prompt-evaluator
SolidEvaluate how you use Claude Code — analyze prompt patterns, feature utilization, and get improvement suggestions. Trigger: /prompts:evaluate, prompt analysis, usage evaluation, how am I using Claude
Install
Quality Score: 87/100
Skill Content
Details
- Author
- nguyenthienthanh
- Repository
- nguyenthienthanh/aura-frog
- Created
- 8 months ago
- Last Updated
- today
- Language
- JavaScript
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
rootnode-prompt-validation
Evaluates and scores Claude prompts and system prompts using a six-dimension Scorecard with anchored 1-5 rubrics. Trigger on: "review my prompt," "score this prompt," "rate my prompt," "evaluate this prompt," "improve my prompt," "what's wrong with my prompt." Also trigger on: "why isn't my prompt working," "my prompt produces bad output," "help me fix this prompt," "is this prompt any good." Scorecard covers Objective Clarity, Context Specificity, Reasoning Fit, Output Precision, Behavioral Calibration, and Architectural Efficiency, plus a five-question Output Evaluation Rubric for assessing actual output. Also use when the user pastes a system prompt and asks for feedback. Activate whenever structured quality assessment of a single prompt is the primary need. Do NOT use for project-level audits involving Custom Instructions architecture, knowledge file organization, or multi-file Project structure (use rootnode-project-audit if available).
prompt-architect
Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.
prompt-optimizer
Optimize LLM prompt templates against an eval harness. Works with autoresearch for overnight prompt optimization. For projects that call LLMs (try-on prompts, chatProxy, MCP tools).