LibraryAI and agent development

SkillOpt

Optimize reusable natural-language agent skills with trajectory-driven edits and validation gates.

Stars
12,683
Forks
1,178
License
MIT
Updated
Updated Jul 14, 2026

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Project overview

SkillOpt treats a skill document as the trainable state of an otherwise frozen agent. An optimizer model converts scored trajectories into bounded add, delete, or replace edits, and a candidate is accepted only when it strictly improves a held-out validation score. The resulting best_skill.md can be deployed without changing model weights or adding extra model calls at inference time, giving researchers and engineering teams a controlled way to improve reusable agent instructions.

Repository facts

Primary language
Python
License
MIT
Repository updated
Jul 14, 2026
Default branch
main

Resource types

LibraryCLI app

Use cases

AI and agent developmentResearch and knowledge

Platforms

GitHub CopilotCodexOpenClaw

Runtime

Command lineLocal

Capabilities

MemoryObservabilityVerification and evals

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