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-playbookgit 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-playbook)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-playbook"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-playbook/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-playbook"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-playbook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00070 | $0.02138 |
| Opus 5.5 | $0.00028 | $0.00855 |
| Sonnet 5.5 | $0.00014 | $0.00428 |
| Haiku 4.5 | $0.00007 | $0.00214 |
Grade A, and why
ax-playbook 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook Codegen Rules (@ax-llm/ax)
Use this skill to generate context-playbook code. A playbook grows an evolving body of task knowledge and renders it into a program's context. The evolution engine (ACE — Agentic Context Engineering) is hidden behind playbook(...), exactly as optimize(...) hides its optimizer. Prefer the playbook(...) concept; only reach for AxACE directly when the user explicitly wants the low-level engine.
Use These Defaults
- Create with
playbook(program, { studentAI, teacherAI? }); it returns anAxPlaybookhandle. - Grow offline with
await pb.evolve(examples, metric)— returns{ bestScore, playbook }. - Grow online with
await pb.update({ example, prediction, feedback })— no metric needed. - Apply with
pb.applyTo(program)(defaults to the bound program). - Persist with
pb.toJSON()and restore withplaybook(program, opts).load(snapshot). - Inspect with
pb.render()(markdown) andpb.getState()({ playbook, artifact }). - For agents use
agent.playbook({ target: 'actor' | 'responder' }); default target is'actor'. - Use a cheaper
studentAIto run the program and an optional strongerteacherAIto reflect/curate. - Pass AI service options for the reflect/curate calls as
teacherOptions. A teacher markedisExpensive(for example OpenAIgpt-5.5-pro) is rejected unless you passteacherOptions: { useExpensiveModel: 'yes' }. The generated Python, Java, C++, Go, and Rust packages apply the same gate and acceptteacherOptionsin their option maps. - Prefer
ai(),ax(), andagent()for new code.
Critical Rules
playbook(...)binds to anAxGenprogram;evolve/updateneed that program's signature.evolve()returns only{ bestScore, playbook }. There is no Pareto front and nooptimizedProgram— that isoptimize(...)'s shape, not a playbook's.update({ example, prediction, feedback })requires the full{ example, prediction };examplemust match the program's input fields (plus any expected output). Do not pass bare input fields at the top level.update()works without a priorevolve()/load()— the handle hydrates lazily on first use.applyTo()injects a## Context Playbookblock into the program description; calling it repeatedly recomposes from the original base (no stacking).- Keep the offline
metricdeterministic and cheap, like a GEPA metric. - A playbook is plain JSON. Persist
pb.toJSON()andload(...)it into a fresh program for production. - The playbook engine, construction-time agent attachment, failure harvesting, and verified agent evolution are available in TypeScript and the generated Python, Java, C++, Go, and Rust packages. Use each package's native casing and callback types.
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.
- 3d ago Changed 8a7e4ce2329c
- 4d ago Changed 985d55221efb
- 6d ago Changed 8d1d84d6b2b0
- 7d ago Changed 34505d8d8cbd
- 9d ago Changed fa27c631913b
- 11d ago Changed be52c7e4cfac
- 13d ago Changed b5135ffcae7e
- 16d ago Changed 2b9baf502ed8
- 17d ago Changed 97025c80cc30
- 18d ago First seen · 134 lines · 70 tokens per session scan A 3db842378e0b
ax-playbook is a skill published in the GitHub repository ax-llm/ax (2,969 stars, last pushed 3d ago), licensed Apache-2.0. It adds 70 tokens to every session and 2,138 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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