refine

Iteratively improves research artifacts through repeated review, fix, and wiki update cycles.

Updated Jul 15, 2026
One-click install
npx skills add https://github.com/1549171506/shizhengmubangai1 --skill refine-1549171506
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: refine
Source: https://github.com/1549171506/shizhengmubangai1/tree/main/Desktop/123/empiricalwiki/i18n/en/skills/refine
Command: npx skills add https://github.com/1549171506/shizhengmubangai1 --skill refine-1549171506

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Research artifacts like ideas, experiment plans, and paper drafts often need multiple rounds of critique and revision, and doing this manually loses track of what was fixed, what score each round received, and which issues remain unresolved. ## Core Features & Use Cases - Multi-round review loop: Repeatedly calls the /review skill on an artifact, parses structured feedback, applies fixes, and re-reviews until a target score is reached or convergence is detected. - Classified issue handling: Sorts actionable items into directly fixable content issues, wiki knowledge gaps, claim status updates, and out-of-scope items, updating wiki entities and graph edges where appropriate. - Improvement history report: Produces a REFINE_REPORT with the score trajectory, fixed issues table, wiki changes, unresolved issues, and suggested next steps. - Use Case: You have a paper draft in wiki/outputs/ scoring 5/10 on review. Run the refine loop with a target score of 8 to automatically apply reviewer feedback across rounds and receive a full audit trail of every change. ## Quick Start Refine the artifact at wiki/outputs/paper-draft-v1.md with a target score of 8 and a maximum of 4 rounds.

Frequently Asked Questions about refine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I iteratively improve a research paper draft with AI review?

Run the refine loop on the draft file path or wiki slug. Each round calls the review skill, applies fixes to the artifact in place, and re-reviews until the target score, convergence, or the maximum round count is reached.

How to set a target review score for iterative refinement?

Pass the --target-score option with a value from 1 to 10, defaulting to 8. The loop terminates as soon as a review round returns a score at or above the target, or when the verdict is ready.

What happens when the review verdict is rethink?

If the first round returns a rethink verdict, the loop terminates immediately and suggests a redesign instead of iterating. Refinement is not applied to artifacts needing fundamental restructuring.

Does the refine loop stop if the score stops improving?

Yes. If the score does not change for two consecutive rounds, the loop treats the artifact as converged and terminates. This prevents infinite iteration on issues that cannot be fixed within the loop.

What are the limitations of automated iterative refinement?

Issues requiring new experimental data, external paper ingestion, or domain expert judgment cannot be fixed inside the loop. These are recorded as unresolved issues with suggested follow-up commands like /exp-design or /ingest.