Extract structured requirements from conversations into mini-PRD, quick-PRD, and original prompt files.
npx skills add https://github.com/mbed92/phd --skill clavix-summarize-mbed92
Please help me install this Agent Skill. Skill: clavix-summarize Source: https://github.com/mbed92/phd/tree/main/.skills/clavix-summarize Command: npx skills add https://github.com/mbed92/phd --skill clavix-summarize-mbed92
This Skill addresses the challenge of capturing and structuring requirements discussed in natural language conversations, preventing loss of detail and ensuring clarity for development.
mini-prd.md, a concise quick-prd.md, and the original-prompt.md.Use the clavix-summarize skill to extract requirements from our last conversation.
High-intent search queries and answers about installing and using this skill.
You can extract structured requirements from a conversation by processing the conversational data to identify features, constraints, and objectives, then generating a mini-PRD, quick-PRD, and original prompt format.
Yes, automatically generating a PRD draft from conversational data creates a detailed mini-prd.md and a concise quick-prd.md file, preventing loss of detail and ensuring clarity for development.
Conversation analysis validates completeness for requirements gathering by running pre-validation checks, assigning confidence levels to extracted data, and enforcing a self-correction protocol for identified mistakes.
The best way to turn natural language conversations into structured output is to parse the dialogue for key objectives, validate the findings, and generate project-specific output files in a designated directory.
When extracting a mini-PRD, quality assessment metrics are provided alongside pre-validation checks and a self-correction protocol to ensure the extracted features and constraints are accurate and complete.
You need a specific format for prompt engineering outputs, specifically generating an original-prompt.md file alongside mini-prd.md and quick-prd.md to capture structured requirements from your conversational data.
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