Paperclip is a Node.js server and React application for coordinating AI agents as a business team, with goals, organizational roles, budgets, governance, and work tracking in one interface. It is used by people managing multiple agents or autonomous agent organizations across tools such as OpenClaw, Claude Code, Codex, and Cursor. The catalogue includes skills, agents, an MCP add-on, and an instruction for working with Paperclip.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add paperclipai/paperclip --skill reflection-coachgit clone --depth 1 https://github.com/paperclipai/paperclipWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/paperclipai/paperclip/reflection-coach)<a href="https://agentmods.dev/skills/paperclipai/paperclip/reflection-coach"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/reflection-coach/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/paperclipai/paperclip/reflection-coach"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/reflection-coach.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.02828 |
| Opus 5.5 | $0.00015 | $0.01131 |
| Sonnet 5.5 | $0.00008 | $0.00566 |
| Haiku 4.5 | $0.00004 | $0.00283 |
Grade A, and why
reflection-coach scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sS "$PAPERCLIP_API_URL/api/agents/<targetAgentId>" \ How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.windowHoursorissueCount— 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. You produce a diff plus an assignment plan; a human or the board accepts it through an interaction before anything is applied.
- Size caps. Skills ≤ 15KB. Tool descriptions ≤ 500 chars.
AGENTS.mdmay grow by at most +20% per proposal. Want more? Split proposals. - Trajectory-backed or drop it. Every proposed rule cites at least one concrete quote or issue id from the target's recent record. No evidence, no rule.
- Not your code. Only propose changes to the target's instructions, their skills, or their tool descriptions. Never to code they do not own or to shared infra.
- Benchmark-gated. Name the replay cases the proposal must still resolve. If a rule would have broken a past success, drop it.
- No reflection on yourself. If
targetAgentId == PAPERCLIP_AGENT_ID, refuse and ask for another coach.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 203 lines · 38 tokens per session scan A 083d5fa8dc43
reflection-coach is a skill published in the GitHub repository paperclipai/paperclip (106,591 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 2,828 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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