ax-python-gen

A reference for using axllm in Python to generate structured results from language models. It covers typed outputs, tools, multiple samples, streaming, validation, traces, usage, and parsing.

In plain words
What is it for?
Use it to define generation signatures, call Ax programs, attach tools, validate outputs, compare several generated results, and process responses.
Why use it?
It helps developers build generation calls that return checked, structured data instead of handling unstructured text manually.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ax-llm/ax/ax-python-gen
Any agent
npx skills add ax-llm/ax --skill ax-python-gen
Clone the repo
git clone --depth 1 https://github.com/ax-llm/ax

Made for: Claude Code, Codex.

Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 734 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00046 $0.00734
Opus 5 $0.00023 $0.00367
Sonnet 5 $0.00009 $0.00147
Haiku 4.5 $0.00005 $0.00073

Measured today against content hash ddf154261579, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ax-python-gen scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

packages/python/skills/ax-python-gen/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AxGen Structured Generation For Python

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

  • Build a structured generation program from a signature.
  • Attach typed tools or MCP-derived tools to a generation call.
  • Generate multiple validated structured samples and select a winner with a native callback.
  • Use package examples for no-key scripted clients and provider-api calls.

Package Facts

  • Language: Python.
  • Package: axllm.
  • Package API docs: API.md and axir-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

program = ax("question:string -> answer:string")
out = program.forward(llm, {"question": "What is Ax?"})

Provider Forward Options

AxGen merges constructor and per-call forward options before invoking the provider. Provider-facing keys such as promptCacheKey, sessionId, and contextCache therefore reach the chat request without being copied into program inputs. Per-call values override constructor defaults.

structuredOutputMode / structured_output_mode accepts auto, native, function, or json_object. Auto follows the selected profile/model ordering, with the provider-neutral singleton string/code JSON-object optimization. Explicit modes must be advertised and fail before transport otherwise. JSON-object mode retains exact-shape prompting, strict parsing, and one bounded correction retry without a synthetic __axOutput tool.

Multi-Sampling

  • Set sampleCount / sample_count to request N provider candidates. Core parses and validates every candidate, preserving each provider result index.
  • Without a result picker, AxGen returns candidate 0. A result picker receives all { index, sample } structured candidates and returns the winning list index; Core rejects an index outside 0..N-1.
  • Native callback surface: ax(..., sample_count=N, result_picker=callback) or set_sample_count / set_result_picker.
  • OpenAI-compatible Chat and Gemini map multi-sampling to n and candidateCount. Anthropic rejects n > 1 explicitly.

Read the full file on GitHub · 62 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed ddf154261579
  2. 2d ago First seen · 62 lines · 46 tokens per session scan A 879fc536d77a

Subscribe to this mod's changes

ax-python-gen is a skill published in the GitHub repository ax-llm/ax (2,890 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 734 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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