Risks to watch for

  • Access to user behavior data: The tool processes clickstream and event logs, which can contain sensitive information about what users do and when. An AI agent with access could expose this data if outputs aren't controlled.
  • Indirect code execution: If the tool loads data from untrusted sources (files, APIs), malicious data might be interpreted as commands or cause crashes.
  • Model poisoning: Attackers who control input event logs could craft fake patterns that make the models produce false insights, tricking decisions based on the analytics.
  • Dependency vulnerabilities: The tool depends on other Python packages that might have security flaws; outdated versions could be exploited.
  • Leaking analytics to third parties: If an agent shares results or model files without permission, proprietary user behavior insights could be exposed.

How to lower the risk

Least-privilege permissions

  • Run the tool in a Python environment with read-only access to data files.
  • Don't grant the agent permission to download or install new packages at runtime.

Network isolation

  • Block outbound network access unless the tool explicitly needs to fetch data from your own servers.
  • Isolate the tool's environment so it can't reach production user databases.

Monitoring

  • Log what data the tool processes and what outputs it produces.
  • Alert on unusual data volumes or unexpected file writes.

Red flags to watch for

  • The tool requesting credentials or API keys it shouldn't need.
  • Attempts to write to system directories or install packages.
  • Outputs that contain raw personally identifiable information (names, emails, etc.).

Questions

How do I install Retentioneering Tools?

Retentioneering Tools does not publish a standalone npm or PyPI package. Clone or follow the setup instructions in its source repository at github.com/retentioneering/retentioneering-tools.

Are there security reports about Retentioneering Tools?

No reports found for this package in our research (last checked 2026-10-30). Retentioneering Tools currently holds Grade C (Moderate risk signals) based on public adoption and transparency signals. Key risk areas to watch include access to user behavior data, indirect code execution, model poisoning.

Is Retentioneering Tools actively maintained?

Retentioneering Tools has 943 GitHub stars. Our directory data was last refreshed on 2026-10-30. Check recent commit activity, release tags, and open issues at github.com/retentioneering/retentioneering-tools before pinning it in production.

What are alternatives to Retentioneering Tools?

Notable alternatives to Retentioneering Tools include Mixpanel / Amplitude (Commercial platforms for event analytics and user journey tracking (cloud-based, not open-source)), PostHog (Open-source product analytics platform with event tracking and user cohorts built in), mParticle / Segment (Data integration platforms that route and analyze event streams (commercial)), Apache Spark MLlib (General-purpose machine learning library that can model user sequences at scale (less specialized for product analytics)). You can also compare Retentioneering Tools side by side with other skills in the Related tools section on this page.

README badge and team notes

Maintain or use Retentioneering Tools? Show its live grade in your GitHub README, docs site, or run a check from the terminal before you install.

AgentArmorHQ Risk Grade C for Retentioneering Tools

GitHub README Markdown:

markdown
[![AgentArmorHQ Risk Grade for Retentioneering Tools](https://agentarmorhq.com/api/badge/retentioneering-retentioneering-tools)](https://agentarmorhq.com/skills/retentioneering-retentioneering-tools)

HTML embed:

html
<a href="https://agentarmorhq.com/skills/retentioneering-retentioneering-tools"><img src="https://agentarmorhq.com/api/badge/retentioneering-retentioneering-tools" alt="AgentArmorHQ Risk Grade for Retentioneering Tools" height="20" /></a>

Terminal check:

terminal
curl -fsSL https://agentarmorhq.com/api/cli | node --input-type=module - check "retentioneering-retentioneering-tools"

Share with your security lead

Need sign-off to use retentioneering-tools? Copy this note for Slack, a ticket or a pull request.

### AgentArmorHQ Security Review Memo: retentioneering-tools
- **Listing URL:** https://agentarmorhq.com/skills/retentioneering-retentioneering-tools
- **Type:** AI agent skill
- **Risk Grade:** Grade C (Moderate risk signals)
- **Outside Security Advisories on File:** 0
- **Public Adoption:** 943 GitHub stars

#### Permission & Blast-Radius Signals
- **Local files:** No local folder access noted
- **Command execution:** No shell command runner noted
- **Network reach:** Connects to outside service API
- **Credentials:** No API token required
- **Package source:** GitHub (retentioneering/retentioneering-tools)

#### Recommended Hardened Setup
- **Source Repo:** https://github.com/retentioneering/retentioneering-tools
- **Controls:** Pin an exact version in `.mcp.json`, scope access to a single workspace folder, and use a least-privilege read-only token where supported.

_Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee._

Key facts

Type
AI agent skill
Publisher
retentioneering
Source code
github.com/retentioneering/retentioneering-tools
Est. context load
~117 tokens idle · ~500 tokens when active (on-demand skill)
Popularity
943 GitHub stars
Last updated
2026-10-30
Last graded
2026-10-23

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