Integrate research, stakeholder interviews, and multi-LLM review into feature planning.
npx skills add https://github.com/krzemienski/shannon --skill gepetto-krzemienski
Please help me install this Agent Skill. Skill: gepetto Source: https://github.com/krzemienski/shannon/tree/main/skills/gepetto Command: npx skills add https://github.com/krzemienski/shannon --skill gepetto-krzemienski
This Skill addresses the challenge of creating detailed and thorough implementation plans by integrating research, stakeholder interviews, and multi-LLM review, ensuring comprehensive and well-structured feature planning.
Begin a new plan by creating a markdown spec file and running the /gepetto @path/to/your-spec.md command.
High-intent search queries and answers about installing and using this skill.
Feature planning for complex implementations is streamlined by integrating research, stakeholder interviews, and multi-LLM review into a structured process. This ensures thorough pre-implementation analysis and well-structured plans.
Stakeholder interviews are incorporated into implementation planning through a structured process that combines research findings and stakeholder input. This integration ensures comprehensive analysis before finalizing a feature plan.
You can use multiple LLMs to review and validate a feature planning spec. This multi-LLM review mechanism provides external feedback, enhancing the reliability and thoroughness of the final implementation plan.
To start the feature planning process, create a markdown spec file and run the command targeting your file path. This initiates the structured workflow from research to final plan and external review.
Multi-LLM review works effectively for complex features requiring thorough pre-implementation analysis. It validates the structured planning process by utilizing multiple LLMs to provide external feedback and enhance plan reliability.
An automated planning process may not be suitable for simple features that do not require thorough pre-implementation analysis. It is specifically tailored for complex features needing detailed research, stakeholder interviews, and multi-LLM validation.
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