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 para-memory-filesgit 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/para-memory-files)<a href="https://agentmods.dev/skills/paperclipai/paperclip/para-memory-files"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/para-memory-files/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/para-memory-files"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/para-memory-files.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00048 | $0.00863 |
| Opus 5.5 | $0.00019 | $0.00345 |
| Sonnet 5.5 | $0.00010 | $0.00173 |
| Haiku 4.5 | $0.00005 | $0.00086 |
Grade A, and why
para-memory-files scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- para-memory-files — 89% identical, 18 lines differ
- para-memory-files — 88% identical, 10 lines differ
- para-memory-files — 88% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PARA Memory Files
Persistent, file-based memory organized by Tiago Forte's PARA method. Three layers: a knowledge graph, daily notes, and tacit knowledge. All paths are relative to $AGENT_HOME.
Three Memory Layers
Layer 1: Knowledge Graph ($AGENT_HOME/life/ -- PARA)
Entity-based storage. Each entity gets a folder with two tiers:
summary.md-- quick context, load first.items.yaml-- atomic facts, load on demand.
$AGENT_HOME/life/
projects/ # Active work with clear goals/deadlines
<name>/
summary.md
items.yaml
areas/ # Ongoing responsibilities, no end date
people/<name>/
companies/<name>/
resources/ # Reference material, topics of interest
<topic>/
archives/ # Inactive items from the other three
index.md
PARA rules:
- Projects -- active work with a goal or deadline. Move to archives when complete.
- Areas -- ongoing (people, companies, responsibilities). No end date.
- Resources -- reference material, topics of interest.
- Archives -- inactive items from any category.
Fact rules:
- Save durable facts immediately to
items.yaml. - Weekly: rewrite
summary.mdfrom active facts. - Never delete facts. Supersede instead (
status: superseded, addsuperseded_by). - When an entity goes inactive, move its folder to
$AGENT_HOME/life/archives/.
When to create an entity:
- Mentioned 3+ times, OR
- Direct relationship to the user (family, coworker, partner, client), OR
- Significant project or company in the user's life.
- Otherwise, note it in daily notes.
For the atomic fact YAML schema and memory decay rules, see references/schemas.md.
Layer 2: Daily Notes ($AGENT_HOME/memory/YYYY-MM-DD.md)
Raw timeline of events -- the "when" layer.
- Write continuously during conversations.
- Extract durable facts to Layer 1 during heartbeats.
Layer 3: Tacit Knowledge ($AGENT_HOME/MEMORY.md)
How the user operates -- patterns, preferences, lessons learned.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 101 lines · 48 tokens per session scan A 51e8e591837f
para-memory-files is a skill published in the GitHub repository paperclipai/paperclip (107,044 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 863 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
system-info
Get system information including CPU usage, memory, disk space, battery status, and network info. Read-only and safe to use.
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
agent-memory-coordinator
Agent skill for memory-coordinator - invoke with $agent-memory-coordinator.
agent-swarm-memory-manager
Agent skill for swarm-memory-manager - invoke with $agent-swarm-memory-manager.
agent-collective-intelligence-coordinator
Agent skill for collective-intelligence-coordinator - invoke with $agent-collective-intelligence-coordinator.
memory-management
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous…