{
    "sourceFile": "llm_prototype/prompt_template.txt",
    "activeCommit": 0,
    "commits": [
        {
            "activePatchIndex": 17,
            "patches": [
                {
                    "date": 1745578477575,
                    "content": "Index: \n===================================================================\n--- \n+++ \n"
                },
                {
                    "date": 1745580055535,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -7,17 +7,18 @@\n 1.  Analyze the provided list of filenames.\r\n 2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n 3.  For each identified asset group, determine:\r\n     *   A concise `asset_name`.\r\n-    *   The `asset_category`. This must be one of the following: {{CATEGORIES_PLACEHOLDER}}.\r\n+    *   A concise `asset_name`.\r\n+    *   The `asset_category`. This must be one of the following: {{EXPECTED_CATEGORIES}}.\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n-5.  For each file, determine its `classification`. This must be one of the following: {{CLASSIFICATIONS_PLACEHOLDER}}.\r\n-6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following: {{MAP_TYPES_PLACEHOLDER}}. If the classification is not \"Map\", the `map_type` should be `null`.\r\n+5.  For each file, determine its `classification`. This must be one of the following: {{EXPECTED_CLASSIFICATIONS}}.\r\n+6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`.\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. Do not include any other text outside the JSON.\r\n \r\n Input File List:\r\n\\ No newline at end of file\n {{FILE_LIST_JSON}}\r\n \r\n Expected Output JSON Schema (Output ONLY this structure after the thinking block):\r\n-{{OUTPUT_SCHEMA_PLACEHOLDER}}\n+{{OUTPUT_SCHEMA}}\n\\ No newline at end of file\n"
                },
                {
                    "date": 1745580971850,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -7,16 +7,15 @@\n 1.  Analyze the provided list of filenames.\r\n 2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n 3.  For each identified asset group, determine:\r\n     *   A concise `asset_name`.\r\n-    *   A concise `asset_name`.\r\n-    *   The `asset_category`. This must be one of the following: {{EXPECTED_CATEGORIES}}.\r\n+    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n-5.  For each file, determine its `classification`. This must be one of the following: {{EXPECTED_CLASSIFICATIONS}}.\r\n+5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n 6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`.\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n-8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. Do not include any other text outside the JSON.\r\n+8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n \r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n \r\n"
                },
                {
                    "date": 1745581729634,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -10,10 +10,10 @@\n     *   A concise `asset_name`.\r\n     *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n-5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n-6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`.\r\n+5.  For each file, determine its `classification`. This must be one of the following exact strings: PBRMap, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n+6.  If the classification is \"PBRMap\", determine the `map_type`. This must be one of the following: {{EXPECTED_MAP_TYPES}}. If the classification is not \"PBRMap\", the `map_type` should be `null`.\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n \r\n Input File List:\r\n"
                },
                {
                    "date": 1745581876695,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -15,8 +15,77 @@\n 6.  If the classification is \"PBRMap\", determine the `map_type`. This must be one of the following: {{EXPECTED_MAP_TYPES}}. If the classification is not \"PBRMap\", the `map_type` should be `null`.\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n \r\n+## Examples\r\n+\r\n+Here are a few examples of input file lists and the EXACT expected JSON output format.\r\n+\r\n+**Example 1: Single PBR Surface**\r\n+Input:\r\n+[\r\n+  \"Wood_Plank_01_COL_1K.jpg\",\r\n+  \"Wood_Plank_01_NRM_1K.jpg\",\r\n+  \"Wood_Plank_01_ROUGH_1K.jpg\"\r\n+]\r\n+Expected Output:\r\n+[\r\n+  {\r\n+    \"asset_name\": \"Wood_Plank_01\",\r\n+    \"asset_category\": \"Surface\",\r\n+    \"asset_archetype\": \"Wood\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Wood_Plank_01_COL_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"COL\"},\r\n+      {\"input_path\": \"Wood_Plank_01_NRM_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"NRM\"},\r\n+      {\"input_path\": \"Wood_Plank_01_ROUGH_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"ROUGH\"}\r\n+    ]\r\n+  }\r\n+]\r\n+\r\n+**Example 2: Multiple Imperfection Maps**\r\n+Input:\r\n+[\r\n+  \"Smudge_A.tif\",\r\n+  \"Scratch_B.tif\",\r\n+  \"Dust_C.tif\",\r\n+  \"preview.jpg\"\r\n+]\r\n+Expected Output:\r\n+[\r\n+  {\r\n+    \"asset_name\": \"Smudge_A\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Smudge\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"PBRMap\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Scratch_B\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Scratch\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"PBRMap\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Dust_C\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Dust\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Dust_C.tif\", \"classification\": \"PBRMap\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Smudge_A\", # Assign extra files to the first asset or a generic asset if needed\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Smudge\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null}\r\n+    ]\r\n+  }\r\n+]\r\n+\r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n \r\n Expected Output JSON Schema (Output ONLY this structure after the thinking block):\r\n"
                },
                {
                    "date": 1745583040505,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -33,11 +33,11 @@\n     \"asset_name\": \"Wood_Plank_01\",\r\n     \"asset_category\": \"Surface\",\r\n     \"asset_archetype\": \"Wood\",\r\n     \"file_classifications\": [\r\n-      {\"input_path\": \"Wood_Plank_01_COL_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"COL\"},\r\n-      {\"input_path\": \"Wood_Plank_01_NRM_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"NRM\"},\r\n-      {\"input_path\": \"Wood_Plank_01_ROUGH_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"ROUGH\"}\r\n+      {\"input_path\": \"Wood_Plank_01_COL_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"COL\"},\r\n+      {\"input_path\": \"Wood_Plank_01_NRM_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"NRM\"},\r\n+      {\"input_path\": \"Wood_Plank_01_ROUGH_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"ROUGH\"}\r\n     ]\r\n   }\r\n ]\r\n \r\n@@ -55,25 +55,25 @@\n     \"asset_name\": \"Smudge_A\",\r\n     \"asset_category\": \"Imperfection\",\r\n     \"asset_archetype\": \"Smudge\",\r\n     \"file_classifications\": [\r\n-      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"PBRMap\", \"map_type\": \"MASK\"}\r\n+      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n     ]\r\n   },\r\n   {\r\n     \"asset_name\": \"Scratch_B\",\r\n     \"asset_category\": \"Imperfection\",\r\n     \"asset_archetype\": \"Scratch\",\r\n     \"file_classifications\": [\r\n-      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"PBRMap\", \"map_type\": \"MASK\"}\r\n+      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n     ]\r\n   },\r\n   {\r\n     \"asset_name\": \"Dust_C\",\r\n     \"asset_category\": \"Imperfection\",\r\n     \"asset_archetype\": \"Dust\",\r\n     \"file_classifications\": [\r\n-      {\"input_path\": \"Dust_C.tif\", \"classification\": \"PBRMap\", \"map_type\": \"MASK\"}\r\n+      {\"input_path\": \"Dust_C.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n     ]\r\n   },\r\n   {\r\n     \"asset_name\": \"Smudge_A\", # Assign extra files to the first asset or a generic asset if needed\r\n@@ -87,6 +87,5 @@\n \r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n \r\n-Expected Output JSON Schema (Output ONLY this structure after the thinking block):\r\n-{{OUTPUT_SCHEMA}}\n\\ No newline at end of file\n+Expected Output JSON Schema (Output ONLY this structure after the thinking block):\n\\ No newline at end of file\n"
                },
                {
                    "date": 1745583051578,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -10,10 +10,10 @@\n     *   A concise `asset_name`.\r\n     *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n-5.  For each file, determine its `classification`. This must be one of the following exact strings: PBRMap, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n-6.  If the classification is \"PBRMap\", determine the `map_type`. This must be one of the following: {{EXPECTED_MAP_TYPES}}. If the classification is not \"PBRMap\", the `map_type` should be `null`.\r\n+5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n+6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n \r\n ## Examples\r\n"
                },
                {
                    "date": 1745583220700,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -86,6 +86,4 @@\n ]\r\n \r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n-\r\n-Expected Output JSON Schema (Output ONLY this structure after the thinking block):\n\\ No newline at end of file\n"
                },
                {
                    "date": 1745583377641,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -15,8 +15,11 @@\n 6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n \r\n+Input File List:\r\n+{{FILE_LIST_JSON}}\r\n+\r\n ## Examples\r\n \r\n Here are a few examples of input file lists and the EXACT expected JSON output format.\r\n \r\n@@ -85,5 +88,4 @@\n   }\r\n ]\r\n \r\n Input File List:\r\n-{{FILE_LIST_JSON}}\r\n"
