Discover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery.
Quickly install symbolic-equation AI skill to your development environment via command line
Source: lingzhi227/agent-research-skills.
Discover interpretable scientific equations from data using LLM-guided evolutionary search.
Step 3: Evolutionary Search Loop Repeat until convergence or max samples:
Step 4: Prompt Construction Present previous equations as versioned sequence:
Discover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery. Source: lingzhi227/agent-research-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/lingzhi227/agent-research-skills --skill symbolic-equationDiscover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery. Source: lingzhi227/agent-research-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/lingzhi227/agent-research-skills --skill symbolic-equation Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/lingzhi227/agent-research-skills