aithy: Skill for Claude Code

AxAgent RLM Runtime Rules (@ax-llm/ax)

Use this skill for code-runtime agents and llmQuery(...) semantic-helper behavior. For ordinary agent setup, child agents, tool namespaces, clarification, and bubbleErrors, use ax-agent. For callbacks and logs, use ax-agent-observability. For memories and skill loading, use ax-agent-memory-skills.

Use These Defaults

• Use agent(...), not new AxAgent(...).
• In stdout-mode RLM, use one observable console.log(...) step per non-final actor turn.
• Rely on autoUpgrade (ON by default) for oversized inputs you did not declare in contextFields: any input value over ~8k serialized chars is kept runtime-only automatically, with a 1,200-char prompt preview plus a contextMetadata line, while the full value stays live in the runtime as inputs.<field>. Declare a field in contextFields only when you want a specific inline policy (promptMaxChars / keepInPromptChars) or need a large required non-string field kept out of the prompt (those are left inline by auto-upgrade).
• Default to contextPolicy: { preset: 'checkpointed', budget: 'balanced' } for most RLM tasks.
• Prefer contextPolicy: { preset: 'adaptive', budget: 'balanced' } when older successful turns should collapse sooner while live runtime state stays visible.
• Use contextMap for recurring long-context corpora when the distiller should start future runs with a small persisted orientation cache.
• Prefer promptLevel: 'default' for normal use.
• Use promptLevel: 'detailed' when you want extra anti-pattern examples and tighter teaching scaffolding in the actor prompt.
• Prefer executorModelPolicy when the actor may need to upgrade after repeated error turns or discovery in specific namespaces without also upgrading the responder.
• Use explicit child agents in functions: [...] when the task needs specialist agents with their own tools/runtime.
• Use llmQuery(...) only for focused semantic questions over narrowed context; it does not spawn a tool-using child AxAgent.
• Prefer maxSubAgentCalls only when you need an explicit cap on llmQuery(...) sub-query usage.