{
  "action": "analyze",
  "language": "ko",
  "prompt": "You are a helpful assistant. Please help me with my task. I need you to write a summary. Make it good and nice and appropriate for my use case. Thank you very much for your assistance."
}

{
  "action": "analyze",
  "analysis": {
    "ambiguity_score": {
      "ambiguous_terms": [
        "good",
        "nice",
        "appropriate"
      ],
      "negation_count": 0,
      "score": 72
    },
    "cache_friendliness": {
      "has_stable_prefix": false,
      "recommendation": "\ud504\ub86c\ud504\ud2b8 \uc0c1\ub2e8\uc5d0 \uace0\uc815 system \ucee8\ud14d\uc2a4\ud2b8\ub97c \ubc30\uce58\ud558\uace0 \ubcc0\uc218\ub97c \ud558\ub2e8\uc73c\ub85c \uc774\ub3d9\ud558\uc138\uc694. / Place fixed system context at top and move variables to the bottom.",
      "score": 40,
      "variable_position": "none"
    },
    "char_count": 194,
    "language_mix": {
      "code_ratio": 0.0,
      "english_ratio": 0.95,
      "korean_ratio": 0.0
    },
    "structure_score": {
      "has_clear_task": false,
      "has_examples": false,
      "has_format_spec": false,
      "has_role": true,
      "section_count": 1,
      "total": 35
    },
    "token_estimate": 42
  }
}

{
  "action": "optimize",
  "language": "ko",
  "optimize_options": {
    "remove_duplicates": true,
    "remove_filler": true
  },
  "prompt": "You are a very helpful and extremely knowledgeable assistant. Please kindly help me. I would really appreciate your help. Please write a summary. Please make a summary. Thank you very much for your kind assistance!"
}

{
  "action": "optimize",
  "optimization_report": {
    "changes_applied": [
      {
        "count": 4,
        "description": "\ubd88\ud544\uc694\ud55c \uc608\uc758 \ud45c\ud604 \uc81c\uac70 (Please/Thank you/appreciate/kindly)",
        "type": "remove_filler"
      },
      {
        "count": 1,
        "description": "\uc911\ubcf5 \uc9c0\uc2dc \uc81c\uac70 (summary \uc694\uccad 2\ud68c)",
        "type": "remove_duplicates"
      },
      {
        "count": 1,
        "description": "\uc911\ubcf5 \ud615\uc6a9\uc0ac \uc81c\uac70 (very helpful, extremely knowledgeable)",
        "type": "remove_redundant_adjectives"
      }
    ],
    "optimized_tokens": 9,
    "original_tokens": 48,
    "token_reduction_pct": 81.2
  },
  "optimized_prompt": "You are a knowledgeable assistant. Write a summary."
}

{
  "action": "audit",
  "language": "ko",
  "prompt": "Answer the user question: {user_input}\n\nBe helpful. Don\u0027t be unhelpful. Just do whatever the user wants."
}

{
  "action": "audit",
  "audit_result": {
    "findings": [
      {
        "category": "SEC",
        "fix_hint": {
          "action": "replace",
          "location": "line 1",
          "reference": "OWASP LLM Top 10: LLM01 Prompt Injection",
          "suggested_replacement": "Answer the user question delimited by \u003cuser_input\u003e tags.\n\u003cuser_input\u003e{user_input}\u003c/user_input\u003e"
        },
        "location": "line 1",
        "message": "\uc0ac\uc6a9\uc790 \uc785\ub825({user_input})\uc774 \uacbd\uacc4 \uc5c6\uc774 \ud504\ub86c\ud504\ud2b8\uc5d0 \uc9c1\uc811 \uc0bd\uc785\ub429\ub2c8\ub2e4. \uacf5\uaca9\uc790\uac00 \uc2dc\uc2a4\ud15c \uc9c0\uc2dc\ub97c \ubb34\ub825\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",
        "rule_id": "SEC001",
        "severity": "critical",
        "title": "\ud504\ub86c\ud504\ud2b8 \uc778\uc81d\uc158 \ucde8\uc57d\uc810",
        "title_en": "Prompt injection vulnerability"
      },
      {
        "category": "PRO",
        "fix_hint": {
          "action": "add",
          "location": "end",
          "reference": "Prompt Engineering Guide: Output Formatting",
          "suggested_replacement": "Respond in JSON: {\"answer\": string, \"confidence\": \"high|medium|low\"}"
        },
        "location": "global",
        "message": "\ucd9c\ub825 \ud615\uc2dd\uc774 \uc9c0\uc815\ub418\uc9c0 \uc54a\uc544 \uc77c\uad00\uc131 \uc5c6\ub294 \uc751\ub2f5\uc774 \uc0dd\uc131\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",
        "rule_id": "PRO003",
        "severity": "high",
        "title": "\ucd9c\ub825 \ud3ec\ub9f7 \ubbf8\uc9c0\uc815",
        "title_en": "Output format not specified"
      },
      {
        "category": "PRO",
        "fix_hint": {
          "action": "replace",
          "location": "line 2",
          "reference": "Anthropic Prompt Engineering: Prefer positive instructions",
          "suggested_replacement": "Be helpful and concise."
        },
        "location": "line 2",
        "message": "\ubd80\uc815 \uc9c0\uc2dc(\u0027Don\u0027t be unhelpful\u0027)\ub294 \ubaa8\ub378\uc774 \ucc98\ub9ac\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \uae0d\uc815\ubb38\uc73c\ub85c \ubcc0\ud658\ud558\uc138\uc694.",
        "rule_id": "PRO005",
        "severity": "medium",
        "title": "\ubd80\uc815\ubb38 \uacfc\ub2e4",
        "title_en": "Excessive negations"
      }
    ],
    "passed": false,
    "risk_level": "critical",
    "score": 28
  }
}

