Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →ax-llm/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.
Passed 22 of 22 test cases with the skill, and 5 of 22 without it.
and 26 more in the full report
17 fixed5 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have two requirements: (1) maintain a persistent orientation cache across daily runs over our API docs, and (2) compact history for a single 40-turn run. Name the context object and downstream skill for requirement (1), and the context object for requirement (2).
| Checked | Without skill | With skill |
|---|---|---|
| Identifies contextMap with ax-agent-rlm for requirement (1). | Missed | Passed |
| Identifies contextPolicy for requirement (2). | Missed | Passed |
The instructions your model reads when the skill loads.
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.
| 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 |
contextMap (persistent orientation cache).contextPolicy: { preset: 'checkpointed', budget: 'balanced' }; move to lean for very long runs with strong models, full for short tasks or weak models.agent.playbook(...) (offline from examples, or online from live feedback).agent.optimize(...) (GEPA).recall(...) for memories, discover({ skills }) for skill guides.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.contextMap to compress a single run's history. That is contextPolicy.contextPolicy to carry knowledge across runs. That is contextMap.agent.playbook(...).recall(...).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.test cases passed, vs 5 / 22 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.