AugmentClaude

Retentioneering Contributor

Turn bug reports and feature ideas into polished pull requests for Retentioneering.

Installation

  1. 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 run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

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  2. 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)
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  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. 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

FileRead it when
references/repo-conventions.mdbefore touching code — build/test/docs commands, architecture rules, naming, sync obligations
references/proposal-templates.mdwhen 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:

  1. Expectation — what the user believed would happen (quote the docstring/docs page that created the expectation, if any).
  2. Reality — what actually happened (exact error text or wrong output).
  3. Cost — time lost, wrong conclusion nearly shipped, workaround written.
  4. Environmentretentioneering.__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:

  1. git log --oneline -30 and CHANGELOG.md — search keywords from the observation.
  2. Search existing issues/PRs: gh issue list --search "<keywords>", gh pr list ....
  3. 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

SituationShape
Clear defect with reproIssue with repro; PR with fix + regression test if user wants to go further
Surprising-but-documented behaviorDocs PR (docstring is the source of truth — site pages regenerate from it)
Missing capabilityFeature issue: use-case first, proposed signature second, evidence third (see templates)
Repeated workaround in user's codeExtract 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:

  1. Fork or branch from master-tracking v5-migration; one logical change per PR.
  2. 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's lint job flags the formatting on your PR. Add make build only when touching widgets/JS.
  3. Follow naming conventions (ADR-0008): path_col/event_col/timestamp_col/session_col, start_event/end_event, verb-first processors, noun widgets, <widget>_data twins.
  4. All DuckDB execution goes through the unified query engine (L1) — no ad-hoc duckdb.sql with replacement-scan idioms (superseded ADR-0002).
  5. Add/extend tests next to the code area (tests/...); a bug fix MUST include the failing-before test from Stage 3.
  6. 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.
  7. 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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