What does the Prompt Builder skill do?

It builds complete, role-specific prompts for sub-agents (like Developers or QA) by combining database context with predefined templates.

Can I use it for manual testing?

Yes, the underlying Python script supports a CLI mode that allows developers to generate and inspect prompts for debugging purposes.

How does it handle different Claude models?

The skill applies specific token budgets per model—900 for Haiku, 1800 for Sonnet, and 2400 for Opus—to ensure prompts fit within context limits.

What are required markers in the prompts?

Markers are specific strings like 'READY_FOR_QA' that the skill validates to ensure the generated prompt contains all necessary workflow triggers.

When should I invoke this skill?

You should invoke it immediately before spawning any specialized agent to ensure they have the most up-to-date project context.

Deterministic Agent Prompt Builder

Name: Deterministic Agent Prompt Builder
Author: mehdic

bymehdic

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Developer Tools

Generates role-specific, token-optimized prompts for specialized AI agents within a multi-agent development workflow.

About

The Prompt Builder skill is a specialized orchestration utility for Claude Code that automates the generation of complex, deterministic instructions for agentic teams. By programmatically retrieving role specializations, error patterns, and project context from a local database, it constructs validated prompts tailored for Developers, QA Experts, Tech Leads, and Project Managers. This skill ensures that every spawned sub-agent operates with consistent context and follows specific implementation patterns, significantly improving the reliability of autonomous multi-agent development cycles.

Key Features

Automated validation of required prompt markers and safety instructions
Model-specific token budget optimization for Haiku, Sonnet, and Opus
Seamless integration with Claude Code Task spawning in a single turn
1 GitHub stars
Deterministic prompt generation using Python-based logic
Dynamic context injection from local database and filesystem

Use Cases

Orchestrating a multi-agent team to implement complex software features
Generating context-aware prompts for QA agents to perform regression testing
Resuming long-running project management tasks with persistent state injection

About

Key Features

Automated validation of required prompt markers and safety instructions
Model-specific token budget optimization for Haiku, Sonnet, and Opus
Seamless integration with Claude Code Task spawning in a single turn
1 GitHub stars
Deterministic prompt generation using Python-based logic
Dynamic context injection from local database and filesystem

Use Cases

Orchestrating a multi-agent team to implement complex software features
Generating context-aware prompts for QA agents to perform regression testing
Resuming long-running project management tasks with persistent state injection