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/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.

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agentlas-os
1.5k47 skills6 agents45 commands2 MCP

Claude Code

$ npx -y skills add agentlas-ai/Agentlas-OS --skill mode-classification --agent claude-code

SKILL.md

mode-classification.SKILL.md
name: mode-classification
description: "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."

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`.

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An open-source AI agent OS inspired by OpenAI Dots, Meta Muse, Claude Duet, Nous Hermes, and Mem0. Built to lead entire projects rather than isolated prompts. Model-agnostic (BYOM) and native to Claude Code, Codex, Cursor, Antigravity, and Agentlas Desktop.

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Questions about mode-classification.

Which coding agents can use mode-classification?
mode-classification installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.