whetstone

Run a skill on a test input, self-grade, and rewrite with A/B decisions.

7|Updated May 4, 2026
One-click install
npx skills add https://github.com/toobulkeh/claude-whetstone --skill whetstone
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: whetstone
Source: https://github.com/toobulkeh/claude-whetstone/tree/main
Command: npx skills add https://github.com/toobulkeh/claude-whetstone --skill whetstone

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you stop iterating on one-off prompts and instead turn a workflow into a durable, self-improving skill artifact that gets better with each run.

Core Features & Use Cases

  • Turn-of-the-crank improvement loop: Load a target skill, run it on a real input, self-grade against its own rules, then rewrite.
  • A/B ambiguity surfacing: When the target skill leaves decisions unspecified, the Skill blocks on constrained A/B choices so the next version matches the user’s preference.
  • Skill distillation and versioning: If the target skill file doesn’t exist, the Skill distills prior context into a structured skill.md draft and then improves it.

Quick Start

Run the whetstone loop on the path to your target skill file and provide a real test input, then answer the A/B questions it asks before it folds changes.

Frequently Asked Questions about whetstone

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

FAQPage Schema
How do I improve coding agent prompts and turn them into reusable skills?▼

To improve coding agent prompts, you run a target skill on a real test input, self-grade the output against its own rules, and then rewrite the skill file with user-approved A/B decisions. This iterative loop refines one-off prompts into durable, versioned instruction artifacts.

What is skill distillation for coding agents when the instruction file is missing?▼

Skill distillation is the process of generating a structured skill.md draft from prior context when the target skill file does not exist. It distills historical workflow context into a baseline instruction file, which is then immediately improved through the self-grading and A/B refinement loop.

How does self-grading work when refining agent workflow instructions?▼

Self-grading works by executing a cold-run on a real test input and evaluating the generated output against the target skill's own established rules. It provides grade evidence to identify unspecified decisions, blocking further refinement until A/B ambiguities are resolved.

Can I use this to resolve unspecified A/B decisions in my agent's skill.md file?▼

Yes, you can use this to resolve A/B ambiguities by surfacing constrained choices whenever the target skill leaves a decision unspecified. It blocks the rewriting process to ask you A/B questions, ensuring the next version matches your specific preference.

What are the limitations of iterative prompt improvement for coding agents?▼

Limitations include enforcing a strict turn budget for the agent's execution and requiring a real test input to run the cold-run. Additionally, the Fold phase edits only the target skill file, meaning external system prompts or unrelated files cannot be modified during the refinement process.

Do I need an existing skill file to start refining my agent workflows?▼

No, you do not need an existing skill file to start refining agent workflows. If the target skill is missing, the system distills prior context into a structured skill.md draft and then immediately begins improving it through the self-grading and A/B loop.