dry-run

Enforce tool-call ceilings and dry-run protocols for Tool Registry operations.

Updated May 11, 2026
One-click install
npx skills add https://github.com/AesopScott/mojo --skill dry-run-aesopscott
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dry-run
Source: https://github.com/AesopScott/mojo/tree/main/harnesses/skills/dry-run
Command: npx skills add https://github.com/AesopScott/mojo --skill dry-run-aesopscott

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents runaway costs and accidental resource waste by enforcing strict boundaries and verification steps before executing expensive or irreversible AI tool calls.

Core Features & Use Cases

  • Tool-Call Ceilings: Limits the number of operations to prevent token or compute exhaustion.
  • Dry-Run Verification: Requires a simulated execution path for costly actions to ensure safety before live deployment.
  • Sandbox Reuse: Optimizes resource usage by maintaining persistent environments for testing.
  • Use Case: When modifying a complex Tool Registry configuration, use this Skill to validate the change in a dry-run mode to ensure no unauthorized API calls or excessive token usage occur.

Quick Start

Use the dry-run skill to inspect the current Tool Registry boundary and propose a cost-efficient implementation plan for the requested change.

Frequently Asked Questions about dry-run

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

FAQPage Schema
How do I prevent runaway token usage during AI tool execution?

Prevent runaway costs by enforcing tool-call ceilings that strictly bound the number of operations, stopping token or compute exhaustion before it happens. This limits execution volume and maintains cost efficiency.

What is a dry-run protocol for pre-execution validation?

A dry-run protocol is a simulated execution path required for costly actions to verify operational safety before live deployment. It ensures no unauthorized API calls or system state changes occur unexpectedly.

How do I validate Tool Registry configuration changes without executing live API calls?

Validate Tool Registry configuration changes by inspecting the current boundary and proposing a cost-efficient implementation plan in a simulated dry-run mode, ensuring no excessive token usage or live API calls occur.

Does AI tool execution support automated rollback planning for system state changes?

Yes, automated rollback planning is supported alongside dry-run protocols to manage system state changes. These features ensure strict boundaries are enforced, providing safe recovery paths if execution exceeds expected limits.

Can I use sandbox reuse to optimize compute spend during maintenance workflows?

Yes, sandbox reuse optimizes compute spend by maintaining persistent testing environments. This prevents repeated environment initialization, bounding resource usage and keeping overall maintenance costs strictly efficient.

When should I enforce tool-call ceilings in development workflows?

Enforce tool-call ceilings during development and maintenance workflows when token usage, compute spend, and system state changes must be strictly bounded. This prevents accidental resource waste and unauthorized operations.