Playbook Codegen Rules (@ax-llm/ax)

Use this skill to generate context-playbook code. A playbook grows an evolving body of task knowledge and renders it into a program's context. The evolution engine (ACE — Agentic Context Engineering) is hidden behind playbook(...), exactly as optimize(...) hides its optimizer. Prefer the playbook(...) concept; only reach for AxACE directly when the user explicitly wants the low-level engine.

Use These Defaults

• Create with playbook(program, { studentAI, teacherAI? }); it returns an AxPlaybook handle.
• Grow offline with await pb.evolve(examples, metric) — returns { bestScore, playbook }.
• Grow online with await pb.update({ example, prediction, feedback }) — no metric needed.
• Apply with pb.applyTo(program) (defaults to the bound program).
• Persist with pb.toJSON() and restore with playbook(program, opts).load(snapshot).
• Inspect with pb.render() (markdown) and pb.getState() ({ playbook, artifact }).
• For agents use agent.playbook({ target: 'actor' | 'responder' }); default target is 'actor'.
• Use a cheaper studentAI to run the program and an optional stronger teacherAI to reflect/curate.
• Prefer ai(), ax(), and agent() for new code.

Critical Rules