                },
                {
                    "date": 1745583918617,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -0,0 +1,91 @@\n+# llm_prototype/prompt_template.txt\r\n+\r\n+You are an expert 3D asset file classifier. Your task is to analyze a list of filenames from a single asset source (which may contain multiple distinct assets) and provide a structured JSON output detailing the identified assets and their file classifications.\r\n+\r\n+Follow these steps:\r\n+\r\n+1.  Analyze the provided list of filenames.\r\n+2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n+3.  For each identified asset group, determine:\r\n+    *   A concise `asset_name`.\r\n+    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n+    *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n+4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n+5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n+6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n+7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n+8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n+\r\n+Input File List:\r\n+{{FILE_LIST_JSON}}\r\n+\r\n+## Examples\r\n+\r\n+Here are a few examples of input file lists and the EXACT expected JSON output format.\r\n+\r\n+**Example 1: Single PBR Surface**\r\n+Input:\r\n+[\r\n+  \"Wood_Plank_01_COL_1K.jpg\",\r\n+  \"Wood_Plank_01_NRM_1K.jpg\",\r\n+  \"Wood_Plank_01_ROUGH_1K.jpg\"\r\n+]\r\n+Expected Output:\r\n+[\r\n+  {\r\n+    \"asset_name\": \"Wood_Plank_01\",\r\n+    \"asset_category\": \"Surface\",\r\n+    \"asset_archetype\": \"Wood\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Wood_Plank_01_COL_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"COL\"},\r\n+      {\"input_path\": \"Wood_Plank_01_NRM_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"NRM\"},\r\n+      {\"input_path\": \"Wood_Plank_01_ROUGH_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"ROUGH\"}\r\n+    ]\r\n+  }\r\n+]\r\n+\r\n+**Example 2: Multiple Imperfection Maps**\r\n+Input:\r\n+[\r\n+  \"Smudge_A.tif\",\r\n+  \"Scratch_B.tif\",\r\n+  \"Dust_C.tif\",\r\n+  \"preview.jpg\"\r\n+]\r\n+Expected Output:\r\n+[\r\n+  {\r\n+    \"asset_name\": \"Smudge_A\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Smudge\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Scratch_B\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Scratch\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Dust_C\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Dust\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Dust_C.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Smudge_A\", # Assign extra files to the first asset or a generic asset if needed\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Smudge\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null}\r\n+    ]\r\n+  }\r\n+]\r\n+\r\n+Input File List:\r\n"
                },
                {
                    "date": 1745583943352,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -0,0 +1,91 @@\n+# llm_prototype/prompt_template.txt\r\n+\r\n+You are an expert 3D asset file classifier. Your task is to analyze a list of filenames from a single asset source (which may contain multiple distinct assets) and provide a structured JSON output detailing the identified assets and their file classifications.\r\n+\r\n+Follow these steps:\r\n+\r\n+1.  Analyze the provided list of filenames.\r\n+2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n+3.  For each identified asset group, determine:\r\n+    *   A concise `asset_name`.\r\n+    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n+    *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n+4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n+5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n+6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n+7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n+8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n+\r\n+Input File List:\r\n+{{FILE_LIST_JSON}}\r\n+\r\n+## Examples\r\n+\r\n+Here are a few examples of input file lists and the EXACT expected JSON output format.\r\n+\r\n+**Example 1: Single PBR Surface**\r\n+Input:\r\n+[\r\n+  \"Wood_Plank_01_COL_1K.jpg\",\r\n+  \"Wood_Plank_01_NRM_1K.jpg\",\r\n+  \"Wood_Plank_01_ROUGH_1K.jpg\"\r\n+]\r\n+Expected Output:\r\n+[\r\n+  {\r\n+    \"asset_name\": \"Wood_Plank_01\",\r\n+    \"asset_category\": \"Surface\",\r\n+    \"asset_archetype\": \"Wood\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Wood_Plank_01_COL_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"COL\"},\r\n+      {\"input_path\": \"Wood_Plank_01_NRM_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"NRM\"},\r\n+      {\"input_path\": \"Wood_Plank_01_ROUGH_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"ROUGH\"}\r\n+    ]\r\n+  }\r\n+]\r\n+\r\n+**Example 2: Multiple Imperfection Maps**\r\n+Input:\r\n+[\r\n+  \"Smudge_A.tif\",\r\n+  \"Scratch_B.tif\",\r\n+  \"Dust_C.tif\",\r\n+  \"preview.jpg\"\r\n+]\r\n+Expected Output:\r\n+[\r\n+  {\r\n+    \"asset_name\": \"Smudge_A\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Smudge\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Scratch_B\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Scratch\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Dust_C\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Dust\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"Dust_C.