Ax Playbook For Python
Use when writing Python code with axllm for the playbook() context-engineering surface, evolving task knowledge, online updates, and rendering a playbook into a program.
Install
Install only this skill for Python:
npx skills add https://ax-llm.github.io/ax/python/ --skill 'ax-python-playbook'Published skill file: ax-python-playbook/SKILL.md.
Source
- Source: packages/python/skills/ax-python-playbook/SKILL.md
- Version:
22.0.9
Skill Instructions
This skill helps an agent write Python code with the generated Ax package axllm. Use the generated package API, examples, and manifests; do not import TypeScript-only APIs unless you are editing the TypeScript package.
When To Use
- Grow an evolving context playbook for a program or agent stage with playbook().
- Refine a playbook online from live feedback or offline from labeled examples.
- Render or persist a playbook and inject it into a program context.
Package Facts
- Language: Python.
- Package:
axllm. - Package API docs:
API.mdandaxir-api.json. - Capability manifest:
axir-capabilities.json. - Runnable examples:
examples/. - Real network support: yes.
- Scripted no-key transport support: yes.
- Runtime profiles:
javascript-quickjs,python-pyodide.
Core Pattern
from axllm import ax, playbook
program = ax("question:string -> answer:string")
pb = playbook(program, {"studentAI": llm})
pb.evolve(examples, metric_fn)Relevant API Surface
- Optimizers:
optimize,AxBootstrapFewShot,AxGEPA,OptimizerEngine,OptimizerEvaluator
Guardrails
- Start from package examples for exact native syntax before inventing a new call shape.
- Use
provider-apiexamples only when the user explicitly has provider credentials available. - Use
no-keyexamples for deterministic local checks and provider request mapping. - Treat AxIR as the source of generated package truth: if package docs disagree with source code, update the compiler and regenerate packages.
- Do not copy repo-maintainer skills from
tools/*/skills/into user packages.