Retentioneering Contributor
Turn bug reports and feature ideas into polished pull requests for Retentioneering.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/retentioneering-contributing-retentioneering/— the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.
Prefer to read the source first? Open on GitHub.
When Claude uses it
Help the user turn their Retentioneering ideas, friction reports, bug findings, or feature needs into high-quality upstream contributions: from capturing and validating the idea, through minimal reproductions and issue drafts, to preparing, testing, and submitting a pull request that follows this repository's conventions. Use when the user says they found a bug, wants a feature, wrote a workaround worth upstreaming, or asks how to contribute, open an issue, or make a PR to retentioneering-tools.
What this skill does
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: ...
Rules: synthetic data only (never the user's real log); deterministic (fixed frames, no randomness without seed); one behavior per repro; assert the expectation so the repro doubles as a failing test.
Stage 4 — Choose the contribution shape
| Situation | Shape |
|---|---|
| Clear defect with repro | Issue with repro; PR with fix + regression test if user wants to go further |
| Surprising-but-documented behavior | Docs PR (docstring is the source of truth — site pages regenerate from it) |
| Missing capability | Feature issue: use-case first, proposed signature second, evidence third (see templates) |
| Repeated workaround in user's code | Extract as proposed API: show the workaround, its cost, the proposed call replacing it |
| Wrong-conclusion trap (library was silent) | Frame as "missing signal": what the library knew and did not surface; propose the warning/field |
For API proposals, the accepted framing (from templates): problem → evidence of frequency → proposed signature → semantics incl. edge cases → acceptance criteria → migration notes.
Stage 5 — Implement (when making a PR, not just an issue)
Read references/repo-conventions.md first. Non-negotiables:
- Fork or branch from
master-trackingv5-migration; one logical change per PR. make install-dev— installs deps (uv sync+npm install) and wires the git hook (a one-time-per-clone step) so commits are auto-checked; skip it and commits bypass the hooks locally and CI'slintjob flags the formatting on your PR. Addmake buildonly when touching widgets/JS.- Follow naming conventions (ADR-0008):
path_col/event_col/timestamp_col/session_col,start_event/end_event, verb-first processors, noun widgets,<widget>_datatwins. - All DuckDB execution goes through the unified query engine (L1) — no ad-hoc
duckdb.sqlwith replacement-scan idioms (superseded ADR-0002). - Add/extend tests next to the code area (
tests/...); a bug fix MUST include the failing-before test from Stage 3. - Keep the loud-and-helpful error pattern: errors that list valid values/keys are the house style; silent degradation (dropped rows, empty results without a signal) is an auto-reject.
- Sync obligations when renaming/changing API: MCP tool layer mirrors Eventstream
method names; JS metric editor consumes the Python metric schema; docstrings feed the
docs site — update all in the same PR (
uv run python docs/scripts/render_pages.py).
Stage 6 — Pre-flight and submit
uv run pre-commit run --all-files # ruff lint+format, gitleaks, hygiene
uv run pytest tests/ -v # full suite (CI runs 3.11–3.13)
uv run python docs/scripts/render_pages.py # if docstrings changed
Commit style: imperative, scoped, explaining WHY when non-obvious (see git log for the
house voice). Update CHANGELOG.md under the unreleased/current section for
user-visible changes.
Submit:
git push -u origin <branch>
gh pr create --title "<imperative summary>" --body-file pr_body.md
PR body (template in references/proposal-templates.md): what & why → linked issue →
repro/before-after → tests added → sync checklist (docs/MCP/JS if applicable) →
breaking-change note. CI must pass: lint + test (3.11/3.12/3.13). master is
PR-only; merging does not release (releases are tag-driven by maintainers).
Stage 7 — Follow through
Respond to review within the PR (avoid force-push after review starts; append commits). If maintainers ask for direction changes, update the issue first, then the code — the issue is the contract.
Portfolio mode: many observations at once
When the user accumulated a batch (e.g., a journal of friction from a project): deduplicate → verify each against current version (Stage 2) → rank by (frequency × silent-failure risk) → file the top 3–5 as separate issues with repros → offer one PR for the cheapest verified fix to build credibility, referencing the issues for the rest. Do not open one mega-issue.
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