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Smudge_A\", # Assign extra files to the first asset or a generic asset if needed\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Smudge\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null}\r\n+    ]\r\n+  }\r\n+]\r\n+\r\n+Input File List:\r\n"
                },
                {
                    "date": 1745584220392,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -10,12 +10,14 @@\n     *   A concise `asset_name`.\r\n     *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n+4.5. If a file does not belong to a distinct asset group (e.g., preview images, notes), classify it accordingly (\"Extra\", \"Ignored\", \"Unrecognised\") and include it in the \"file_classifications\" list of the *first* asset object in your output.\r\n 5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n 6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n+**The output MUST be a valid JSON list `[...]` containing one object `{...}` for EACH distinct asset identified. EACH asset object MUST include the keys \"asset_name\", \"asset_category\", \"asset_archetype\", and \"file_classifications\".**\r\n \r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n \r\n@@ -88,186 +90,4 @@\n   }\r\n ]\r\n \r\n Input File List:\r\n-# llm_prototype/prompt_template.txt\r\n-\r\n-You are an expert 3D asset file classifier. Your task is to analyze a list of filenames from a single asset source (which may contain multiple distinct assets) and provide a structured JSON output detailing the identified assets and their file classifications.\r\n-\r\n-Follow these steps:\r\n-\r\n-1.  Analyze the provided list of filenames.\r\n-2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n-3.  For each identified asset group, determine:\r\n-    *   A concise `asset_name`.\r\n-    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n-    *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n-4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n-5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n-6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n-7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n-8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n-\r\n-Input File List:\r\n-{{FILE_LIST_JSON}}\r\n-\r\n-## Examples\r\n-\r\n-Here are a few examples of input file lists and the EXACT expected JSON output format.\r\n-\r\n-**Example 1: Single PBR Surface**\r\n-Input:\r\n-[\r\n-  \"Wood_Plank_01_COL_1K.jpg\",\r\n-  \"Wood_Plank_01_NRM_1K.jpg\",\r\n-  \"Wood_Plank_01_ROUGH_1K.jpg\"\r\n-]\r\n-Expected Output:\r\n-[\r\n-  {\r\n-    \"asset_name\": \"Wood_Plank_01\",\r\n-    \"asset_category\": \"Surface\",\r\n-    \"asset_archetype\": \"Wood\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Wood_Plank_01_COL_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"COL\"},\r\n-      {\"input_path\": \"Wood_Plank_01_NRM_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"NRM\"},\r\n-      {\"input_path\": \"Wood_Plank_01_ROUGH_1K.jpg\", \"classification\": \"PBRMap\", \"map_type\": \"ROUGH\"}\r\n-    ]\r\n-  }\r\n-]\r\n-\r\n-**Example 2: Multiple Imperfection Maps**\r\n-Input:\r\n-[\r\n-  \"Smudge_A.tif\",\r\n-  \"Scratch_B.tif\",\r\n-  \"Dust_C.tif\",\r\n-  \"preview.jpg\"\r\n-]\r\n-Expected Output:\r\n-[\r\n-  {\r\n-    \"asset_name\": \"Smudge_A\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Smudge\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Scratch_B\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Scratch\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Dust_C\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Dust\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Dust_C.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Smudge_A\", # Assign extra files to the first asset or a generic asset if needed\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Smudge\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null}\r\n-    ]\r\n-  }\r\n-]\r\n-\r\n-Input File List:\r\n-# llm_prototype/prompt_template.txt\r\n-\r\n-You are an expert 3D asset file classifier. Your task is to analyze a list of filenames from a single asset source (which may contain multiple distinct assets) and provide a structured JSON output detailing the identified assets and their file classifications.