Keyoku

Configuration

2b. Muscle memory — converged goals become reusable workflows

A goal that converges (goal_assess reports all criteria met) promotes its action trace into a learned workflow. Next time you start a similar goal, keyoku surfaces what worked before — so the agent never rediscovers it.

• Capture happens three ways: explicit goal_record, live goal_focus (real actions stream into the goal's trace as you work), or activity backfill (if you just did the work without recording, keyoku lifts the steps from the activity log). Already have hollow workflows from older runs? keyoku backfill repopulates them.
• Reuse needs no API key. keyoku is driven by a frontier coding agent, so the agent is the judge of relevance. goal_assess returns candidateWorkflows and the agent picks the ones that genuinely apply — which matches verbose, differently-worded goals that token-overlap never could. A lite model is an optional accelerator for headless runs (keyoku watch/cron), not a requirement.
• It self-prunes. Suggestions rank by similarity × precision, where precision is learned from whether a workflow's steps actually recur — so word-matching-but-never-useful workflows sink.
• Negative memory too. Approaches that failed on the way to convergence are captured as pitfalls and surfaced as "avoid (failed before): …" on similar goals.
• Refine raw into clean. keyoku refine <slug> turns noisy captured steps into a tight, {{parameterized}} template ready to run.