Claude Code Prompt Engineer

Instructions

Implementation Reference

• index.ts:472-553 (handler)

  Core handler function that executes the auto_optimize tool logic: analyzes text, detects if optimization is needed, handles interactive questioning or direct optimization.
  async function autoOptimizeText( text: string, context?: string, forceInteractive?: boolean ): Promise<{ shouldOptimize: boolean; optimizedPrompt?: string; analysis: any; needsQuestions?: boolean; questions?: string[]; sessionId?: string; }> { const analysis = shouldAutoOptimize(text); if (!analysis.shouldOptimize) { return { shouldOptimize: false, analysis }; } const detectedInfo = await detectLanguageAndTaskType(text); // Check if we should ask questions (either forced or auto-detected) const shouldAskQuestions = forceInteractive || analysis.needsQuestions; if (shouldAskQuestions) { try { const questions = await generateClarifyingQuestions(text, detectedInfo.language, context); const sessionId = generateSessionId(); // Store session for later use activeSessions.set(sessionId, { originalPrompt: text, context: context ? [context] : [], refinements: [], language: detectedInfo.language, complexity: detectedInfo.complexity as any, taskType: detectedInfo.taskType as any }); return { shouldOptimize: true, analysis: { ...analysis, detectedLanguage: detectedInfo.language, taskType: detectedInfo.taskType, complexity: detectedInfo.complexity }, needsQuestions: true, questions, sessionId }; } catch (error) { // If question generation fails, fall back to direct optimization console.error('Question generation failed, falling back to direct optimization:', error); } } // Direct optimization (no questions needed or questions failed) try { const optimizedPrompt = await engineerPrompt(text, detectedInfo.language, context); return { shouldOptimize: true, optimizedPrompt, analysis: { ...analysis, detectedLanguage: detectedInfo.language, taskType: detectedInfo.taskType, complexity: detectedInfo.complexity, method: 'built-in' } }; } catch (error) { // If optimization fails, still return the analysis return { shouldOptimize: true, analysis: { ...analysis, error: error instanceof Error ? error.message : String(error) } }; } }
• index.ts:97-119 (schema)

  Tool schema definition with name, description, input schema for arguments validation (text required, context and interactive optional).
  const AUTO_OPTIMIZE_TOOL: Tool = { name: "auto_optimize", description: "Automatically detects and optimizes natural language text for Claude Code. Use this when the user is writing conversational text that should be translated into an optimized prompt.", inputSchema: { type: "object", properties: { text: { type: "string", description: "The natural language text to analyze and potentially optimize" }, context: { type: "string", description: "Any additional context about the current project or situation" }, interactive: { type: "boolean", description: "Whether to enable interactive questioning if the prompt is unclear or needs more info (default: auto-detect based on complexity)" }, }, required: ["text"], title: "auto_optimizeArguments" } };
• index.ts:570-572 (registration)

  Registration of available tools including AUTO_OPTIMIZE_TOOL in the ListToolsRequestSchema handler.
  server.setRequestHandler(ListToolsRequestSchema, async () => ({ tools: [ENGINEER_PROMPT_TOOL, ASK_CLARIFICATION_TOOL, ANSWER_QUESTIONS_TOOL, AUTO_OPTIMIZE_TOOL], }));
• index.ts:675-723 (handler)

  Dispatch handler in CallToolRequestSchema that validates arguments, calls autoOptimizeText, and formats the response based on the result.
  case "auto_optimize": { if (!isAutoOptimizeArgs(args)) { throw new Error("Invalid arguments for auto_optimize"); } const { text, context, interactive } = args; const result = await autoOptimizeText(text, context, interactive); if (!result.shouldOptimize) { return { content: [{ type: "text", text: `**Analysis:** This text doesn't appear to need optimization.\n\nReason: ${result.analysis.reason}\nConfidence: ${(result.analysis.confidence * 100).toFixed(1)}%\n\n**Original text:** "${text}"` }], isError: false, }; } // If questions are needed, start interactive session if (result.needsQuestions && result.questions && result.sessionId) { return { content: [{ type: "text", text: `**🤔 I need more information to optimize this effectively!**\n\n**Analysis:** ${result.analysis.questioningReason || 'Request needs clarification'}\n\n**Questions to help me create the best prompt:**\n\n${result.questions.map((q, i) => `${i + 1}. ${q}`).join('\n')}\n\n**Next Step:** Use the answer_questions tool with session ID "${result.sessionId}" to provide your answers.\n\n---\n**Detected:** ${result.analysis.detectedLanguage || 'General'} | ${result.analysis.taskType} | ${result.analysis.complexity}` }], isError: false, }; } // Direct optimization result if (result.optimizedPrompt) { const analysis = result.analysis; return { content: [{ type: "text", text: `**🚀 Auto-Optimized Prompt for Claude Code:**\n\n${result.optimizedPrompt}\n\n**Are you ready to use this prompt?**` }], isError: false, }; } else { return { content: [{ type: "text", text: `**Analysis:** This text appears to be a prompt that should be optimized, but optimization failed.\n\nReason: ${result.analysis.reason}\nError: ${result.analysis.error || 'Unknown error'}\n\n**Original text:** "${text}"` }], isError: false, }; } }
• index.ts:214-253 (helper)

  Key helper function that analyzes input text to determine if it should be auto-optimized, calculates confidence, and decides if interactive questions are needed.
  function shouldAutoOptimize(text: string): { shouldOptimize: boolean; confidence: number; reason: string; needsQuestions: boolean; questioningReason?: string; } { if (text.length < 10) { return { shouldOptimize: false, confidence: 0, reason: "Text too short", needsQuestions: false }; } // Don't optimize if it's already a well-structured prompt if (text.includes('**') || text.includes('1.') || text.includes('- ')) { return { shouldOptimize: false, confidence: 0.1, reason: "Already structured", needsQuestions: false }; } // Don't optimize if it's clearly just conversation const conversationPatterns = /\b(thanks|thank you|yes|no|ok|okay|sure|got it|makes sense)\b/i; if (conversationPatterns.test(text) && text.length < 50) { return { shouldOptimize: false, confidence: 0.2, reason: "Simple conversation", needsQuestions: false }; } if (isNaturalLanguagePrompt(text)) { const confidence = Math.min(0.9, text.length / 100 + 0.3); // Determine if questions are needed const needsQuestions = shouldAskQuestions(text); const questioningReason = needsQuestions ? getQuestioningReason(text) : undefined; return { shouldOptimize: true, confidence, reason: "Natural language prompt detected", needsQuestions, questioningReason }; } return { shouldOptimize: false, confidence: 0.1, reason: "Not a prompt", needsQuestions: false }; }