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Mode Classification

OrganizationPopular
agentlas-ai
mode-classification

Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

Overview

Publisheragentlas-ai
RepositoryAgentlas-OS
Skill namemode-classification
Stars
1.6K
Forks
111
Bundled files
Instructions only
LicenseApache-2.0
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 agentlas-ai on GitHub. Read the source before you install it.

Installation

Install the Mode Classification 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.
    https://github.com/agentlas-ai/Agentlas-OS/tree/main/.agents/skills/mode-classification
  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/agentlas-ai/Agentlas-OS.git /tmp/Agentlas-OS
mkdir -p .claude/skills
cp -r /tmp/Agentlas-OS/.agents/skills/mode-classification .claude/skills/mode-classification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mode Classification 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 Mode Classification 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 Mode Classification 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.

Mode Classification

Pick one Agentlas meta-agent mode before generating or repairing files.

Procedure

  1. Inspect the user request and any provided path, repo, ZIP, prompt, or agent files.
  2. Step 0 - existing material wins: if existing material is being converted, repaired, cleaned, imported, or released, choose agentlas-packager.
  3. Step 1 - count independent ownership boundaries. Ask how many roles must independently own all three of:
    • their own memory/context;
    • their own tools/permissions;
    • their own success criteria. One boundary means single-agent-creator. Two or more boundaries means a team-builder candidate. If the boundary count is unclear, run the clarify question loop before generating; do not infer from the word "team" alone.
  4. Step 2 - check synthesis need for multi-boundary candidates. If those role outputs must be routed, reviewed, synthesized, or chained through produces/consumes dependencies, choose team-builder and require an orchestrator/HQ plus memory, policy, eval, and QA. If the roles are unrelated, create separate single-agent packages instead of one team.
  5. Step 3 - shape guard. single-agent-creator may have many skills/tools but must not emit multiple loose worker agent.md files. team-builder may be small, but it must not omit the orchestrator/HQ.
  6. Use keyword signals only as hints after the ownership-boundary check:
    • MULTI hints: separate memory partitions, tools or permissions that must not be merged, role-to-role review/policy separation, and produces/consumes pipelines.
    • SINGLE hints: one coherent job, many tools/skills owned by one worker, no routing or final synthesis requirement.
  7. Overlay check: if the request depends on knowledge search over user documents, evidence-based or citation-attached generation, or a document corpus (HWPX/docx/pdf/제안서/계약서/견적서), additionally apply the ontology-backed-agent overlay (modes/ontology-backed-agent.md) with ontology_backed: true on the chosen base mode.
  8. Loop policy: derive loop_policy from task purpose and risk using .agentlas/contract-injection-map.json risk tiers — none for simple one-shot tasks, self-correct for complex or long-running work, verified (separate-context verifier + side-effect gate) when the agent performs external writes or sends. Do not force loops onto simple tasks.
  9. If the choice changes the output and the request is ambiguous, run the clarify question loop instead of guessing.

Return

Return the selected mode, whether the ontology-backed-agent overlay applies, the derived loop_policy, and one short reason. Then route to the matching builder.

Reference

See docs/mode-classifier.md.

Frequently asked questions

What does the Mode Classification AI skill do?

Use before routing a /meta-agent request to choose single-agent-creator, team-builder, or agentlas-packager from the user's wording and available files.

Why use Mode Classification on TypingMind?

Because you install it once and use it with any model. Mode Classification 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 Mode Classification in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentlas-ai/Agentlas-OS/tree/main/.agents/skills/mode-classification. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mode Classification?

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 Mode Classification?

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

Is the Mode Classification AI skill free?

Yes. It is published on GitHub by agentlas-ai under the Apache-2.0 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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