ax-agent-context

name	ax-agent-context
description	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.
version	23.0.0

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.