Analyze evidence and propose bounded AI prompt modifications for user approval.
npx skills add https://github.com/kevintelford/holdfast --skill evolve-kevintelford
Please help me install this Agent Skill. Skill: evolve Source: https://github.com/kevintelford/holdfast/tree/main/skills/evolve Command: npx skills add https://github.com/kevintelford/holdfast --skill evolve-kevintelford
This Skill addresses the challenge of systematically proposing and applying bounded improvements to AI systems based on accumulated evidence, ensuring safe and controlled evolution.
Use the evolve skill to analyze recent evidence and generate a proposal for improving the AI prompt or approach.
High-intent search queries and answers about installing and using this skill.
Evidence-based AI evolution works by analyzing collected data patterns to identify output deficiencies, then crafting precise, bounded change proposals for evolvable surfaces. This mechanism ensures safe, controlled system improvements by requiring user approval before applying modifications.
The best way to apply incremental improvements safely is through an application workflow that supports both pipeline and direct file editing modes. This approach focuses on user approval and versioning to control the evolution of AI prompts and approaches.
You generate an AI prompt improvement proposal by analyzing recent evidence and output data to identify areas for improvement. The system crafts specific change suggestions that specify what changes, what remains fixed, and cite supporting evidence for the evolution.
Yes, you can use direct file editing to apply changes to AI approaches without a pipeline. The application workflow supports both pipeline and direct file editing modes for applying proposed evolutions, maintaining user approval and versioning controls.
The limitation of using bounded proposals for AI system optimization is that modifications are constrained to specific, incremental changes rather than sweeping updates. This ensures safe evolution but requires multiple iterations to achieve large-scale system transformations.
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