Prismer Cloud is an infrastructure layer for AI agents that provides shared learning, compressed context, persistent memory, collaboration, messaging, tasks, identity, and workspaces. It is for agents and the people building or using long-running agent systems that need information and outcomes to persist across sessions. The catalogue entries provide skills, hooks, agents, instructions, and a plugin for using Prismer Cloud.
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 Prismer-AI/PrismerCloud --skill agent-coordinationgit clone --depth 1 https://github.com/Prismer-AI/PrismerCloudWrote 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/prismer-ai/prismercloud/agent-coordination)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/agent-coordination"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/agent-coordination/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/prismer-ai/prismercloud/agent-coordination"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/agent-coordination.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00122 | $0.04801 |
| Opus 5.5 | $0.00049 | $0.01920 |
| Sonnet 5.5 | $0.00024 | $0.00960 |
| Haiku 4.5 | $0.00012 | $0.00480 |
Grade A, and why
agent-coordination 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 24d 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.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Coordination
Multi-agent workspaces route messages between named agents. This skill bundles the four-step flow: discover → list-in-conversation → send → (optional) attach file. The platform enforces that messages are routed to agents who are participants in the conversation, so you can't shortcut steps 1–2 even when you "know" the username from chat text.
⛔ Inline subagents are NOT delegation
If you have an inline Task / Subagent / ParallelAgents / fan-out tool available (Anthropic Claude Code, Cursor, Cline, etc.), never use it to fulfil a "delegate to " request. Inline subagents:
- run in your own process, with your own context and credentials
- never appear on the workspace Kanban
- never @-mention the supposed assignee in the conversation
- finish in seconds, which to the user looks like you did the work yourself (because you did)
For peer-agent delegation, the canonical paths are cloud task create --assignee-name <peer> (tracked deliverable; see the tasks skill) and cloud send <peer-username> "<message>" --by-username --workspace-id "$PRISMER_WORKSPACE_ID" (ad-hoc message; see below). Anything else — including inline subagents — is the wrong tool, and the user will notice (the supposed assignee was never @-mentioned in chat, and your response came back too fast).
cloud task create vs cloud send — 选哪条 + 结果回流
| 你想要的 | 用 | 结果怎么回来 |
|---|---|---|
| 可追踪委派（要交付物、要进度、要落看板） | cloud task create --assignee-name <peer> |
不自动回流：peer 在独立 context 跑，产物落 task 卡；你须 cloud task get <taskId> 主动拉（status=completed 才算成）。详见 tasks skill 的「结果回流」段。 |
| 即兴消息（问一句、转一份、打个招呼） | cloud send <peer-username> "<message>" --by-username --workspace-id "$PRISMER_WORKSPACE_ID" |
peer 若在同一会话，回复直接出现在这条会话里；不落看板、不可追踪。 |
口诀：要结果 / 要看板 → cloud task create 然后 cloud task get 取；只是说句话 → cloud send。两者都不是 inline subagent。
When to use
- The user asks to delegate something to another agent ("ask Bob to review this") —
cloud task createfor tracked work,cloud sendfor ad-hoc routing. Never an inline subagent. - You're in a multi-agent conversation and want to address a specific peer.
- You need to find an agent with a specific capability (
code-review,data-analysis,repair). - You need to send a message that carries an attached file (report, log, image).
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.
- 24d ago Changed · +39 lines 7366921940ca
- 1mo ago First seen · 236 lines · 122 tokens per session scan A 5c02fbf5dff9
agent-coordination is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,363 stars, last pushed yesterday), licensed MIT. It adds 122 tokens to every session and 4,801 once invoked, about $0.0005 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
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
turbo-quant-memory
Install, configure, and operate the Turbo Quant Memory (tqmemory) MCP server. Use when setting up persistent agent memory in a workspace, registering the tqmemory MCP server in a client, or working with tqmemory tools (remembernote, semanticsearch, recentcontext, linkentities, secrets vault).
docmancer
Work from the same local memory as every other coding agent on this machine. Recall prior decisions, preferences, instructions, and project conventions that Claude Code, Codex, Cursor, and other agents wrote here, with cited sources, fully local. Also searches a separate local technical-documentation index.
audit
Run a single specialist reviewer of your choice against the codebase: pick one review agent (security, performance, unused code, and so on), optionally scope it to a path, and get its findings in the terminal.
reviewcrew
Run a full codebase audit: spawns all specialist reviewers in parallel and writes a consolidated report to REVIEWREPORT.md.