\r\n-\r\n-Follow these steps:\r\n-\r\n-1.  Analyze the provided list of filenames.\r\n-2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n-3.  For each identified asset group, determine:\r\n-    *   A concise `asset_name`.\r\n-    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n-    *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n-4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n-5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n-6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n-7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n-8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n-\r\n-Input File List:\r\n-{{FILE_LIST_JSON}}\r\n-\r\n-## Examples\r\n-\r\n-Here are a few examples of input file lists and the EXACT expected JSON output format.\r\n-\r\n-**Example 1: Single PBR Surface**\r\n-Input:\r\n-[\r\n-  \"Wood_Plank_01_COL_1K.jpg\",\r\n-  \"Wood_Plank_01_NRM_1K.jpg\",\r\n-  \"Wood_Plank_01_ROUGH_1K.jpg\"\r\n-]\r\n-Expected Output:\r\n-[\r\n-  {\r\n-    \"asset_name\": \"Wood_Plank_01\",\r\n-    \"asset_category\": \"Surface\",\r\n-    \"asset_archetype\": \"Wood\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Wood_Plank_01_COL_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"COL\"},\r\n-      {\"input_path\": \"Wood_Plank_01_NRM_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"NRM\"},\r\n-      {\"input_path\": \"Wood_Plank_01_ROUGH_1K.jpg\", \"classification\": \"Map\", \"map_type\": \"ROUGH\"}\r\n-    ]\r\n-  }\r\n-]\r\n-\r\n-**Example 2: Multiple Imperfection Maps**\r\n-Input:\r\n-[\r\n-  \"Smudge_A.tif\",\r\n-  \"Scratch_B.tif\",\r\n-  \"Dust_C.tif\",\r\n-  \"preview.jpg\"\r\n-]\r\n-Expected Output:\r\n-[\r\n-  {\r\n-    \"asset_name\": \"Smudge_A\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Smudge\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Scratch_B\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Scratch\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Dust_C\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Dust\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Dust_C.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Smudge_A\", # Assign extra files to the first asset or a generic asset if needed\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Smudge\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null}\r\n-    ]\r\n-  }\r\n-]\r\n-\r\n-Input File List:\r\n"
                },
                {
                    "date": 1745584498378,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -15,9 +15,9 @@\n 5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n 6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n-**The output MUST be a valid JSON list `[...]` containing one object `{...}` for EACH distinct asset identified. EACH asset object MUST include the keys \"asset_name\", \"asset_category\", \"asset_archetype\", and \"file_classifications\".**\r\n+**The output MUST be a valid JSON list `[...]` containing one object `{...}` for EACH distinct asset identified. EACH asset object MUST include the keys \"asset_name\", \"asset_category\", \"asset_archetype\", and \"file_classifications\". DO NOT include markdown code fences (```json) or comments (`//` or `#`) in the JSON output.**\r\n \r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n \r\n"
                },
                {
                    "date": 1745584576561,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -15,9 +15,9 @@\n 5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n 6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n-**The output MUST be a valid JSON list `[...]` containing one object `{...}` for EACH distinct asset identified. EACH asset object MUST include the keys \"asset_name\", \"asset_category\", \"asset_archetype\", and \"file_classifications\". DO NOT include markdown code fences (```json) or comments (`//` or `#`) in the JSON output.**\r\n+**STRICT INSTRUCTION: Output ONLY the final JSON list structure after the </thinking> tag. The output MUST be a valid JSON list `[...]` containing one object `{...}` for EACH distinct asset identified. EACH asset object MUST include the keys \"asset_name\", \"asset_category\", \"asset_archetype\", and \"file_classifications\". ABSOLUTELY NO markdown code fences (```json) or comments (`//` or `#`) are allowed in the JSON output.**\r\n \r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n \r\n"
                },
                {
