Design and evaluate versioned prompts for LLMs with automated test cases.
npx skills add https://github.com/tonone-ai/tonone --skill cortex-prompt
Please help me install this Agent Skill. Skill: cortex-prompt Source: https://github.com/tonone-ai/tonone/tree/main/team/cortex/skills/cortex-prompt Command: npx skills add https://github.com/tonone-ai/tonone --skill cortex-prompt
Designing effective prompts and validating their quality across models is complex and error-prone. Cortex-prompt provides a structured workflow to create, version, and evaluate prompts to ensure consistent results across tasks and deployments.
Create a versioned prompt package under prompts/v1 with a system prompt, user template, config, and examples, then run the evaluation harness to compare versions.
High-intent search queries and answers about installing and using this skill.
Prompt versioning manages prompts with changelogs and version history, while an evaluation harness runs automated test cases and scoring to quantify prompt quality. This ensures repeatable, measurable results across development and production deployments.
Automating prompt testing uses environment detection to identify LLM stacks and adapt prompts for different runtimes. It runs automated test cases and scoring within an evaluation harness to deliver repeatable prompt quality across environments.
Creating production-ready prompt packages involves generating versioned prompt bundles containing system prompts, user templates, configs, and examples. These ready-to-use bundles support both experimental evaluation and direct production deployment.
Yes, environment-aware prompt engineering detects LLM stacks and adapts prompts for different runtimes. This environment detection ensures prompts remain compatible and maintain consistent performance across various development and production contexts.
An evaluation harness is needed to run automated test cases and scoring, quantifying prompt quality objectively. It validates prompt effectiveness across models before deployment, solving the error-prone nature of unstructured prompt validation.
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