ax-playbook

name	ax-playbook
description	This skill helps an LLM generate correct playbook code using @ax-llm/ax. Use when the user asks about playbook(), AxPlaybook, context playbooks, evolving context, ACE / Agentic Context Engineering, agent.playbook(), or growing/applying task knowledge offline and online with evolve() and update().
version	23.0.14

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

• playbook(...) binds to an AxGen program; evolve/update need that program's signature.
• evolve() returns only { bestScore, playbook }. There is no Pareto front and no optimizedProgram — that is optimize(...)'s shape, not a playbook's.
• update({ example, prediction, feedback }) requires the full { example, prediction }; example must match the program's input fields (plus any expected output). Do not pass bare input fields at the top level.
• update() works without a prior evolve()/load() — the handle hydrates lazily on first use.
• applyTo() injects a ## Context Playbook block into the program description; calling it repeatedly recomposes from the original base (no stacking).
• Keep the offline metric deterministic and cheap, like a GEPA metric.
• A playbook is plain JSON. Persist pb.toJSON() and load(...) it into a fresh program for production.
• The playbook engine, construction-time agent attachment, failure harvesting, and verified agent evolution are available in TypeScript and the generated Python, Java, C++, Go, and Rust packages. Use each package's native casing and callback types.

Offline Pattern (evolve)

import { type AxMetricFn, ai, ax, playbook } from '@ax-llm/ax';

const program = ax('review:string -> sentiment:class "positive, negative"');
const studentAI = ai({ name: 'openai', apiKey: process.env.OPENAI_APIKEY! });
const metric: AxMetricFn = ({ prediction, example }) =>
  (prediction as any).sentiment === (example as any).sentiment ? 1 : 0;

const pb = playbook(program, { studentAI, maxEpochs: 2 });
const { bestScore } = await pb.evolve(train, metric);
pb.applyTo(program);

Online Pattern (update)

// After a real run, feed the outcome back so the playbook keeps learning.
await pb.update({
  example: { review: 'Five stars, would buy again.' },
  prediction: { sentiment: 'negative' },
  feedback: 'WRONG: enthusiastic praise is positive.',
});
pb.applyTo(program);

Persist And Restore

const snapshot = pb.toJSON(); // { playbook, artifact } — plain JSON
// later, in another process / a production program instance:
playbook(prodProgram, { studentAI }).load(snapshot).applyTo(prodProgram);

Agents

a.playbook({ target }) returns an agent-aware AxAgentPlaybook (the stage AxPlaybook handle plus an agent-level evolve). The one playbook the agent renders into its prompt grows three ways:

• Continuous (trust): the construction-time playbook option (see ax-agent) harvests each run's failures automatically — no dataset.
• On-demand (trust): apb.update({ example, prediction, feedback }).
• Batch verified (proof): apb.evolve(dataset, options) runs the full agent over a task set, mines failure clusters, and proposes one playbook bullet per weakness; with verify (default on) it keeps a bullet only if held-in improves AND the validation held-out set does not regress, else exact rollback. verify: false = trust-batch. Bullets-only.

const a = agent('ticket:string -> reply:string', { ai });
const apb = a.playbook({ target: 'actor' }); // agent-aware handle; 'actor' (default) or 'responder'
await apb.update({ example, prediction, feedback }); // online: injected into the live stage prompt
const result = await apb.evolve(
  { train, validation }, // AxAgentEvalDataset
  { metric, runsPerTask: 2 }, // verify:true by default
);

The agent-level evolve(dataset, options) is distinct from the program-level pb.evolve(examples, metric) above: it takes an AxAgentEvalDataset plus options, runs the whole pipeline, and returns baseline/final held-in & held-out with per-bullet outcomes (no { bestScore }). For full-pipeline tuning of agent instructions and demos (not the playbook) use agent.optimize(...) (GEPA).

Generated packages expose that same agent-bound loop with language-shaped APIs:

Language	Agent-bound evolve call
Python	agent.playbook().evolve(dataset, options)
Java	agent.playbook(null).evolve(dataset, options)
C++	agent.get_playbook()->evolve(dataset, options)
Go	agent.GetPlaybook().EvolveAgent(ctx, dataset, options)
Rust	playbook.evolve_agent(&mut agent, client, dataset, options)

All five generated packages thread structured failureSignals through agent evaluation predictions. The default verify gate accepts a proposed bullet only when held-in score improves and held-out score stays within epsilon; rejection restores the exact prior snapshot. Scoring is host-shaped: TypeScript uses its metric, Python/Java/Go can accept a metric callback, and all generated ports can use task score/scores values plus the agent evaluation result.

Playbook vs optimize()

• playbook(...) — accumulate reusable, evolving task knowledge; the only path that also learns online via update(...).
• optimize(...) / agent.optimize(...) — tune instruction text and few-shot demos offline to a best/Pareto result.
• They are complementary; a project can use both.

Troubleshooting

• "Cannot convert undefined or null to object" from update() → you passed input fields at the top level; wrap them in example: { ... }.
• Empty playbook after evolve() → the model already scored well, so nothing was curated; use harder/ambiguous examples or a weaker studentAI to surface lessons.
• Playbook not affecting an agent's behavior → ensure apply is not false and you used agent.playbook(...) (not a bare playbook() on an internal program).

See Also

• ax-gepa - optimize(...) and AxGEPA for instruction/demo tuning.
• ax-agent-context - choosing between contextMap, contextPolicy, agent.playbook(...), and recall.
• ax-agent-optimize - agent.optimize(...) GEPA tuning for agents.