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Multica Platform

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multica-ai
multica-platform

Use for Multica platform actions the runtime brief does not fully cover: issue and PR contracts, mentions, agents, squads, autopilots, projects, runtimes, skill import. Not for the product code you are working on.

Overview

Publishermultica-ai
Repositorymultica
Skill namemultica-platform
Stars
50.4K
Forks
6.5K
Bundled files
8
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  • 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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by multica-ai on GitHub. Read the source before you install it.

Installation

Install the Multica Platform 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/multica-ai/multica.git /tmp/multica
mkdir -p .claude/skills
cp -r /tmp/multica/server/internal/service/builtin_skills/multica-platform .claude/skills/multica-platform
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multica Platform 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 Multica Platform 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 Multica Platform 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.

Operating Multica

Your runtime brief owns the per-turn workflow: which issue you are on, when to comment, what status to write. This skill owns the platform contracts behind it — what a command actually does, what the server validates, and which writes have consequences you cannot take back.

Read the invariants below, then open the reference(s) your task actually needs — usually one, sometimes a few. Do not read them all.

Routing

OpenWhen the task is about
references/issues.mdIssues: PR linking vs close intent, reading a linked PR's state, custom properties, status side effects, sub-issues and stages, who else is running
references/mentions.mdWriting a mention:// link: which types enqueue a run, which are inert, why one silently did nothing
references/agents.mdCreating, copying or debugging an agent definition: fields, secrets, MCP config, skill binding
references/squads.mdSquads: leader routing, roster, recording leader activity, why a squad did or did not run
references/autopilots.mdAutopilots: schedule / webhook / manual triggers, create_issue vs run_only, why one did not fire
references/projects.mdProjects and their durable resources (github_repo, local_directory, worktree mode)
references/runtimes.mdRuntimes, daemons, repo checkout, and the task CLI boundary
references/skill-import.mdImporting a skill into this workspace from a URL or a local archive

Open what the task needs. A single-domain task usually needs one; a task that crosses domains needs each domain it touches — creating a squad, assigning it an issue, then writing a mention needs squads.md, issues.md and mentions.md, and skipping one of those means acting on a contract you have not read.

What is never right is reading all eight because you are not sure. Each reference states its own contracts in full and none depends on another, so pick by domain and skip the rest.

Invariants

These hold across every reference and are not repeated there.

Read before you write. Start with the read-only commands the reference you opened names — most domains have a list and a get that take --output json and have no side effects. Run those before any mutation. When a command's shape is unclear, multica <command> --help beats guessing at flags.

A name is not an id. Mention links, assignment, and every --*-id flag take a real UUID from the matching list --output json. Never type a display name where an id belongs, and never invent a UUID: an id that is well-formed but belongs to nothing fails in ways that read like a permission error, which sends you debugging access when the real problem was the id.

--output json writes to stdout; warnings and confirmations go to stderr. Do not merge them (2>&1) into anything that parses the output — that makes a write which SUCCEEDED look like it failed, and invites a duplicate retry.

Writes are real. Creating, updating, deleting, assigning, commenting, mentioning, triggering and status changes mutate durable workspace state or start agent runs that cost real budget. Never run one to see what happens. When the user has not asked for a specific mutation, propose it instead of making it.

--no-start when you are only recording. Assignment and status writes normally enqueue a run. When the work is already underway and the write merely records ownership or progress, pass --no-start on EVERY command in that flow — suppressing the assignment alone does not suppress a later status update.

Status keys identify workflow states; categories describe lifecycle only. Custom statuses do not inherit built-in automation behavior. For status side effects and API field meanings, read references/issues.md.

Comment reads stay bounded. Scan the threads cheaply (--roots-only --summary --compact), then expand only what matters (--thread <thread-id> --tail 30). Never one unbounded pull — a wide read on a busy issue costs more than the answer is worth and still buries the reply bodies where triggers and instructions actually live. One exception, and it is narrower than it looks: when the per-turn message hands you a --since delta read, that read is the bounded scan — the server already computed which comments are new, so running it returns exactly those and nothing else.

When behavior looks wrong

Classify before concluding: expected behavior, a configuration problem, a product limitation, or an actual bug. Explain what the platform currently does rather than defending it; when the behavior is technically correct but bad for the user, say so and propose a scoped change.

Do not silently alter routing, briefing, or trigger behavior to make a complaint go away. Those are product contracts, and changing one without confirmation moves the surprise to somebody else.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Multica Platform AI skill do?

Use for Multica platform actions the runtime brief does not fully cover: issue and PR contracts, mentions, agents, squads, autopilots, projects, runtimes, skill import. Not for the product code you are working on.

Why use Multica Platform on TypingMind?

Because you install it once and use it with any model. Multica Platform 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 Multica Platform in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/multica-ai/multica/tree/main/server/internal/service/builtin_skills/multica-platform. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Multica Platform?

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 Multica Platform?

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

Is the Multica Platform AI skill free?

It is published on GitHub by multica-ai. Check the repository for licensing terms. 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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