# Scrub Reflection Self Improvement

> Run the repository self-improvement scrub only from its scheduler or an explicit request for that scrub.

- Skill: `marin-community/scrub-reflection-self-improvement` (Agent Skill)
- Install (CLI): `npx skillmds@latest add marin-community/scrub-reflection-self-improvement`
- Raw SKILL.md: https://api.skillmd.com/api/skills/marin-community/scrub-reflection-self-improvement/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: marin-community (https://skillmd.com/u/marin-community)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/marin-community/scrub-reflection-self-improvement

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# scrub-reflection-self-improvement

Use this skill on scheduled scrub turns to identify and land high-leverage improvements in `marin-community/marin`.

## Focus

- Look for improvements from recent issues, PR feedback, and recurring operational friction.
- Prefer one concrete implementation per run when feasible.
- If implementation is blocked, produce a concrete plan and capture follow-up work in GitHub.

## Candidate Signals

- Repeated confusion in docs, recipes, or contributor workflows.
- Recurring failures or avoidable manual steps in experiments, scripts, and infra operations.
- Capability gaps that reduce the value of agent-assisted contributions.

## Triage Checklist

Run a lightweight, repeatable scan before choosing work:

1. Review recent open issues and open PRs for repeated friction clusters.
2. Explicitly check for already-open scrub-generated issues/PRs touching the same area; prefer advancing or deferring to that existing artifact instead of creating a parallel one.
3. Review the latest commits on `main` for changes that imply follow-on docs/workflow updates.
4. Search `AGENTS.md`, `.agents/skills/`, and docs for stale workflow guidance related to those clusters.
5. De-duplicate against existing issues/PRs before creating new artifacts.

When possible, prefer improvements that remove recurring operator time (for example,
turning ad-hoc scrub judgment into explicit repeatable guidance).

## Decision Heuristics

- Pick the highest-leverage change with the lowest coordination overhead.
- Treat open scrub-generated issues/PRs as first-class prior art during triage; if one already covers the candidate improvement, avoid opening a second artifact unless the new scope is clearly distinct.
- De-duplicate against existing issues/PRs before opening new work.
- When an improvement changes recurring workflow guidance, codify it in durable repo instructions:
  `AGENTS.md` for cross-cutting agent behavior, or `.agents/skills/` for repeatable task workflows.
- If no justified improvement exists now, choose a no-op outcome.
- Prefer direct implementation over opening new issues when the change is fully in-repo and low-risk.

## Output

- Keep rationale explicit: observed gap, change made (or plan), and expected impact.
- Prefer durable artifacts over transient notes: land guidance updates in `AGENTS.md` and/or recipe docs when that is the primary improvement.
- Treat local-only edits as incomplete work. If you modify files, publish the result (commit/push and open or update a PR per `.agents/skills/commit/SKILL.md`) before finishing this scrub run.
- If publish is blocked (auth, permissions, CI infra, etc.), report the blocker and set a future `needs_followup_at` instead of ending the run.
- If you choose no-op, include explicit inspected signals and why no justified improvement exists now.
- End the run with exactly one footer line of valid one-line JSON: `HARNESS_SCRUB_LOOP {"needs_followup_at":null}`. Set `needs_followup_at` to null when the run is complete, or a future RFC 3339 timestamp when another follow-up turn is needed.

For no-op outcomes, include at minimum:

- which issue/PR/commit windows were inspected,
- why each candidate was not suitable for this run,
- and why deferring to the next scheduled run is preferable to opening a low-signal artifact now.