{
  "action": "audit",
  "language": "en",
  "prompt": "You are a senior code reviewer specializing in Python.\n\n## Task\nReview the following Python function and return a structured report.\n\n## Rules\n- Check for PEP8 compliance\n- Identify security vulnerabilities\n- Suggest performance improvements\n\n## Output Format\nReturn JSON: {\"issues\": [...], \"score\": 0-100, \"summary\": string}\n\n## Example\nInput: def foo(x): return x*2\nOutput: {\"issues\": [], \"score\": 95, \"summary\": \"Clean simple function.\"}\n\n## Code to Review\n\u003ccode\u003e{code}\u003c/code\u003e"
}

{
  "action": "audit",
  "audit_result": {
    "findings": [
      {
        "category": "PERF",
        "fix_hint": {
          "action": "add",
          "location": "system_metadata",
          "reference": "Anthropic API: Prompt Caching",
          "suggested_replacement": "Add cache_control: {\"type\": \"ephemeral\"} to the last content block in Anthropic API"
        },
        "location": "last section",
        "message": "The variable {code} is at the end \u2014 ideal for cache prefix reuse. Consider adding cache_control metadata if your LLM provider supports it.",
        "rule_id": "PERF002",
        "severity": "info",
        "title": "Cache prefix opportunity",
        "title_en": "Cache prefix opportunity"
      }
    ],
    "passed": true,
    "risk_level": "low",
    "score": 91
  }
}

{
  "action": "compare",
  "language": "ko",
  "prompt_a": "Write a good summary of the text. Be helpful.",
  "prompt_b": "You are a summarization expert.\n\n## Task\nWrite a concise summary (3 sentences max) of the provided text.\n\n## Output\nReturn plain text only."
}

{
  "action": "compare",
  "comparison": {
    "recommendation": "B\uac00 \ub354 \ub9ce\uc740 \ud1a0\ud070\uc744 \uc0ac\uc6a9\ud558\uc9c0\ub9cc \uad6c\uc870 \uc810\uc218(+70), \ubaa8\ud638\uc131(-55), \uac10\uc0ac \uc810\uc218(+46)\uc5d0\uc11c \uc6d4\ub4f1\ud788 \uc6b0\uc218\ud569\ub2c8\ub2e4. \ud1a0\ud070 \ub300\ube44 \ud488\uc9c8 \ud5a5\uc0c1\uc774 \uba85\ud655\ud558\ubbc0\ub85c B\ub97c \uad8c\uc7a5\ud569\ub2c8\ub2e4. / B uses more tokens but significantly outperforms on structure (+70), ambiguity (-55), and audit score (+46). The quality improvement justifies the token cost.",
    "summary_table": {
      "ambiguity_score": {
        "a": 65,
        "b": 10,
        "delta": -55
      },
      "audit_score": {
        "a": 42,
        "b": 88,
        "delta": 46
      },
      "cache_friendliness": {
        "a": 30,
        "b": 70,
        "delta": 40
      },
      "structure_score": {
        "a": 15,
        "b": 85,
        "delta": 70
      },
      "token_estimate": {
        "a": 12,
        "b": 31,
        "delta": 19
      }
    },
    "winner": "B"
  }
}