Execute the prompt optimization workflow via the Python optimize module.
npx skills add https://github.com/sachio222/based-stack --skill prompt-engineer-sachio222
Please help me install this Agent Skill. Skill: prompt-engineer Source: https://github.com/sachio222/based-stack/tree/main/prompt-engineer Command: npx skills add https://github.com/sachio222/based-stack --skill prompt-engineer-sachio222
This Skill enables immediate prompt optimization by invoking a dedicated Python workflow, removing the need for manual prompt analysis.
Request prompt optimization for a specific prompt and the system will immediately run the optimization workflow via the Python module.
High-intent search queries and answers about installing and using this skill.
You can automate prompt optimization by executing the dedicated Python module, which immediately runs the optimization workflow without requiring initial manual analysis. This script-driven approach handles single prompts, sub-agents, and ecosystems on demand.
Script-driven prompt optimization is the process of invoking a Python module to triage and refine prompts automatically. It is needed when you want to remove manual analysis and instantly apply optimized prompts to active workflows or sub-agents.
Yes, you can optimize sub-agent prompts at the ecosystem level by invoking the Python optimization module. The workflow supports single prompts, sub-agent prompts, and ecosystem-level prompt orchestration through appropriate step arguments.
No, the prompt optimization workflow does not perform initial analysis before execution. It immediately invokes the dedicated Python module to run the optimization process, removing the need for manual prompt analysis entirely.
The best way to integrate prompt optimization into CLI scripts is to invoke the optimize module directly using python -m skills.prompt_engineer.optimize with appropriate --step arguments, allowing immediate automated workflow execution.
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