                    "date": 1745585712622,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -7,14 +7,14 @@\n 1.  Analyze the provided list of filenames.\r\n 2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n 3.  For each identified asset group, determine:\r\n     *   A concise `asset_name`.\r\n-    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}})\r\n+    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}}). Note: The 'Imperfection' category typically contains a single map file.\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n 4.5. If a file does not belong to a distinct asset group (e.g., preview images, notes), classify it accordingly (\"Extra\", \"Ignored\", \"Unrecognised\") and include it in the \"file_classifications\" list of the *first* asset object in your output.\r\n 5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n-6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering).\r\n+6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following exact strings: {{EXPECTED_MAP_TYPES}}. If the classification is not \"Map\", the `map_type` should be `null`. Use the most appropriate map type from the list. For imperfection maps, the map_type should typically be \"utility\". Common synonyms for these map types include: COL (Color, Diffuse, Albedo), NRM (Normal), ROUGH (Roughness), METAL (Metallic), AO (Ambient Occlusion), DISP (Displacement, Height), REFL (Reflection), MASK (Mask, Alpha, Opacity), SSS (Subsurface Scattering), utility (Utility, Data, Helper).\r\n 7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n 8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. **Ensure the JSON is complete and valid.** Do not include any other text outside the JSON.\r\n **STRICT INSTRUCTION: Output ONLY the final JSON list structure after the </thinking> tag. The output MUST be a valid JSON list `[...]` containing one object `{...}` for EACH distinct asset identified. EACH asset object MUST include the keys \"asset_name\", \"asset_category\", \"asset_archetype\", and \"file_classifications\". ABSOLUTELY NO markdown code fences (```json) or comments (`//` or `#`) are allowed in the JSON output.**\r\n \r\n@@ -48,46 +48,27 @@\n \r\n **Example 2: Multiple Imperfection Maps**\r\n Input:\r\n [\r\n-  \"Smudge_A.tif\",\r\n-  \"Scratch_B.tif\",\r\n-  \"Dust_C.tif\",\r\n-  \"preview.jpg\"\r\n+  \"DG-01-Smudges.tif\",\r\n+  \"DG-02-Scratches.tif\",\r\n+  \"DG-03-Smudges-Scratches.tif\",\r\n+  \"preview.jpg\",\r\n+  \"notes.txt\"\r\n ]\r\n Expected Output:\r\n [\r\n   {\r\n-    \"asset_name\": \"Smudge_A\",\r\n+    \"asset_name\": \"Imperfections\",\r\n     \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Smudge\",\r\n+    \"asset_archetype\": \"Mixed\",\r\n     \"file_classifications\": [\r\n-      {\"input_path\": \"Smudge_A.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n+      {\"input_path\": \"DG-01-Smudges.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"},\r\n+      {\"input_path\": \"DG-02-Scratches.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"},\r\n+      {\"input_path\": \"DG-03-Smudges-Scratches.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"},\r\n+      {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null},\r\n+      {\"input_path\": \"notes.txt\", \"classification\": \"Ignored\", \"map_type\": null}\r\n     ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Scratch_B\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Scratch\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Scratch_B.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Dust_C\",\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Dust\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"Dust_C.tif\", \"classification\": \"Map\", \"map_type\": \"MASK\"}\r\n-    ]\r\n-  },\r\n-  {\r\n-    \"asset_name\": \"Smudge_A\", # Assign extra files to the first asset or a generic asset if needed\r\n-    \"asset_category\": \"Imperfection\",\r\n-    \"asset_archetype\": \"Smudge\",\r\n-    \"file_classifications\": [\r\n-      {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null}\r\n-    ]\r\n   }\r\n ]\r\n \r\n Input File List:\r\n"
                },
                {
                    "date": 1745586048062,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -7,9 +7,9 @@\n 1.  Analyze the provided list of filenames.\r\n 2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n 3.  For each identified asset group, determine:\r\n     *   A concise `asset_name`.\r\n-    *   The `asset_category`. This must be one of the following exact strings: Model, Surface, Decal, ATLAS, Imperfection. (Using placeholder: {{CATEGORIES_PLACEHOLDER}}). Note: The 'Imperfection' category typically contains a single map file.\r\n+    *   The `asset_category`. This must be one of the following exact strings: {{CATEGORIES_PLACEHOLDER}}. Refer to the examples below for common synonyms: {{CATEGORY_EXAMPLES}}. Note: The 'Imperfection' category typically contains a single map file.\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n 4.5. If a file does not belong to a distinct asset group (e.g., preview images, notes), classify it accordingly (\"Extra\", \"Ignored\", \"Unrecognised\") and include it in the \"file_classifications\" list of the *first* asset object in your output.\r\n 5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n@@ -20,8 +20,11 @@\n \r\n Input File List:\r\n {{FILE_LIST_JSON}}\r\n \r\n+Category Examples:\r\n+{{CATEGORY_EXAMPLES}}\r\n+\r\n ## Examples\r\n \r\n Here are a few examples of input file lists and the EXACT expected JSON output format.\r\n \r\n"
