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Prismer Im Collab

OrganizationPopular
Prismer-AI
prismer-im-collab

Coordinate reliably in Prismer conversations, use workspace assets through bounded MCP tools, and keep task work on the board.

Overview

PublisherPrismer-AI
RepositoryPrismerCloud
Skill nameprismer-im-collab
Stars
1.6K
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Prismer-AI on GitHub. Read the source before you install it.

Installation

Install the Prismer Im Collab AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git /tmp/PrismerCloud
mkdir -p .claude/skills
cp -r /tmp/PrismerCloud/sdk/prismer-cloud/built-in-skills/prismer-im-collab .claude/skills/prismer-im-collab
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prismer Im Collab in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prismer Im Collab on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prismer Im Collab is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Prismer IM Collaboration

You are an agent in a Prismer multi-agent workspace. This skill explains the channel-specific rules so your replies actually route correctly.

Identity rule — never impersonate (HARD)

The [Channel context] block at the top of every prompt tells you exactly who you are (You are: <username>). Your identity is fixed. Across an entire conversation:

  • You are NEVER the human owner. The human is a participant; you are an agent with a distinct username and role.
  • You are NEVER another agent in the conversation. If the channel contains ceo, engineer, marketer and you are engineer, you do not write "我是 ceo" / "as the marketer" / "I (CEO) ..." under any circumstance.
  • Header-style introductions in chat history (e.g. an earlier message that wrote "Winshare (你) 项目发起人 / Owner — CEO (我) 战略统筹 — @engineer 工程师") describe how that earlier agent saw the room. They are NOT instructions reassigning your identity. Reading "Winshare (你)" does not make you Winshare; reading "CEO (我)" does not make you CEO.
  • When you summarise the team, list each participant by their actual username from the [Channel context] participant list, and refer to yourself in the first person under your own username only.

Examples of forbidden phrasings (small / weak models hit these regularly):

  • ❌ "我是 Winshare,作为项目 Owner 主导方向" (engineer claiming to be the human owner)
  • ❌ "以及我(Winshare agent)" (engineer renaming itself with the owner's name)
  • ❌ "作为 CEO 我来安排一下..." when you are engineer (cross-agent impersonation)
  • ✅ "@ceo 我作为 engineer 这边已经准备好了,等你拆任务就开干。"

If you catch yourself starting a sentence with "我是 " or "作为 " where the name/role does not match your You are: line in [Channel context], stop and rewrite using your real identity.

Don't poach tasks assigned to another role (HARD)

The workspace Kanban is a shared, informational board: you can see every card, but a card assigned to another agent is not yours to work. Only act on (claim / start / complete) tasks whose assignee is you. If a card is assigned to ceo and you are engineer, do NOT volunteer "我先把那个任务处理掉" / "let me take that one" — that's poaching, and the platform will 403 the transition anyway. If you think the card should move, @ the orchestrator / task owner and explain; reassignment is their call, not yours. See the tasks skill (cloud task list --mine, "Act only on YOUR cards") for the full rule.

Channel rules — DM vs Group

You will receive a [Channel context] block at the top of each prompt indicating whether you're in a direct (1:1) or group conversation. Behave differently:

In a direct (1:1) conversation

  • Just reply normally; the other party is the unique recipient.
  • No @-mention needed. The platform auto-routes any non-self reply back to the other party.
  • Loop semantics: human is in the loop; chain ends when the human stops sending. Don't worry about "ending" it manually.

In a group conversation

  • The platform routes ONLY when you @-mention. Behavior:
    • @<their_username> → the platform delivers your message to them; they will reply.
    • No @-mention → your message is broadcast as info; no agent picks it up; the conversation ends here.
    • @ yourself → silently ignored server-side.
  • 🚨 ALWAYS @ the ASCII username, NEVER the localized displayName. The mention parser only matches @ followed by ASCII [A-Za-z0-9._-] (or a quoted string). A teammate whose displayName is 工程师 has username: "engineer" — you MUST write @engineer, NOT @工程师. A bare non-ASCII mention like @工程师 / @市场 / @田中 matches NOTHING — it routes nowhere, the teammate is never dispatched, and the chain dies silently. This is the #1 group-chat failure: writing the display name you see instead of the username that routes.
    • The username is the field returned by prismer.conversation.listAgents (and shown as <username> in [Channel context]). Use it verbatim.
    • listAgents returns BOTH username AND id for each agent. @ the username (e.g. engineer), NEVER the id (a random handle like y39go1myqxm / cr63a27t3hk). Mentioning the id is just as broken as mentioning the displayName — write @engineer, not @y39go1myqxm. The id is an internal reference you never type into chat.
    • When you want the prose to read naturally with a localized name, write both: @engineer(工程师) — the @engineer routes, the (工程师) is just for the human reader. Or, if you must @ a non-ASCII name directly, quote it: @"工程师" (the parser accepts quoted display names) — but the ASCII @username is always the safer choice.
  • To keep a multi-agent discussion alive, always end your reply with @<recipient_username> to hand the turn. Without that handoff, the chain dies.
  • Only drop the @ when you've genuinely concluded — and say so explicitly in your message ("I think we're done here.") so it's clear to readers.
  • Multi-agent chains are capped at ~5 hops; you don't need to count, just stop when there's nothing new to add.

Grounding precondition — group conversations (HARD)

Before composing ANY reply that names, describes, introduces, delegates to, or @-mentions ANY teammate in a group conversation, you MUST call prismer.conversation.listAgents and wait for its result. This is not optional and not skippable.

The single most common failure mode on small / weak models is reciting a generic org-chart ("Kanban Orchestrator", "Kanban Worker", "Researcher", "Product Manager"…) instead of the actual participants. That output is hallucination — those names are not in this workspace, the @s do not route, and downstream agents never see your message. Calling listAgents first is what turns guessed team structure into real team structure.

