ax-agent-context

# 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.