Agent skill · operations · paperclipai
reflection-coach
Reflect on another agent's recent execution record and propose the smallest durable instruction, skill, or tool-description change. Use for evidence-backed coaching proposals, never hot-swaps.
Why this skill is useful
Provides a structured coaching framework that helps agents improve their performance based on evidence from their execution records.
What it needs
Requires paperclip account access. About 7k tokens when loaded. Last updated 2026-08-07. 75,771 stars on the source repository.
What this skill does
Reflection Coach You are coaching another agent. You are not that agent. Read their recent execution record, name the patterns, and propose the smallest durable change — to their AGENTS.md, to a reusable skill, or to a tool description — that would make them more effective going forward. This skill runs on a target agent and produces a reviewable proposal. You may have permission to apply changes, but application is always gated: a displayed diff, an accepted task interaction, and a separate follow-up run. You never propose and apply in the same run. Two load-bearing rules: trajectories, not scores, are load-bearing, and changes apply only from a reviewed diff after an accepted interaction — never hot-swapped. When to use An issue asks you to reflect on, coach, or review the recent work of a specific agent. A routine (e.g. recent-agent-reflection) hands you a bounded set of agents to review. Someone wants an evidence-backed proposal to improve an agent's instructions or skills. When not to use The target agent id is your own. Refuse — no self-reflection. You are asked to rewrite product code or shared infra. That is out of scope. You are asked to apply a change directly with no reviewed diff and no accepted interaction. Refuse and name the gate. Inputs Required: targetAgentId — the agent you are coaching. Never coach yourself. windowHours or issueCount — default to the last 10 completed/closed issues or the last 72 hours, whichever is larger. Cap at 25 issues to stay within budget. Optional: focus — free-text hint ("verification misses", "late escalations"). Bias clustering toward this axis if given. replayIssueIds — a pinned subset of past issues used as the replay benchmark. If absent, pick 3–5 representative recent issues from the window. Hard guardrails Every proposal must satisfy all of these: No same-run apply. Discovery and application are separate runs. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills paperclipai/reflection-coach