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.