| name | iterative-refinement |
| description | Disciplined, measurable iteration for a substantial refinement or investigation: loop against verifiable pass/fail conditions, fan work out to subagents, and keep the main context lean. Use when improving something measurable over repeated cycles (tuning a metric or detector, refactoring against a regression bar), chasing a surprising or suspicious number, or driving a long multi-step task where delegation and context discipline matter. Not for one-shot edits or quick lookups that don't warrant a loop.
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Iterative refinement with verifiable conditions
Improve any system (script, pipeline, prompt, doc, config, dataset) by looping against measurable pass/fail conditions while keeping the main thread's context lean. Task-agnostic. The aim is to make "is it good yet?" a single repeatable command, catch each regression at the edit that caused it, and keep load-bearing reasoning in cheap, auditable steps.
Use this as a toolkit, not a script. Each method below earns its place by what it prevents, and that reasoning is stated inline so you can judge when it applies. Reach for the methods the situation calls for, scale them to the stakes, and adapt or skip what doesn't fit; they compose well, but no fixed subset is mandatory and this skill can't anticipate every task you'll point it at. When a method clearly fits, lean into it fully rather than half-applying it. The judgement of which to use, and how hard, stays yours.
The loop
- Turn the goal into a pass/fail rubric with explicit thresholds. "Useful" isn't checkable; "contamination < 2%, both naive and effective ratios reported, every input row classified, prints
Overall: PASS" is.
- Bake the rubric into the artifact as a self-check it prints. The artifact computes its own metrics and prints
[PASS]/[FAIL] per condition, so the check can't drift out of sync with the code the way an external checklist does. Now any party (you, a subagent, the user) re-verifies with one command.
- Change one layer, re-run the check. Catch each regression at the edit that caused it, not three edits later.
- Loop on a fast slice, not the full dataset. Size the slice so a run takes seconds, not minutes. Run the full corpus only at checkpoints, when a layer is structurally complete and at sign-off, since that's where rare signals and period splits actually appear and the cost is justified.
