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aidlc-execution-agent

Enforce execution agent boundaries and authority across the AI-DLC lifecycle.

Updated Apr 11, 2026
Search Tags:#branching#scope#kb#annotation#tests-first#ai-dlc#execution-agent
One-click install
npx skills add https://github.com/CornFedKratos/s3-aidlc --skill aidlc-execution-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aidlc-execution-agent
Source: https://github.com/CornFedKratos/s3-aidlc/tree/main/plugins/s3-aidlc/skills/aidlc-execution-agent
Command: npx skills add https://github.com/CornFedKratos/s3-aidlc --skill aidlc-execution-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Execution Agent is a precision instrument designed to enforce scope, authority, and process across all agents in the AI-DLC lifecycle. It prevents scope creep, enforces governance, and standardizes how agents annotate, branch, test, and report.

Core Features & Use Cases

  • Identity & Scope Enforcement: defines declared boundaries and ensures agents operate strictly within them.
  • Protocol Adherence: codifies the mandatory steps (annotation, branching, tests-first, KB logging) to guarantee traceability.
  • Lifecycle Readiness: activated at session start and guides every action until the work is completed and documented.

Quick Start

Provide module-level guardrails and initialize the execution agent with the current ticket and scope.

Frequently Asked Questions about aidlc-execution-agent

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

FAQPage Schema
How do I enforce execution agent scope and prevent unauthorized actions in an AI-DLC workflow?▼

Execution agent scope is enforced by declaring strict boundaries and authority limits at agent initialization, ensuring every action operates strictly within the declared scope to prevent scope creep.

What is the tests-first protocol for AI-driven development lifecycle agents?▼

The tests-first protocol is a mandatory step in the AI-DLC lifecycle that requires writing tests before code execution, ensuring deterministic, auditable actions and traceability for every agent operation.

How do I standardize branching and annotation for AI agents before they write code?▼

Standardize branching and annotation by codifying hard rules that require agents to create dedicated branches and complete annotation before writing any code, guaranteeing process adherence and traceability.

Can I use execution agent discipline to manage KB logging and PR preparation?▼

Yes, execution agent discipline manages KB logging and PR preparation by activating at session start and guiding every action until work is completed, documented, and ready for pull request submission.

When do I need to initialize guardrails for an AI-DLC execution agent?▼

Initialize guardrails at agent initialization, during scope definition, branch creation, and whenever an agent encounters work outside its declared scope, ensuring lifecycle readiness and protocol adherence throughout the session.

Why does my AI agent encounter work outside its declared scope during development?▼

Agents encounter out-of-scope work when boundaries are not enforced at initialization, requiring the execution agent discipline to reapply declared file lists and scope rules to prevent unauthorized actions.

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