Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/retentioneering/retentioneering-toolsnpx agentmods add skills/retentioneering/retentioneering-tools/retentioneering-contributingWrote 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-contributing)<a href="https://agentmods.dev/skills/retentioneering/retentioneering-tools/retentioneering-contributing"><img src="https://agentmods.dev/badge/skills/retentioneering/retentioneering-tools/retentioneering-contributing/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-contributing"><img src="https://agentmods.dev/badge/skills/retentioneering/retentioneering-tools/retentioneering-contributing.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 | $0.00106 | $0.01768 |
| Opus 5.5 | $0.00042 | $0.00707 |
| Sonnet 5.5 | $0.00021 | $0.00354 |
| Haiku 4.5 | $0.00011 | $0.00177 |
Grade A, and why
retentioneering-contributing 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 1mo 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contributing to retentioneering-tools
Objective
Convert a user's observation — a bug, a paper cut, a missing capability, a workaround they keep re-writing — into the smallest upstream change that would have prevented it, packaged so maintainers can accept it quickly.
Bundled references
| File | Read it when |
|---|---|
references/repo-conventions.md |
before touching code — build/test/docs commands, architecture rules, naming, sync obligations |
references/proposal-templates.md |
when drafting — issue/feature/PR templates with worked examples |
The full route: idea → merged PR
Stage 1 — Capture the observation properly (do this even for "small" ideas)
Record four things while they are fresh:
- Expectation — what the user believed would happen (quote the docstring/docs page that created the expectation, if any).
- Reality — what actually happened (exact error text or wrong output).
- Cost — time lost, wrong conclusion nearly shipped, workaround written.
- Environment —
retentioneering.__version__, Python, OS, install source (pip wheel vs source checkout).
Field lesson: reports formatted as expectation/reality/cost/repro get acted on; "X is broken" reports stall.
Stage 2 — Validate against the CURRENT version
Many pain points are already fixed on v5-migration — verify before drafting:
git log --oneline -30andCHANGELOG.md— search keywords from the observation.- Search existing issues/PRs:
gh issue list --search "<keywords>",gh pr list .... - Reproduce on the current checkout (see Stage 3). If it no longer reproduces, the contribution may become a docs clarification or a regression test instead — both welcome.
Stage 3 — Minimal reproduction (the heart of a bug report)
Build the smallest toy that shows the gap, e.g.:
import pandas as pd
from retentioneering import Eventstream
df = pd.DataFrame({"user_id": ["u1","u1","u2"], "event": ["a","b","a"],
"timestamp": pd.date_range("2026-01-01", periods=3, freq="1min")})
# EXPECTED: ... ACTUAL: ...
What ships with it
2 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.
- 1mo ago First seen · 156 lines · 106 tokens per session scan A 1c5a7e8ef3cb
retentioneering-contributing is a skill published in the GitHub repository retentioneering/retentioneering-tools (942 stars, last pushed yesterday), licensed Apache-2.0. It adds 106 tokens to every session and 1,768 once invoked, about $0.0004 per session on Opus 5.5. 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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