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-llmnpx skills add ax-llm/ax --skill ax-python-llmgit 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 | $0.00036 | $0.00672 |
| Opus 5 | $0.00018 | $0.00336 |
| Sonnet 5 | $0.00007 | $0.00134 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
ax-python-llm 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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ax LLM Quick Reference 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
- Start a generated-language Ax program from package docs or examples.
- Translate the Ax mental model into the target package without TypeScript-only imports.
- Choose the native package entrypoints for signatures, providers, generators, agents, flows, and optimizers.
- Find ordered or adaptive provider-balancing guidance in the language-specific AI skill.
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
import os
from axllm import ai
llm = ai("openai", api_key=os.environ["OPENAI_API_KEY"])
Relevant API Surface
- Signatures:
s,f,AxSignature - AxGen:
ax,AxGen - AxAI:
ai,get_supported_ai_models,dict[str, str],Callable[[dict[str, str]], dict[str, str]],closable generator,OpenAICompatibleClient,OpenAIResponsesClient,GoogleGeminiClient,AnthropicClient,AxUsageContext,AxUsageEvent,AxUsageObserver,set_usage_observer,AxRuntimeHooks,AxRateLimitInfo,AxRateLimiter,AxTracer,AxMeter,AxGlobals,set_rate_limiter,set_tracer,set_meter,AxBalancer,AxBalancerAdaptiveStrategy,AxBalancerStatsStore,AxInMemoryBalancerStatsStore,create_balancer_route_stats,update_balancer_route_stats,sample_balancer_route_health,MultiServiceRouter,ProviderRouter - Agents And RLM:
agent,AxAgent - Flow:
flow,AxFlow - Optimizers:
optimize,playbook,AxPlaybook,AxBootstrapFewShot,AxGEPA,OptimizerEngine,OptimizerEvaluator
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 8c8d9de8dfb6
- 2d ago First seen · 52 lines · 36 tokens per session scan A 45a6420791ff
ax-python-llm is a skill published in the GitHub repository ax-llm/ax (2,890 stars, last pushed yesterday), licensed Apache-2.0. It adds 36 tokens to every session and 672 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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