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 agentmods add skills/prismer-ai/prismercloud/tasksnpx skills add Prismer-AI/PrismerCloud --skill tasksgit 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/tasks)<a href="https://agentmods.dev/skills/prismer-ai/prismercloud/tasks"><img src="https://agentmods.dev/badge/skills/prismer-ai/prismercloud/tasks.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00068 | $0.05625 |
| Opus 5 | $0.00034 | $0.02812 |
| Sonnet 5 | $0.00014 | $0.01125 |
| Haiku 4.5 | $0.00007 | $0.00562 |
Grade A, and why
tasks 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 5d 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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tasks
Use this skill to drive the workspace Kanban end-to-end. Tasks are durable board items with owner, priority, schedule, and conversation linkage. Every operation goes through the cloud task CLI — never reply that a task was created/moved/completed unless the command actually returned a task ID and status.
⛔ Hard rules — delegation discipline
When the user asks you to assign / delegate / hand off work to ANOTHER agent (e.g. "create kanban tasks and assign to @research-agent", "give this to Bob"):
- Call
cloud task create --assignee-name <agent>(or--assignee-id). That is the ONLY legitimate delegation path. The cloud routes the task to the target agent's daemon, and the target agent picks it up via its own dispatch loop. - STOP as soon as
cloud task createreturns task IDs. Reply to the user with the IDs / titles. Do not invoke any other tool to "also do the research yourself" or "make sure it gets done". - NEVER use a generic
Task/ subagent / fan-out / parallel-agent tool to silently do the work in-process. That bypasses the kanban board, the assignee never sees the card, the user's mental model ("an agent is working on this") is violated, and the delegation is fake. If you find yourself reaching for any tool whose name is "Task", "Subagent", "Worker", "Fanout", "ParallelAgents", or anything else that spawns an inline executor — stop and re-read this section. - The phrase "I've also started working on these" after a
cloud task createis a red flag that you violated rule 3. The correct phrase is "Tasks created and assigned. @research-agent will pick up the cards."
This is non-negotiable. Bypassing it makes the agent ecosystem look broken even when the cards are correct, because the user sees the answer come back too fast and notices that the supposed assignee was never @-mentioned in the conversation.
When to use
- The user asks to create, assign, schedule, or track work ("add to kanban", "give Bob this task", "remind me to ship X by Friday").
- You need to delegate a concrete deliverable to another agent. (See ⛔ rules above.)
- A long-running objective should persist as a workspace goal (
kind=goal). - The user asks about board state ("what's pending", "show me Bob's tasks", "what's blocking the release").
- A task already on the board needs to be updated (priority change, retitle, attach context), completed, approved/rejected in review, or cancelled.
- The task output is a document / deck / spreadsheet / PDF / report. In that
case use the
office-artifactsskill before completion; a prose-only result is not enough for file-deliverable tasks.
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.
- 5d ago First seen · 399 lines · 68 tokens per session scan A d7a297f424d2
tasks is a skill published in the GitHub repository Prismer-AI/PrismerCloud (1,410 stars, last pushed 29d ago), licensed MIT. It adds 68 tokens to every session and 5,625 once invoked, about $0.0003 per session on Opus 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.
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.
agent-prompts-warmup
Audit and sync agent instruction files across all coding agent formats. FRE (first-run) checks scaffolding completeness; ongoing use keeps files in sync after edits.
memex-sync
Sync Zettelkasten cards across devices via git.
setup-bot
Diagnose and fix Telegram bot connection issues -- verify config, test send, resolve common errors.