optimize

Run the evo optimization loop. Each round, the orchestrator writes structured briefs and spawns parallel subagents that execute within them. Each subagent is semi-autonomous: it reads the pointer traces, forms the concrete edit, runs experiments, and can iterate within its branch. Runs until interrupted or the stall limit is reached.

Host conventions

This skill runs on any host that implements the Agent Skills spec. When the body uses generic phrases, apply the host's best-fit equivalent:

• "spawn N subagents in parallel" -- use your host's parallel-subagent or background-task tool if you have one (e.g. Agent with run_in_background, spawn_agent + wait_agent, spawn_agents_on_csv for batch). Respect the host's concurrency cap -- if N exceeds it, run in batches. If the host has no parallel-subagent tool, run them serially and note the reduced round width in the final summary.
• Slash commands shown in user-facing copy (e.g. /evo:optimize) -- translate to your host's mention syntax when speaking to the user (e.g. $evo optimize on Codex -- plugin namespace then skill name, separated by a space).

Configuration

These defaults can be overridden via arguments: /optimize [subagents=N] [budget=N] [stall=N]

• subagents: number of parallel subagents per round (default: 5)
• budget: max iterations each subagent can run within its branch (default: 5)
• stall: consecutive rounds with no improvement before auto-stopping (default: 5)

Prerequisites

• Workspace must be initialized (evo status should succeed)
• A baseline experiment must be committed (run /discover first)
• All benchmark dependencies must be available in the environment

Architecture

Orchestrator (this agent):
  - Reads state, identifies failure patterns cross-cutting the tree
  - Writes one brief per subagent: objective + parent + boundaries + pointer traces
  - Verifies briefs are diverse (no two attacking the same surface)
  - Collects results, prunes dead branches, adjusts strategy

  Subagent A (brief, budget: N iterations):
    - Reads its pointer traces, forms the concrete edit
    - Creates experiment, edits target, runs benchmark, analyzes
    - If budget remains and sees a promising follow-up, continues
    - Can run up to N serial experiments on its own branch
    - Returns: what it tried, what worked, what it learned

  Subagent B (different brief, budget: N iterations):
    - Same protocol, non-overlapping objective
    ...