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-flownpx skills add ax-llm/ax --skill ax-python-flowgit 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 | $0.00035 | $0.00954 |
| Opus 5 | $0.00017 | $0.00477 |
| Sonnet 5 | $0.00007 | $0.00191 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
ax-python-flow 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 2d ago.
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
86% identical to ax-cpp-flow — 85 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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AxFlow 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
- Compose generators, agents, and nested flows into a workflow graph.
- Reason about flow state, node inputs, returns, caching, and errors.
- Use generated package examples for flow graphs and provider-backed flows.
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, flow
draft = ax("topicText:string -> draftText:string")
wf = (
flow({"id": "docs.coreFlow"})
.execute("draft", draft, {"reads": ["topicText"], "writes": ["draftResult", "draftText"]})
.returns({"draftText": "draftText"})
)
More Patterns
Typed programs
Build each flow node from its own input/output contract.
classifier = ax('requestText:string -> route:class "support, sales, engineering"')
responder = ax("requestText:string, route:string -> responseText:string")
Class decision
Declare reads and writes so the responder waits for the typed route.
branch_flow = (
flow({"id": "docs.branchFlow"})
.execute("classifier", classifier, {"reads": ["requestText"], "writes": ["classifierResult", "route"]})
.execute("responder", responder, {"reads": ["requestText", "route"], "writes": ["responderResult", "responseText"]})
.returns({"route": "route", "responseText": "responseText"})
)
Parallel fan-out and join
Independent reads let research and audience analysis share one planner group.
parallel_flow = (
flow({"id": "docs.parallelFlow"})
.execute("research", research, {"reads": ["topicText"], "writes": ["researchResult", "factList"]})
.execute("audience", audience, {"reads": ["topicText"], "writes": ["audienceResult", "audienceAngle"]})
.execute("join", join, {"reads": ["factList", "audienceAngle"], "writes": ["joinResult", "briefText"]})
.returns({"briefText": "briefText"})
)
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.
- 2d ago First seen · 112 lines · 35 tokens per session scan A a91e28cef470
ax-python-flow is a skill published in the GitHub repository ax-llm/ax (2,888 stars, last pushed 2d ago), licensed Apache-2.0. It adds 35 tokens to every session and 954 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to ax-cpp-flow, differing in 85 lines, and is treated as a copy.
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