Analyze prompts for weaknesses and provide actionable recommendations.
npx skills add https://github.com/jbrukh/skills --skill think-critically
Please help me install this Agent Skill. Skill: think-critically Source: https://github.com/jbrukh/skills/tree/main/skills/think-critically Command: npx skills add https://github.com/jbrukh/skills --skill think-critically
Rigorously evaluate prompts to anticipate LLM outputs and provide actionable recommendations.
Run the evaluation workflow on a given prompt to obtain a structured critique and concrete recommendations.
High-intent search queries and answers about installing and using this skill.
Adversarial analysis tests prompts by identifying brittle assumptions and failure modes across inputs. The evaluation workflow analyzes your prompt to reveal hidden weaknesses in expected outputs and provides structured critique with actionable recommendations.
Prompt evaluation is the process of assessing prompts for quality, safety, and reliability before deployment. You need it to anticipate LLM outputs, mitigate risks, and ensure robust performance across professional AI workflows.
Yes, the prompt critique workflow assesses prompts before deployment to ensure robust performance across inputs and adversarial variants. It enforces structured evaluation and explicit remediation steps for quality assurance.
The best way to identify hidden flaws is running an adversarial evaluation workflow that rigorously tests prompt design. This reveals weaknesses in expected outputs and delivers concrete recommendations for risk mitigation and safety improvements.
Prompt evaluation applies directly to risk assessment in professional AI workflows by enforcing adversarial testing and structured evaluation. It reveals weaknesses in expected outputs to ensure quality, safety, and reliability across inputs.
Inconsistent outputs often stem from brittle assumptions and hidden flaws in prompt design. Adversarial prompt evaluation identifies these failure modes and provides explicit remediation steps to improve reliability across varied inputs.
alexandereiseghohiEvaluate and refine AI agent outputs through self-critique and reflection loops.
selfagencyEvaluate AI prompts for safety, bias, security, and effectiveness.
Davie521Monitor a deployed URL for regressions after deploys.
hyukudanTest and evaluate LLM applications with BLEU, ROUGE-L, and LLM-as-judge.
