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prompt-generator

Generate Blu platform prompts with Python following naming and architecture constraints.

Updated Mar 21, 2026
Search Tags:#prompt management#Jinja2#Langfuse#prompt generation#Blu platform
One-click install
npx skills add https://github.com/CidLucas/repo_platform --skill prompt-generator-cidlucas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-generator
Source: https://github.com/CidLucas/repo_platform/tree/main/.claude/skills/prompt-generator
Command: npx skills add https://github.com/CidLucas/repo_platform --skill prompt-generator-cidlucas

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of creating, critiquing, and refining prompts for the Blu platform, ensuring compliance with naming conventions, architecture constraints, and Langfuse/builtin delivery systems.

Core Features & Use Cases

  • Prompt Creation: Generate prompts for all 5 prompt types (agents, skills, orchestrator, specialists, fragments).
  • Naming Conventions: Enforce Blu naming conventions and architecture constraints.
  • Langfuse vs Builtin Placement: Determine the correct placement for Langfuse-managed or builtin-only prompts.
  • Flat Jinja2 Variables: Use flat Jinja2 variables for prompt content.
  • Use Case: For a new agent, use this Skill to generate the appropriate prompt that respects layer boundaries and naming conventions.

Quick Start

Use the prompt-generator skill to create a new agent prompt for the 'customer-service' agent.

Frequently Asked Questions about prompt-generator

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

FAQPage Schema
How do I create a prompt for the Blu platform that follows the 4-layer agent architecture?▼

To create a Blu platform prompt, use the prompt generator to automate adherence to the 4-layer agent architecture, enforcing naming conventions and architecture constraints across all 5 prompt types.

When should I use Langfuse-managed prompts versus builtin-only prompts on the Blu platform?▼

Determine Langfuse versus builtin placement by evaluating the prompt type and architecture constraints; the generator automates this decision to ensure correct delivery system selection for your Blu platform components.

Can I use flat Jinja2 variables for prompt generation in Python?▼

Yes, you can use flat Jinja2 variables for prompt content; the generator uses Python to automate creation while strictly enforcing the use of flat Jinja2 syntax for Blu platform compatibility.

What are the Blu naming conventions for generating agent and specialist prompts?▼

Blu naming conventions dictate specific structural formats for agents, skills, orchestrators, specialists, and fragments; the generator automatically enforces these constraints during prompt creation.

Do I need Python to automate Blu platform prompt management and critique?▼

Yes, Python is required as a dependency to automate the creation, critique, and refinement of Blu platform prompts, ensuring compliance with architecture constraints and Langfuse placement.

What is the best way to generate a customer-service agent prompt that respects layer boundaries?▼

The best way to generate a compliant customer-service agent prompt is using the generator, which automatically respects layer boundaries and enforces Blu naming conventions for the new agent.

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