Ax is a TypeScript-first programming framework for building applications with large language models through typed generation, agents, workflows, and optimization tools. It is intended for developers who want one model for LLM programs across TypeScript, Python, Java, C++, Go, Rust, and other runtimes.
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.
npx skills add ax-llm/ax --skill ax-python-agent-optimizegit clone --depth 1 https://github.com/ax-llm/axWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ax-llm/ax/ax-python-agent-optimize)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-python-agent-optimize"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-python-agent-optimize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ax-llm/ax/ax-python-agent-optimize"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-python-agent-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.00457 |
| Opus 5.5 | $0.00017 | $0.00183 |
| Sonnet 5.5 | $0.00009 | $0.00091 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
ax-python-agent-optimize 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 yesterday.
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.
This is a copy
89% identical to ax-cpp-agent-optimize — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
AxAgent Optimize 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
- Optimize an AxAgent or reusable program component.
- Mine grounded weaknesses from failed agent tasks and keep only playbook proposals that pass the verification gate.
- Create evaluator callbacks and persist optimizer artifacts.
- Keep optimization runs bounded by explicit budgets and dataset rows.
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 AxGEPA
engine = AxGEPA(reflection_client)
result = engine.optimize(request, evaluator)
Relevant API Surface
- Agents And RLM:
agent,AxAgent,AxAgent.add_child_agent,AxAgent.streaming_forward - Optimizers:
optimize,playbook,AxPlaybook,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.
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.
- yesterday Changed eca39a919e87
- 3d ago Changed 7daf7132eacf
- 5d ago Changed ac20c40ed95d
- 8d ago Changed f7576df3788f
- 9d ago Changed c76eb2c606c6
- 10d ago Changed a73ec3591ece
- 16d ago Changed 5b06155f0cc4
- 19d ago Changed ce8d6bffa303
- 21d ago Changed e3e8e11506ef
- 24d ago First seen · 48 lines · 43 tokens per session scan A 2f4c12f83117
ax-python-agent-optimize is a skill published in the GitHub repository ax-llm/ax (2,967 stars, last pushed 2d ago), licensed Apache-2.0. It adds 43 tokens to every session and 457 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to ax-cpp-agent-optimize, differing in 22 lines, and is treated as a copy.
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