Shape and govern end-to-end optimization workflows for algorithm-first quests.
npx skills add https://github.com/ResearAI/DeepScientist --skill optimize-researai
Please help me install this Agent Skill. Skill: optimize Source: https://github.com/ResearAI/DeepScientist/tree/main/src/skills/optimize Command: npx skills add https://github.com/ResearAI/DeepScientist --skill optimize-researai
Shapes and governs the end-to-end optimization workflow for algorithm-first quests by turning vague ideas into durable optimization lines and structured processes.
Instruct the system to shape the frontier, generate 2-3 candidate briefs, rank them, and promote the best into a durable optimization line.
High-intent search queries and answers about installing and using this skill.
To coordinate an optimization workflow, you shape ideas into candidate briefs and promote the best into a durable optimization line. This process manages discovery, ranking, and promotion with explicit exploration, exploitation, fusion, debug, and stop routes for reproducible progress.
A durable optimization line is a structured progression path that manages ranked candidate attempts from exploration to deployment. You need it when converting vague algorithmic ideas into reproducible, governed workflows with controlled exploration and exploitation routes.
Generate 2-3 candidate briefs by instructing the system to shape the frontier, then rank them based on structured criteria. The highest-ranked brief is promoted into a durable optimization line, enforcing reproducible workflows through prescribed templates like OPTIMIZE_CHECKLIST.md and CANDIDATE_BOARD.md.
Yes, the optimization workflow requires system interfaces such as bash_exec, artifact, and memory. It also mandates specific templates including OPTIMIZE_CHECKLIST.md, CANDIDATE_BOARD.md, PLAN.md, and CHECKLIST.md to enforce reproducible artifact generation and runtime interactions.
The best way to manage these phases is using controlled routing within a durable optimization line. The workflow explicitly directs exploration, exploitation, fusion, debug, and stop routes while recording outcomes to ensure reproducible algorithmic progress.
Structured templates like OPTIMIZE_CHECKLIST.md and CANDIDATE_BOARD.md enforce reproducible workflows and mandatory interface interactions. They prevent ad-hoc tracking errors by converting ideas into durable optimization lines with governed progression from exploration to deployment.
Rycen7822Coordinate algorithm-first quests by managing candidate briefs and frontier state.
leoplastureManage algorithm-first optimization quests with brief, rank, and execution submodes.
arcasilesgroupRecord, resolve, and renew risk acceptances with expiry and remediation plans.
hatch3rRank and select candidates from piped stdin using fzf fuzzy matching.
