Agent Prompt Evolution | Agent Skills Atlas

About this skill

Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (An tracking), meta-agent evolution (Mn tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M0 evolution. 2-3 hours overhead per experiment.

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0 potential findings · Scanned 11/4/2026 · Scanner 0.2.1

Content SHA-256: e85480d086b93ed981831c3bef6886372dcd6bf8b4a65b49e8c83bb00b594800

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Original listing metadata

Original name

Agent Prompt Evolution

Original description

Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.

About this listing

Category: Science & research
Reported license: Not specified
Listing language (estimated): English
Review status: Not individually reviewed
Agent compatibility: Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.