                },
                {
                    "date": 1745586118651,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -7,9 +7,9 @@\n 1.  Analyze the provided list of filenames.\r\n 2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n 3.  For each identified asset group, determine:\r\n     *   A concise `asset_name`.\r\n-    *   The `asset_category`. This must be one of the following exact strings: {{CATEGORIES_PLACEHOLDER}}. Refer to the examples below for common synonyms: {{CATEGORY_EXAMPLES}}. Note: The 'Imperfection' category typically contains a single map file.\r\n+    *   The `asset_category`. This must be one of the following exact strings: {{CATEGORIES_PLACEHOLDER}}. Refer to the descriptions below for guidance: {{CATEGORY_EXAMPLES}}. Note: The 'Imperfection' category typically contains a single map file.\r\n     *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n 4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n 4.5. If a file does not belong to a distinct asset group (e.g., preview images, notes), classify it accordingly (\"Extra\", \"Ignored\", \"Unrecognised\") and include it in the \"file_classifications\" list of the *first* asset object in your output.\r\n 5.  For each file, determine its `classification`. This must be one of the following exact strings: Map, Model, Extra, Ignored, Unrecognised. (Using placeholder: {{CLASSIFICATIONS_PLACEHOLDER}})\r\n"
                },
                {
                    "date": 1745586699026,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -60,15 +60,36 @@\n ]\r\n Expected Output:\r\n [\r\n   {\r\n-    \"asset_name\": \"Imperfections\",\r\n+    \"asset_name\": \"Smudges\",\r\n     \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Smudge\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"DG-01-Smudges.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Scratches\",\r\n+    \"asset_category\": \"Imperfection\",\r\n+    \"asset_archetype\": \"Scratch\",\r\n+    \"file_classifications\": [\r\n+      {\"input_path\": \"DG-02-Scratches.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Smudges-Scratches\",\r\n+    \"asset_category\": \"Imperfection\",\r\n     \"asset_archetype\": \"Mixed\",\r\n     \"file_classifications\": [\r\n-      {\"input_path\": \"DG-01-Smudges.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"},\r\n-      {\"input_path\": \"DG-02-Scratches.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"},\r\n-      {\"input_path\": \"DG-03-Smudges-Scratches.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"},\r\n+      {\"input_path\": \"DG-03-Smudges-Scratches.tif\", \"classification\": \"Map\", \"map_type\": \"utility\"}\r\n+    ]\r\n+  },\r\n+  {\r\n+    \"asset_name\": \"Extra Files\",\r\n+    \"asset_category\": \"Extra\",\r\n+    \"asset_archetype\": null,\r\n+    \"file_classifications\": [\r\n       {\"input_path\": \"preview.jpg\", \"classification\": \"Extra\", \"map_type\": null},\r\n       {\"input_path\": \"notes.txt\", \"classification\": \"Ignored\", \"map_type\": null}\r\n     ]\r\n   }\r\n"
                }
            ],
            "date": 1745578477575,
            "name": "Commit-0",
            "content": "# llm_prototype/prompt_template.txt\r\n\r\nYou are an expert 3D asset file classifier. Your task is to analyze a list of filenames from a single asset source (which may contain multiple distinct assets) and provide a structured JSON output detailing the identified assets and their file classifications.\r\n\r\nFollow these steps:\r\n\r\n1.  Analyze the provided list of filenames.\r\n2.  Identify distinct asset groups within the list based on naming patterns, file types, or other logical groupings.\r\n3.  For each identified asset group, determine:\r\n    *   A concise `asset_name`.\r\n    *   The `asset_category`. This must be one of the following: {{CATEGORIES_PLACEHOLDER}}.\r\n    *   The `asset_archetype` (e.g., Wood, Metal, Fabric, Concrete, Smudge, Scratch, etc.).\r\n4.  For each file in the original input list, determine which asset group it belongs to (if any).\r\n5.  For each file, determine its `classification`. This must be one of the following: {{CLASSIFICATIONS_PLACEHOLDER}}.\r\n6.  If the classification is \"Map\", determine the `map_type`. This must be one of the following: {{MAP_TYPES_PLACEHOLDER}}. If the classification is not \"Map\", the `map_type` should be `null`.\r\n7.  Output your reasoning process within <thinking> and </thinking> tags. Explain how you identified the asset groups and classified the files.\r\n8.  After the </thinking> tag, output ONLY the final JSON list structure based on your analysis. Do not include any other text outside the JSON.\r\n\r\nInput File List:\r\n{{FILE_LIST_JSON}}\r\n\r\nExpected Output JSON Schema (Output ONLY this structure after the thinking block):\r\n{{OUTPUT_SCHEMA_PLACEHOLDER}}"
        }
    ]
}