/analyze-prompt
Analyze an AI prompt for clarity, effectiveness, and potential issues.
$ npx -y skills add rohitg00/awesome-claude-code-toolkit --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/analyze-prompt
Context preview
What this command does when you run it.
Analyze an AI prompt for clarity, effectiveness, and potential issues.
Command definition
analyze-prompt.md/analyze-prompt - Analyze AI Prompt
Analyze an AI prompt for clarity, effectiveness, and potential issues.
Steps
1. Read the prompt text provided by the user 2. Assess prompt structure: system instruction, context, task, format, examples 3. Check for clarity: ambiguous language, vague instructions, missing context 4. Evaluate specificity: are constraints clearly defined (length, format, tone) 5. Identify missing elements: examples, output format, edge case handling 6. Check for conflicting instructions that could confuse the model 7. Assess the prompt length: too short (underspecified) or too long (diluted focus) 8. Evaluate the role or persona definition if present 9. Check for proper few-shot examples if the task requires them 10. Identify potential failure modes: hallucination triggers, unsafe content risks 11. Score the prompt on: clarity (1-10), specificity (1-10), completeness (1-10) 12. Provide specific improvement recommendations ranked by impact
Rules
- Evaluate against the stated goal of the prompt
- Consider the target model's capabilities and limitations
- Check for prompt injection vulnerabilities in user-facing prompts
- Assess whether the prompt uses chain-of-thought when needed
- Verify output format instructions are unambiguous
- Do not rewrite the entire prompt; suggest targeted improvements
- Consider token efficiency: remove redundant instructions
Read more
/analyze-prompt - Analyze AI Prompt
Analyze an AI prompt for clarity, effectiveness, and potential issues.
Steps
1. Read the prompt text provided by the user 2. Assess prompt structure: system instruction, context, task, format, examples 3. Check for clarity: ambiguous language, vague instructions, missing context 4. Evaluate specificity: are constraints clearly defined (length, format, tone) 5. Identify missing elements: examples, output format, edge case handling 6. Check for conflicting instructions that could confuse the model 7. Assess the prompt length: too short (underspecified) or too long (diluted focus) 8. Evaluate the role or persona definition if present 9. Check for proper few-shot examples if the task requires them 10. Identify potential failure modes: hallucination triggers, unsafe content risks 11. Score the prompt on: clarity (1-10), specificity (1-10), completeness (1-10) 12. Provide specific improvement recommendations ranked by impact
Rules
- Evaluate against the stated goal of the prompt
- Consider the target model's capabilities and limitations
- Check for prompt injection vulnerabilities in user-facing prompts
- Assess whether the prompt uses chain-of-thought when needed
- Verify output format instructions are unambiguous
- Do not rewrite the entire prompt; suggest targeted improvements
- Consider token efficiency: remove redundant instructions
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Repo: rohitg00/awesome-claude-code-toolkit
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