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.