Recent agent reflection sweep

This routine is paused by default and spends no tokens until an operator enables its schedule or triggers a manual run. When it runs, it produces coaching proposals only.

What this run must do

1. Select target agents using {{targetAgentMode}}:
   • recent_active — agents with completed/in-review/blocked issue activity within the last {{lookbackDays}} days.
   • all — every non-terminated agent in the company.
   • explicit — only agents named in the run inputs. Cap the set at {{maxTargetAgents}}. Drop any agent id listed in {{excludeAgentIds}}, and always drop your own PAPERCLIP_AGENT_ID (no self-reflection).
2. For each selected target, run the reflection-coach skill as the operating procedure: pull recent trajectories, read current AGENTS.md and assigned skills, cluster evidence-backed patterns, and draft the smallest durable change.
3. Produce, per target agent, a proposal document with clustered patterns, linked issue/comment evidence, minimal diffs, and replay cases. Create a follow-up proposal issue when a change is worth carrying forward.

Hard limits for this routine

• Proposal-only. This routine must not edit any agent's live AGENTS.md, skill assignments, or tool descriptions directly.
• Any actual instruction/skill/tool-description change requires a displayed diff and an accepted request_confirmation task interaction, applied only in a separate follow-up run.
• Mutation confirmations must bind the exact resource key they will apply, using agent:<agentId>:instructions, agent:<agentId>:profile, skill:<skillId>, skill-slug:<slug>, skill-import:<source>, or skills:scan-projects.
• Keep every read company-scoped. Do not cross company boundaries.
• Every proposed rule needs linked issue/comment evidence or it is dropped. No scoring without trajectories.
• Respect the size caps: AGENTS.md +20% max per proposal, skills 15KB max, tool descriptions 500 chars max.

Output

A single bounded routine issue that links one proposal document (or follow-up proposal issue) per reviewed target agent, plus a summary comment listing: agents reviewed, window, clusters found, surfaces proposed, and the next-step owner for each accepted-or-pending change.