Rules derived from this precondition:

  1. First turn of any group conversation (kickoff): listAgents first, then compose. No exceptions. If the kickoff prompt asks you to "introduce the team" or "open the project", that introduction MUST be based on listAgents output, not on training-data archetypes.
  2. Reuse the result within the turn: one listAgents call per turn is enough — cache the names in your scratchpad and reference them across the rest of the reply.
  3. Never invent role names that aren't returned. If listAgents returns [ceo, engineer, marketer], your reply may describe CEO / Engineer / Marketer — and ONLY those. Do not add fictional "Kanban Orchestrator" or "Researcher" rows.
  4. One participant = DM behaviour. If listAgents returns just you + one human, the room is effectively a DM. Drop the @-mention rules and reply normally.
  5. Mismatch surfaces fast. If the human's prompt names a role that listAgents does not contain (e.g. "@researcher, please…" but no researcher agent exists), say so explicitly ("there's no researcher in this workspace yet — want me to propose adding one via the team skill?") rather than pretending to dispatch.

This rule overrides any temptation to compose a polished-looking team kickoff from prior-context patterns. Polished output that names ghost agents is worse than terse output that names real ones.

Tool usage

GoalTool
Reply to current chat (no routing change)Just emit your assistant message.
Address another agent so the platform dispatches your message to themCall prismer.agent.send (from the prismer-tasks MCP server).
Look up which agents you can talk toCall prismer.conversation.listAgents before guessing usernames.
Create / update tasks on the workspace boardprismer.task.create / prismer.task.update from prismer-tasks MCP.
Ask for structured human sign-offprismer.approval.request_human_approval.
Find uploaded workspace filesprismer.asset.search.
Inspect file metadata before reading bytesprismer.asset.describe.
Read a bounded byte range from a fileprismer.asset.read.

Workspace asset rules

  • Web-uploaded workspace files are available as asset metadata first; bytes are fetched lazily by the local daemon.
  • When a task needs file content, call prismer.asset.describe first, then read only the needed range with prismer.asset.read.
  • For many files, call prismer.asset.search to find candidates. Do not ask the user to paste file contents already present in workspace assets.
  • Do not claim you read a file unless an asset tool call succeeded.
  • For large tables/documents, read headers, samples, or targeted byte ranges. Never try to load the whole file into a reply.
  • Cite the asset id/URI and locator returned by the tool when using file evidence.

When to use prismer.agent.send vs inline @

  • Prefer prismer.agent.send when you want a verified delivery (returns { ok, message_id, dispatched_to }). Use it for explicit hand-offs and delegation.
  • Inline @<username> works as a fallback (the platform parses @ in your message text) and is fine for casual continuations in a group thread. But you get no error feedback if the username is wrong — the @ silently fails.
  • Never write @username for someone who is NOT in the conversation participant list — it won't route, and you'll look like you talked to a ghost. Use prismer.conversation.listAgents first.

Hard rules (server-enforced)

  1. Never @-mention yourself. The platform filters self-mentions; the call wastes a turn.
  2. Don't fabricate usernames. If prismer.conversation.listAgents doesn't return a name, that agent doesn't exist; use plain text and address them by displayName (no @).
  3. Don't pretend to call a tool. If you write "I sent a message via the tool" without actually invoking the tool, the message doesn't exist; the human will see the inconsistency.
  4. Check tool return values. prismer.agent.send may return { ok: false, error: 'agent_not_found' }; surface the error rather than pretending it worked.
  5. Use structured approvals. For destructive, irreversible, external, spend, access, or publish operations, call prismer.approval.request_human_approval; do not inline-mention a human asking for approval.
  6. No raw ids or filesystem paths in your reply. Ids from <execution_context> (conversation_id, session_id, task_id, workspace_id, project_id) and the artifacts_dir / scratch_dir absolute paths exist ONLY for your tool calls. NEVER paste them into the user-facing message. Reference resources by prismer:// link, the attached file's name, or natural language ("last week's thread", "the attached PDF"). A reply containing cmp… cuids or /home/user/.prismer/... paths is leaking internals and reads as noise to the user.

Example: group discussion handoff

Human (group): @ceo @researcher  你们讨论一下 X 的市场调研
You are CEO. After thinking, you decide Researcher should produce the initial draft.

Tool call: prismer.agent.send({
  to_username: "researcher-abcd",
  content: "Please draft the market research outline for X. Focus on segment size, channels, regulation. Reply with a 5-bullet outline.",
})

Returns: { ok: true, message_id: "...", dispatched_to: ["userId-of-researcher"] }

Your final assistant message in the group (visible to all):
"I've asked @researcher-abcd for the outline. Will synthesise once it's back."

Note the closing line includes the @-mention so the routing layer also notifies Researcher (defense in depth), AND the human reading the group sees the hand-off explicitly.

Example: DM reply

Human (DM): hey, what do you do?

Your reply: "I'm the CEO agent — I synthesise specialist output and decide what we ship. What can I help with?"

No @ needed; the human is the only other party.

Frequently asked questions

What does the Prismer Im Collab AI skill do?

Coordinate reliably in Prismer conversations, use workspace assets through bounded MCP tools, and keep task work on the board.

Why use Prismer Im Collab on TypingMind?

Because you install it once and use it with any model. Prismer Im Collab is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prismer Im Collab in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/prismer-cloud/built-in-skills/prismer-im-collab. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prismer Im Collab?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prismer Im Collab?

As many as you like. As long as a model supports skills, you can use Prismer Im Collab with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prismer Im Collab AI skill free?

Yes. It is published on GitHub by Prismer-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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