Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add retentioneering/retentioneering-tools --skill retentioneering-product-analyticsgit clone --depth 1 https://github.com/retentioneering/retentioneering-toolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/retentioneering/retentioneering-tools/retentioneering-product-analytics)<a href="https://agentmods.dev/skills/retentioneering/retentioneering-tools/retentioneering-product-analytics"><img src="https://agentmods.dev/badge/skills/retentioneering/retentioneering-tools/retentioneering-product-analytics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/retentioneering/retentioneering-tools/retentioneering-product-analytics"><img src="https://agentmods.dev/badge/skills/retentioneering/retentioneering-tools/retentioneering-product-analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.01456 |
| Opus 5 | $0.00054 | $0.00728 |
| Sonnet 5 | $0.00022 | $0.00291 |
| Haiku 4.5 | $0.00011 | $0.00146 |
Grade A, and why
retentioneering-product-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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: session id, segment attributes (device, source, plan), event properties, conversion labels.
Workflow
1. Environment
- Confirm the package:
python -c "import retentioneering; print(retentioneering.__version__)". Expect 5.x; this skill's API map is version-verified for 5.0 — on a different major version, trust installed docstrings over the map. - Locate the event data (CSV / Parquet / frames in existing code). Never modify inputs.
- Do not invent methods: anything not in
references/api-map.mdmust be verified against the installed package before use.
2. Inspect the data BEFORE choosing methods
Run scripts/inspect_event_log.py <path> [--sep ...] (or replicate its checks inline for
in-memory frames). It profiles columns, infers the user/event/timestamp mapping, checks
timestamp parseability, duplicates, per-path ordering, path-length distribution, and
emits artifacts/data-profile.json plus a ready-to-paste Eventstream(...) schema.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 129 lines · 108 tokens per session scan A 9bdbdbbf13fb
retentioneering-product-analytics is a skill published in the GitHub repository retentioneering/retentioneering-tools (918 stars, last pushed 5d ago), licensed Apache-2.0. It adds 108 tokens to every session and 1,456 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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