Agent Skills
retentioneering ships two Agent Skills — task-oriented instruction packages that coding agents (Claude Code, Codex, Cursor, and other compatible tools) load automatically when a task matches, so the agent follows a tested workflow instead of improvising one from scratch each time.
This is different from the MCP server: the MCP server runs inside a local Jupyter kernel, so using it means keeping a kernel process alive for the agent to connect to. Agent Skills carry no such requirement — they teach an agent to write and run retentioneering code directly, e.g. against your own files in its normal working environment, with no server to keep running.
Available skills
| Skill | Use when |
|---|---|
retentioneering-product-analytics | You have event-level data (user, event, timestamp columns) and ask an agent why users convert, churn, loop, or abandon a flow. Drives the full workflow: inspect the log → pick a recipe → execute → validate → interpret. |
retentioneering-contributing | You ask an agent to turn a bug, friction report, or feature idea into an issue or pull request against this repository. Drives: capture → validate → minimal reproduction → issue/PR. |
The skills are duplicated under two directories in the repository, kept identical by design:
.claude/skills/— loaded automatically by Claude Code..agents/skills/— the generic/Codex-compatible location, per the open Agent Skills spec.
Each skill is a directory with a SKILL.md entry point (objective, activation criteria, step-by-step workflow) plus references/ (deep material, read on demand) and, for the product-analytics skill, a scripts/inspect_event_log.py profiler.
Using a skill
There are two ways to give an agent access to a skill:
- Regular: import the skill's
SKILL.md. Point your agent at theSKILL.mdof the skill you want (from.claude/skills/or.agents/skills/in this repository), without cloning anything else alongside it. The agent follows the skill's workflow but runs retentioneering itself from the version installed from PyPI. - Advanced: run the agent from inside a clone of the repository. Clone
retentioneering-toolsand run the agent from within the checkout. The agent still picks up the skills from.claude/skills/or.agents/skills/, but now runs retentioneering directly against the library code in the checkout instead of a PyPI install — useful when you're developing against an unreleased change or want the skill and the library code to always match exactly.
Contributing to the skills
Skills are versioned alongside the library and welcome the same kind of contributions as the code — see Contributing. If you change the public API, the product-analytics skill's references/api-map.md is version-verified against the checkout and should be re-checked; update both .claude/skills/ and .agents/skills/ copies together.