optimizing-instructions

Name: optimizing-instructions
Availability: InStock
Author: axiomantic

Compress long instruction files while preserving capabilities and safety constraints.

8|5|Updated Dec 30, 2025

Search Tags:#prompt-engineering #token-reduction #instruction-optimization #semantics-preservation #safety-preservation #deterministic-changes

Ready to run

npx skills add https://github.com/axiomantic/spellbook --skill optimizing-instructions

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: optimizing-instructions
Source: https://github.com/axiomantic/spellbook/tree/main/skills/optimizing-instructions
Command: npx skills add https://github.com/axiomantic/spellbook --skill optimizing-instructions

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Long instruction files (skills, prompts, CLAUDE.md) can become unwieldy and costly to process. This Skill provides safe, deterministic compression to reduce tokens while preserving capability.

Core Features & Use Cases

Token-efficient compression: reduce length without altering behavior.
Safety-preserving: maintains critical safety constraints and edge-case handling.
Use Case: ideal for pruning verbose prompts before deployment in token-constrained environments or when consolidating multiple instructions.

Quick Start

Provide the instruction file path to optimize and request a shorter, equivalent version that preserves all capabilities.

Frequently Asked Questions about optimizing-instructions

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

FAQPage Schema

How do I compress long instruction files without losing capability?▼

To compress instruction files without losing capability, provide the file path to apply deterministic token-count reduction while preserving triggers, edge cases, and safety constraints. This yields a shorter, equivalent version ready for deployment.

How do I reduce tokens in a CLAUDE.md file before prompt deployment?▼

Reducing tokens in a CLAUDE.md file requires deterministic instruction optimization that prunes verbose prompts while preserving semantic behavior and critical safety constraints. This results in a compressed file suitable for token-constrained environments.

What is the best way to consolidate multiple nested skill definitions?▼

Consolidating multiple nested skill definitions involves deterministic instruction compression to reduce token length while maintaining all original triggers and edge-case handling. This produces a unified, token-efficient instruction set.

Does prompt compression preserve safety constraints and edge-case handling?▼

Yes, prompt compression strictly preserves safety constraints and edge-case handling during token-count reduction. The deterministic transformation ensures the optimized instruction retains all critical safety requirements and operational triggers.

Can I set a specific target reduction when optimizing prompts?▼

Yes, you can request a specific target reduction when optimizing prompts. The compression process applies deterministic changes to achieve the desired token-count reduction while validating that semantics and capabilities remain intact.

When should I avoid using automated instruction optimization?▼

Automated instruction optimization should be avoided when your prompts lack explicit safety constraints or when deterministic transformations cannot validate complex, non-deterministic semantic behaviors required by your specific AI workflows.