Skill
agentlas-core-engine-m…
Use when creating a single Agentlas agent, creating a multi-agent team, or packaging an existing local/external agent into Agentlas architecture. Make sure to…
@agentlas-aiView SkillDevelopment
Skill
/llm-runtime-architecture
Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.
Install
$ npx -y skills add agentlas-ai/Agentlas-OS --skill llm-runtime-architecture --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/llm-runtime-architecture
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.
SKILL.md
llm-runtime-architecture.SKILL.mdname: llm-runtime-architecture description: "Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools."
LLM Runtime Architecture
Procedure
1. Keep `AGENTS.md` as the canonical behavior contract. 2. For each runtime, name entry point, global command, adapter files, available tools, memory access, limitations, and verification command. 3. Keep adapters thin and point them back to the canonical core. 4. Write or repair `.agentlas/global-commands.json` when creating or packaging an agent. 5. State unsupported capabilities explicitly.
Output
Return a runtime matrix with `runtime`, `entry_point`, `global_command`, `adapter_files`, `memory_access`, `limitations`, and `verification`.
Ships withagentlas-os
Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.
Get the whole plugin
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