| 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 | 23.0.14 |
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.
