yogsoth-ai Optimization Run
Execute multi-objective optimization over the candidate set given defined objectives and constraints, producing a Pareto front of non-dominated solutions.
Execution
Spawns a subagent that applies optimization logic to enumerate, evaluate, and filter candidate portfolios, returning the non-dominated set.
Why Subagent
Optimization requires systematic enumeration or heuristic search across the combinatorial space of possible portfolios. This computational work is self-contained and produces a well-defined output structure.
HARD-GATE
Output must contain at least 5 non-dominated solutions on the Pareto front. If the candidate set is too small or constraints too tight, report the maximum achievable frontier size with explanation.
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. |