prompt-builder

Execute prompt_builder.py to generate agent prompts with token budgets.

Updated Nov 7, 2025
One-click install
npx skills add https://github.com/mehdic/CDC --skill prompt-builder
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: prompt-builder
Source: https://github.com/mehdic/CDC/tree/main/.claude/skills/prompt-builder
Command: npx skills add https://github.com/mehdic/CDC --skill prompt-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill deterministically builds complete prompts for agents (Developer, QA, SSE, TL, PM, etc.), ensuring consistent context, budgets, and markers before any spawn.

Core Features & Use Cases

  • Reads context, specializations, and token budgets from the database
  • Validates required markers and writes the final prompt to a file
  • Returns a JSON summary including tokens, lines, and status to the orchestrator

Quick Start

Build a Developer prompt for group CALC and save it to bazinga/prompts/{session}/developer_CALC.md.

Frequently Asked Questions about prompt-builder

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

FAQPage Schema
How do I build deterministic prompts for agent spawning?

Deterministic prompt building executes a script that reads context and token budgets from your database, constructs complete prompts for agents like Developer or QA Expert, validates required markers, and writes the final prompt to a file before spawning. This ensures consistent, reproducible agent behavior across sessions.

What's included in a generated agent prompt?

Generated prompts include task context from databases, agent-specific specializations, per-model token budgets, required validation markers, and role definitions for Developer, QA, Tech Lead, PM, Investigator, and Requirements Engineer roles. The output includes token count, line count, and status metadata.

Can I customize token budgets for different models?

Yes. The prompt builder reads per-model token budgets from configuration, applies them during prompt construction, and validates that the final prompt respects those limits before saving and returning the result for spawning.

What happens if required markers are missing from the prompt?

The prompt builder validates that all required markers are present during construction. If validation fails, the build process reports the status so you can correct the agent definitions or context before spawning.

How do I integrate prompt building into my agent spawn workflow?

Call the prompt builder script with your parameters JSON file specifying the agent role, context group, and output path. It returns a JSON result with token count and status; use this result to immediately spawn your agent with the generated prompt.

What input data does the prompt builder need?

The builder reads parameters from a JSON file specifying agent role and context group, retrieves specializations and token budgets from your database, loads agent definitions from the filesystem, and combines them to construct the complete prompt.