What is the 'fit-to-generalize' loop?

It is an iterative process where the model fits a prompt to known input-output pairs while ensuring the prompt still performs correctly on new, unseen, or adversarial inputs.

Can I test my prompts locally with this skill?

Yes, the skill includes specialized CLI utilities for Claude and Codex to perform live testing of system prompts and output formats directly from the terminal.

What is the purpose of the 'parsimony' check in the rubric?

Parsimony ensures that the changes made to a prompt are the smallest possible edits required to fix a failure, preventing 'prompt bloat' and maintaining clarity.

How are read-trigger descriptions handled?

The skill provides specific patterns for triggers that ensure the AI opens necessary documentation immediately rather than summarizing steps prematurely.

How does this skill prevent prompt regressions?

It implements a micro-evaluation step that measures the pass rate after every small change, requiring that improvements outweigh any introduced failures before a change is accepted.

Prompt Alignment

Name: Prompt Alignment
Author: mshuffett

bymshuffett

Productivity & Workflow

Refines and validates AI prompts through an iterative fit-to-generalize loop to ensure consistent model behavior across diverse inputs.

About

Prompt Alignment provides a structured framework for optimizing Claude's performance by iteratively mapping input-to-output examples and testing for generalization. By proposing minimal textual edits and conducting micro-evaluations, this skill helps developers eliminate regressions, adhere to global safety and style constraints, and ensure prompts remain robust against adversarial or boundary cases. It is an essential tool for developers who need to fine-tune system prompts, read-trigger descriptions, or complex output styles with precision and confidence using a systematic, data-driven approach.

Key Features

Enforcement of global rules, safety policies, and style constraints
Iterative fit-to-generalize optimization loop
CLI-based live testing integration with Claude and Codex
0 GitHub stars
Micro-evaluation of pass rates for incremental prompt changes
Automated generalization probing for boundary and adversarial cases

Use Cases

Fine-tuning complex system prompts for specialized software engineering tasks
Standardizing model output styles across diverse input types while preventing behavioral regressions
Optimizing read-trigger descriptions to ensure Claude accesses documentation at appropriate moments

About

Key Features

Enforcement of global rules, safety policies, and style constraints
Iterative fit-to-generalize optimization loop
CLI-based live testing integration with Claude and Codex
0 GitHub stars
Micro-evaluation of pass rates for incremental prompt changes
Automated generalization probing for boundary and adversarial cases

Use Cases

Fine-tuning complex system prompts for specialized software engineering tasks
Standardizing model output styles across diverse input types while preventing behavioral regressions
Optimizing read-trigger descriptions to ensure Claude accesses documentation at appropriate moments