retentioneering-product-analytics
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.
npx skills add retentioneering/retentioneering-tools --skill retentioneering-product-analytics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Retentioneering product analytics ## Objective Turn event-level behavioral data into a reproducible answer to a **product question** — why users convert, churn, loop, or abandon — using user trajectories, transitions, funnels, and behavioral segments. Do not merely generate visualizations. Connect each output to the question, separate observation from interpretation, and never present path correlations as causal effects. ## Bundled references (read on demand, not upfront) | File | Read it when | |---|---| | `references/api-map.md` | before writing any Retentioneering call — verified signatures, argument conventions, return shapes for 5.x | | `references/analysis-recipes.md` | after the question is clear — 10 field-tested patterns (R1–R10) with skeletons and pitfalls | | `references/gotchas-and-validation.md` | before executing (API gotchas G1–G10) and before presenting (integrity checklist B1–B10) | | `scripts/inspect_event_log.py` | step 2 — automated data profiling and schema suggestion | ## Required event-log semantics Minimum: a path identifier (user or session), an event name, a timestamp (or a reliable order column — see gotcha G2 for order-only data). Useful extras: sessio
- Objective
- Bundled references (read on demand, not upfront)
- Required event-log semantics
- Workflow
- 1. Environment
- 2. Inspect the data BEFORE choosing methods
- 3. Frame the product question, then pick the SMALLEST recipe
- 4. Execute reproducibly
- 5. Validate before presenting
- 6. Interpret and deliver
What does the retentioneering-product-analytics skill do?
Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.
How do I install it?
Run `npx skills add retentioneering/retentioneering-tools --skill retentioneering-product-analytics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From retentioneering/retentioneering-tools, a repository with 911 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.