Skillopt
skillberry-ai/cap-evolveRuns the SkillOpt single-lineage optimization loop over epochs x mini-batches with a textual learning rate (an integer edit budget that decays on a constant|linear|cosine schedule), a within-epoch rejected-edit + failure-pattern buffer injected into the optimizer prompt, and a gated epoch-boundary slow/meta update that fixes longitudinal regressions. Parent is always the current best; acceptance is the val significance gate; the test split stays sealed. Use when a run benefits from disciplined annealing and per-epoch consolidation rather than hill-climb's one-shot whole-trainset proposals or g...
At a glance
/plugin marketplace add skillberry-ai/cap-evolve
/plugin install skillopt
Also in skillberry-ai/cap-evolve
View the repoFully-agentic, free-form optimization algorithm. Use in agent orchestration mode when you want the conversational agent to own the whole sea...
Establish the starting point. Use after implement-and-check and before any algorithm. Creates the run directory, freezes the seeded train/va...
Extract the learning signal from execution traces — the textual analogue of a gradient. Use between evaluation and proposing edits. Reads a...
Score a candidate on a split with honest, variance-aware evaluation. Use whenever you need a number for a candidate (the algorithm calls it...
Score the best candidate on the held-out TEST split exactly once and seal the run. Use as the last evaluation step, after optimization stops...
Apply the acceptance decision that keeps optimization honest — always on the val split, by default requiring the improvement to exceed the s...
Runs the real GEPA optimization loop (arXiv:2507.19457) — sample-efficient reflective Pareto search. Use when rollouts are expensive and the...
Runs a global hill-climb optimization loop where the parent is always the current best candidate and the val significance gate decides accep...
The HARD GATE that must pass before any optimization budget is spent. Use right after intake. Walks the agent through implementing the 4 ada...
Phase 1 of the pipeline — collect inputs and scaffold the run. Use at the very start of any optimization. Interviews the user to decide what...
Optimize an MCP toolset whose server is EXTERNAL (you can't re-implement the tools). Use when the agent talks to tools served over MCP and m...
Drive the entire cap-evolve pipeline end to end, autonomously. Use when the user wants the whole optimization run with minimal hand-holding....