execute

Automate code execution across AI agents with QA gates and evidence logs.

1|Updated May 17, 2026
One-click install
npx skills add https://github.com/djimit/djimitflo --skill execute-djimit
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: execute
Source: https://github.com/djimit/djimitflo/tree/main/.opencode/skills/execute
Command: npx skills add https://github.com/djimit/djimitflo --skill execute-djimit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the execution of tasks across multiple AI coding agents, ensuring high-quality code output with rigorous quality assurance checks and completion evidence tracking.

Core Features & Use Cases

  • Task Execution: Automates code execution, handling coder retries and QA gates.
  • QA Gates: Includes syntax checks, static analysis, and review gates to ensure code quality.
  • Completion Evidence: Provides detailed logs and evidence of code completion for audit trails.
  • Use Case: Ideal for organizations that need to ensure the quality and safety of code produced by AI agents, particularly in environments where strict compliance and auditability are required.

Quick Start

Run the execute skill to process a task and ensure its completion with all QA gates passed.

Frequently Asked Questions about execute

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

FAQPage Schema
How do I automate task execution across multiple AI coding agents?

Automate task execution across multiple AI coding agents by running tasks through automated coder retries, syntax checks, static analysis, and review gates. This handles execution end-to-end while enforcing quality control and generating audit trails.

What are QA gates in AI code generation and how do they work?

QA gates in AI code generation are automated checkpoints that enforce quality by running syntax checks, static analysis, and review processes. They ensure code produced by AI agents meets required standards before completion is approved.

How do I track completion evidence for code generated by AI agents?

Track completion evidence for AI-generated code by automating task execution with detailed logging and audit trails. This provides verifiable proof that code tasks passed all QA gates and review checks before completion.

Can I use automated QA gates for strict compliance and auditability environments?

Yes, automated QA gates support strict compliance and auditability environments. The execution process generates detailed logs and completion evidence, ensuring AI-generated code meets rigorous quality and safety standards.

What happens when an AI coding agent fails a static analysis or syntax check?

When an AI coding agent fails a static analysis or syntax check, the execution automation handles coder retries. It reprocesses the task until it passes all review gates, ensuring final code quality.