evo-hq
Organizationturns your codebase into an autoresearch loop — discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.
Categories
Indexed Skills (6)
discover
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 setting up evo, wants to instrument a codebase for autonomous optimization, or asks to start a new evo run on a project.
infra-setup
Non-user-invocable provider/setup reference for evo backend switching, prerequisite checks, and auth/install guidance.
optimize
Run the evo optimization loop with parallel subagents until interrupted.
subagent
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 they're required to emit -- useful when writing briefs or debugging a subagent's behavior.
finetuning
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, smoke-run gate, three failure diagnostics, five false-progress patterns. Provider recipes and I/O contract in references/.
report
Print the dashboard's dot chart (score over experiment order, status colors, best-path stair) inline in the terminal for every run in the workspace. Use when the user invokes /evo:report, asks for a quick score chart without opening the dashboard, or wants the scatter plot in chat output.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.