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-agent-rlmgit 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-agent-rlm)<a href="https://agentmods.dev/skills/ax-llm/ax/ax-agent-rlm"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-agent-rlm/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-agent-rlm"><img src="https://agentmods.dev/badge/skills/ax-llm/ax/ax-agent-rlm.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 | $0.00088 | $0.07473 |
| Opus 5.5 | $0.00035 | $0.02989 |
| Sonnet 5.5 | $0.00018 | $0.01495 |
| Haiku 4.5 | $0.00009 | $0.00747 |
Grade A, and why
ax-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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ax-agent-rlm — 98% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 505 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AxAgent RLM Runtime Rules (@ax-llm/ax)
Use this skill for code-runtime agents and llmQuery(...) semantic-helper behavior. For ordinary agent setup, child agents, tool namespaces, clarification, and bubbleErrors, use ax-agent. For callbacks and logs, use ax-agent-observability. For memories and skill loading, use ax-agent-memory-skills.
Use These Defaults
- Use
agent(...), notnew AxAgent(...). - In stdout-mode RLM, use one observable
console.log(...)step per non-final actor turn. - Rely on
autoUpgrade(ON by default) for oversized inputs you did not declare incontextFields: any input value over ~8k serialized chars is kept runtime-only automatically, with a 1,200-char prompt preview plus acontextMetadataline, while the full value stays live in the runtime asinputs.<field>. Declare a field incontextFieldsonly when you want a specific inline policy (promptMaxChars/keepInPromptChars) or need a large required non-string field kept out of the prompt (those are left inline by auto-upgrade). - Default to
contextPolicy: { preset: 'checkpointed', budget: 'balanced' }for most RLM tasks. - Prefer
contextPolicy: { preset: 'adaptive', budget: 'balanced' }when older successful turns should collapse sooner while live runtime state stays visible. - Use
contextMapfor recurring long-context corpora when the distiller should start future runs with a small persisted orientation cache. - Prefer
promptLevel: 'default'for normal use. - Use
promptLevel: 'detailed'when you want extra anti-pattern examples and tighter teaching scaffolding in the actor prompt. - Prefer
executorModelPolicywhen the actor may need to upgrade after repeated error turns or discovery in specific namespaces without also upgrading the responder. - Use explicit child agents in
functions: [...]when the task needs specialist agents with their own tools/runtime. - Use
llmQuery(...)only for focused semantic questions over narrowed context; it does not spawn a tool-using child AxAgent. - Prefer
maxSubAgentCallsonly when you need an explicit cap onllmQuery(...)sub-query usage.
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 350d58cf475e
- 4d ago Changed f8a2c27c2537
- 6d ago Changed 4f15d1c83b66
- 7d ago Changed 20b8107aa5da
- 9d ago Changed c99643e00ebd
- 11d ago Changed 5a39942a04ba
- 13d ago Changed 7de5e8fe8cd5
- 16d ago Changed 6365f23260a8
- 17d ago Changed f5e047696a40
- 18d ago First seen · 505 lines · 88 tokens per session scan A 3c0684c9a074
ax-agent-rlm is a skill published in the GitHub repository ax-llm/ax (2,969 stars, last pushed 3d ago), licensed Apache-2.0. It adds 88 tokens to every session and 7,473 once invoked, about $0.0004 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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output-dev-prompt-file
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output-dev-eval-testing
Create offline evaluation tests for Output SDK workflows using @outputai/evals. Use when implementing test evaluators with verify(), creating dataset YAML files, building eval workflows, or running workflow tests via CLI.
output-eval-judge-prompt
Design effective LLM judge .prompt files for evaluators. Use when creating judgeVerdict/judgeScore/judgeLabel prompts, or when existing judges produce unreliable results.