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 agentmods add skills/ax-llm/ax/ax-python-agent-rlmnpx skills add ax-llm/ax --skill ax-python-agent-rlmgit clone --depth 1 https://github.com/ax-llm/axWhat 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 | $0.00041 | $0.00441 |
| Opus 5 | $0.00020 | $0.00220 |
| Sonnet 5 | $0.00008 | $0.00088 |
| Haiku 4.5 | $0.00004 | $0.00044 |
Grade A, and why
ax-python-agent-rlm 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.
This is a copy
89% identical to ax-cpp-agent-rlm — 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 RLM Runtime 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
- Wire an AxCodeRuntime or AxCodeSession implementation.
- Use ProcessCodeRuntime or an optional runtime profile for actor-code sessions.
- Explain that generated packages are not TypeScript transpilers; they adapt the Ax runtime contract.
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 agent
helper = agent("question:string -> answer:string")
out = helper.forward(llm, {"question": "How should I proceed?"})
Relevant API Surface
- Agents And RLM:
agent,AxAgent - Runtime Profiles:
ProcessCodeRuntime,RuntimeCapabilities,RuntimeEnvelope,javascript-quickjs,python-pyodide
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.
- today Changed 552728bc5445
- 3d ago First seen · 47 lines · 41 tokens per session scan A 5abd5cd6f544
ax-python-agent-rlm is a skill published in the GitHub repository ax-llm/ax (2,890 stars, last pushed yesterday), licensed Apache-2.0. It adds 41 tokens to every session and 441 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to ax-cpp-agent-rlm, differing in 22 lines, and is treated as a copy.
Other skills, from other repositories
add-harness-package
Guide for adding new AI SDK harness packages. Use when creating a new @ai-sdk/harness- package that adapts a coding-agent runtime to HarnessV1.
cubepi
Use when building, extending, or debugging agents with the CubePi framework. Covers the Agent API, providers, tools, middleware, checkpointing, MCP integration, and HITL. References the cubepi-trace skill for run debugging.
ask-matt
Ask which skill or flow fits your situation. A router over the skills in this repo.
nodetool-chat-cli
Use NodeTool chat CLI commands, interactive terminal, agent mode, workspace management, and Global Chat features. Use when user asks about chat commands, interactive terminal, chat features, agent mode in chat, or the Global Chat interface.
nodetool-browser-agent
Create browser automation agents that navigate websites, extract data, fill forms, and perform multi-step web tasks using natural language instructions. Use when user asks to automate browsing, scrape websites with AI, build a web agent, or perform complex browser interactions.
maintain-model-list
Maintain the supported LLM model list: add a new model, or run routine maintenance to verify availability and discover new models worth adding. Use when the user asks to add/support a model, update the model list, or check model availability.