How do I install the Ax Agent Context skill?

Run npx skillmds@latest add dosco/ax-agent-context in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ax Agent Context safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ax Agent Context?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ax Agent Context free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ax Agent Context?

dosco (@dosco) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ax Agent Context by dosco — Agent Skill

Name: Ax Agent Context
Author: dosco

Ax Agent Context

This skill helps an LLM pick the right AxAgent context tool for a job - contextMap for recurring corpora, contextPolicy presets for within-run trajectory compaction, agent.optimize for offline GEPA instruction/demo tuning, agent.playbook for an evolving context playbook (offline evolve + online update), and recall/memories + skills for per-turn retrieval. Use when the user asks "which context feature should I use", confuses contextMap with contextPolicy or memory, or wants a decision guide for long-context agents. For contextPolicy/contextMap codegen use ax-agent-rlm; for recall/skills use ax-agent-memory-skills; for agent.optimize or agent.playbook use ax-agent-optimize.

dosco c0b99e4 3.5 KB Updated Sep 17, 2026

File contents

AxAgent Context Selection (@ax-llm/ax)

Use this skill to route a context-management need to the right AxAgent tool, then open the matching codegen skill. AxAgent manages four distinct context objects; choosing the wrong one is the usual mistake. Do not write tutorial prose; pick the tool and hand off.

Pick The Right Context Tool

Need	Object	Scope	Use	Next skill
Many tasks over the same large corpus (repo, doc set, dataset)	Context map	recurring corpus, persists across runs	`contextMap`	`ax-agent-rlm`
One long run whose own history must stay under control	Trajectory compaction	this run only	`contextPolicy: { preset, budget }`	`ax-agent-rlm`
Evolve task strategy from examples or live feedback	Context playbook	a stage, offline + online	`agent.playbook(...)`	`ax-agent-optimize`
Tune the prompt/instructions/demos offline	Instruction text	a program, offline	`agent.optimize(...)` (GEPA)	`ax-agent-optimize`
Pull task-relevant facts or guides for a turn	Retrieval	one turn	`recall(...)` / skills	`ax-agent-memory-skills`

Defaults

Recurring corpus + many different questions -> contextMap (persistent orientation cache).
One long multi-turn run with prompt pressure -> contextPolicy: { preset: 'checkpointed', budget: 'balanced' }; move to lean for very long runs with strong models, full for short tasks or weak models.
Evolve a context playbook -> agent.playbook(...) (offline from examples, or online from live feedback).
Tune instructions/demos offline -> agent.optimize(...) (GEPA).
Fetch facts or guides on demand -> recall(...) for memories, discover({ skills }) for skill guides.
A single oversized input value (a pasted doc, a big JSON blob) -> do nothing; autoUpgrade (ON by default) keeps it runtime-only with a prompt preview. Reach for contextFields only when you want a specific inline policy or the value is a large required non-string field. See ax-agent-rlm.

Anti-Patterns

Do not use contextMap to compress a single run's history. That is contextPolicy.
Do not use contextPolicy to carry knowledge across runs. That is contextMap.
Do not hand-build a strategy playbook in the prompt. Evolve it with agent.playbook(...).
Do not stuff a whole corpus into the prompt every run. Use a context map plus on-demand recall(...).
Do not confuse runtime skills (discover({ skills }) guides) with these installable codegen skills.

ax-agent-rlm - contextPolicy presets, context maps, and runtime sessions.
ax-agent-memory-skills - recall, memories, and dynamic skill loading.
ax-agent-optimize - GEPA via agent.optimize(...) and the context playbook via agent.playbook(...).
ax-agent - core agent shape and the final/clarification protocol.

---name: ax-agent-contextdescription: This skill helps an LLM pick the right AxAgent context tool for a job - contextMap for recurring corpora, contextPolicy presets for within-run trajectory compaction, agent.optimize for offline GEPA instruction/demo tuning, agent.playbook for an evolving context playbook (offline evolve + online update), and recall/memories + skills for per-turn retrieval. Use when the user asks "which context feature should I use", confuses contextMap with contextPolicy or memory, or wants a decision guide for long-context agents. For contextPolicy/contextMap codegen use ax-agent-rlm; for recall/skills use ax-agent-memory-skills; for agent.optimize or agent.playbook use ax-agent-optimize.---# AxAgent Context Selection (@ax-llm/ax)Use this skill to route a context-management need to the right AxAgent tool, then open the matching codegen skill. AxAgent manages four distinct context objects; choosing the wrong one is the usual mistake. Do not write tutorial prose; pick the tool and hand off.## Pick The Right Context Tool| Need | Object | Scope | Use | Next skill || --- | --- | --- | --- | --- || Many tasks over the same large corpus (repo, doc set, dataset) | Context map | recurring corpus, persists across runs | `contextMap` | `ax-agent-rlm` || One long run whose own history must stay under control | Trajectory compaction | this run only | `contextPolicy: { preset, budget }` | `ax-agent-rlm` || Evolve task strategy from examples or live feedback | Context playbook | a stage, offline + online | `agent.playbook(...)` | `ax-agent-optimize` || Tune the prompt/instructions/demos offline | Instruction text | a program, offline | `agent.optimize(...)` (GEPA) | `ax-agent-optimize` || Pull task-relevant facts or guides for a turn | Retrieval | one turn | `recall(...)` / skills | `ax-agent-memory-skills` |## Defaults- Recurring corpus + many different questions -> `contextMap` (persistent orientation cache).- One long multi-turn run with prompt pressure -> `contextPolicy: { preset: 'checkpointed', budget: 'balanced' }`; move to `lean` for very long runs with strong models, `full` for short tasks or weak models.- Evolve a context playbook -> `agent.playbook(...)` (offline from examples, or online from live feedback).- Tune instructions/demos offline -> `agent.optimize(...)` (GEPA).- Fetch facts or guides on demand -> `recall(...)` for memories, `discover({ skills })` for skill guides.- A single oversized input value (a pasted doc, a big JSON blob) -> do nothing; `autoUpgrade` (ON by default) keeps it runtime-only with a prompt preview. Reach for `contextFields` only when you want a specific inline policy or the value is a large required non-string field. See `ax-agent-rlm`.## Anti-Patterns- Do not use `contextMap` to compress a single run's history. That is `contextPolicy`.- Do not use `contextPolicy` to carry knowledge across runs. That is `contextMap`.- Do not hand-build a strategy playbook in the prompt. Evolve it with `agent.playbook(...)`.- Do not stuff a whole corpus into the prompt every run. Use a context map plus on-demand `recall(...)`.- Do not confuse runtime skills (`discover({ skills })` guides) with these installable codegen skills.## See Also- `ax-agent-rlm` - contextPolicy presets, context maps, and runtime sessions.- `ax-agent-memory-skills` - recall, memories, and dynamic skill loading.- `ax-agent-optimize` - GEPA via `agent.optimize(...)` and the context playbook via `agent.playbook(...)`.- `ax-agent` - core agent shape and the final/clarification protocol.

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