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evo-hq/evo

SkillsMP has collected 7 skills from evo-hq/evo. Open a skill to review its source and details.

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skills collected
7
GitHub stars
1,446
GitHub forks
109

Showing 7 of 7 collected skills.

occupation
Computer Occupations, All Other
description

Initialize evo for the current repository by exploring the codebase, proposing unexplored optimization dimensions, constructing the benchmark inside a baseline worktree, and running the first experiment. Use when the user invokes /evo:discover, mentions…

updated
occupation
Network & Computer Systems Administrators
description

Non-user-invocable provider/setup reference for evo backend switching, prerequisite checks, and auth/install guidance.

updated
occupation
Computer Occupations, All Other
description

Drive structured autoresearch iteration after evo:discover and the baseline commit. Use when the user invokes /evo:optimize or asks to try ideas, try variants, run experiments, use available GPUs, improve the current best/frontier, continue an evo search, or…

updated
occupation
Computer Occupations, All Other
description

Read-only evo run reporting. Use when the user invokes /evo:report, asks what happened overnight, asks what improved recently, asks for the best/frontier candidates, asks for a quick score chart without opening the dashboard, or wants the scatter plot in chat…

updated
occupation
Software Developers
description

Land the winning experiment from an evo run as a clean, mergeable change -- open a PR when the repo has a remote, otherwise merge into the working branch. Distills the best-scoring experiment down to the minimal diff that reproduces its behaviour, shaped for…

updated
occupation
Computer Occupations, All Other
description

Protocol that evo optimization subagents follow when dispatched from /optimize. Auto-loaded by spawned subagents via their host's skill loader. The orchestrator may also invoke this skill to understand the brief shape its dispatched subagents expect + what…

updated
occupation
Data Scientists
description

This skill should be used when picking or diagnosing a training move (SFT, LoRA, DPO/KTO/ORPO, RFT, GRPO/PPO/RLOO, RLHF), or when the user mentions fine-tuning, post-training, training recipe, reward design, or weight updates. Decision tree by reward shape,…

updated
Showing 7 of 7 collected skills.