Scrub Reflection Self Improvement

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.