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.

Decision Heuristics

• Pick the highest-leverage change with the lowest coordination overhead.
• 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.

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) 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.
• Always end with the required HARNESS_SCRUB_LOOP footer (provided by the base scrub contract).