SkillOpt Model Card

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SkillOpt Model Card

1. Role in Self Evolve

SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts. It matters because self-evolving agents need explicit runtime, memory, skill, and benchmark substrates before their improvement claims become trustworthy.

2. Working Principle

collect trajectories -> propose skill edits -> validate on held-out tasks -> keep stronger best_skill artifacts -> repeat like epochs and mini-batches without touching base model weights

3. Evidence Path

web-observed GitHub repo page and commit history showed about 4k stars, 416 forks, 5 issues, 2 pull requests, 92 commits, MIT license, explicit epoch/batch/validation-gate framing for text-space skill optimization, and latest visible commits on 2026-05-31. This iteration keeps freshness honest: the snapshot comes from the public GitHub page observed on 2026-06-05, while shell GitHub API access remained blocked in this workspace.

4. Teaching Use

Use this card to explain Self-Evolving Agent Skill Optimizer: it shows how swarm runtimes, skill optimizers, benchmark suites, browser harnesses, and memory middleware fit into the broader self-evolving-agent pipeline.

5. Limits

The repository was not cloned in this iteration; no benchmark run, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page signals unless independently revalidated later.