| name | refine |
| description | Trigger continual harness refinement from IPython. Use when you notice a repeated failure, reusable tactic, delegation role, or behavior policy that should be persisted as a harness entry. Returns immediately; refinement runs when the current turn ends. |
Refine
Refinement analyzes the conversation trajectory and applies small, evidence-backed
updates to the continual harness (prompts, memories, skills, subagent specs).
The implementation lives in the host (the same one behind the user's /refine
command); this skill is the kernel-side interface to it. Call it directly from
IPython:
await refine.status()
await refine.run()
await refine.run("create a memory about always checking git status before committing")
await refine.run("promote the error-handling pattern to a global skill", global_=True)
API
await refine.status() — current refine state as a dict: pending (whether a
requested refine is already queued for this turn) and in_flight (whether a
refine is currently planning or applying).await refine.run(instructions=None, global_=False) — schedule refinement.
Returns {"scheduled": True} immediately, or {"scheduled": False, "reason": ...}
when refinement cannot start. Optional instructions focus the refinement on a
specific observation. Set global_=True to target the global harness store
(cross-session); omit for local (session-scoped) refinement.
Rules
- Refinement never runs mid-cell. A scheduled refinement runs when the current
turn ends; the harness applies changes and rebuilds the system prompt, then
resumes you automatically. Continue working normally after calling it.
- One request per turn is enough; calling
run again before the turn ends only
updates the instructions.
- Use refinement after observing a repeated failure, a reusable tactic, a
repeated delegation role, or a behavior policy worth persisting. Do not
rewrite the whole harness when a focused memory, skill, prompt note, or
subagent spec is enough.
