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LLMs-Universal-Life-Science-and-Clinical-Skills- Meta_Prompter

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install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/Platform/Meta_Prompter" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-meta-prompter && rm -rf "$T"
manifest: Skills/Platform/Meta_Prompter/SKILL.md
source content
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name: meta-prompter description: Automatic prompt engineering & optimization keywords:

  • prompt-engineering
  • optimization
  • meta-prompting
  • llm
  • tuning measurable_outcome: Improves prompt performance metrics by >15% over baseline. license: MIT metadata: author: Biomedical OS Team version: "1.0.0" compatibility:
  • system: Python 3.10+ allowed-tools:
  • run_shell_command
  • read_file

Meta-Prompter

The Meta-Prompter is a tool for self-optimizing agent prompts. It analyzes agent performance and iteratively refines system prompts to maximize accuracy and adherence to instructions.

When to Use This Skill

  • When an agent is consistently failing a specific type of task.
  • When deploying a new agent and needing to tune its persona.
  • When A/B testing different prompting strategies.

Core Capabilities

  1. Prompt Optimization: Rewriting prompts for clarity and effectiveness.
  2. Performance Evaluation: Testing prompts against benchmarks.
  3. Few-Shot Generation: Creating optimal examples for context.

Example Usage

User: "Optimize the Clinical Reasoning prompt."

Agent Action:

python3 platform/optimizer/meta_prompter.py --target "clinical_reasoning" --iterations 5
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