{
  "schema_version": "ATIF-v1.5",
  "session_id": "019f0036-92c4-77a3-b5f4-48c1da060d43",
  "agent": {
    "name": "codex",
    "version": "0.115.0",
    "model_name": "gpt-5.5",
    "extra": {
      "originator": "codex_exec",
      "cwd": "/app"
    }
  },
  "steps": [
    {
      "step_id": 1,
      "timestamp": "2026-06-25T19:16:44.388Z",
      "source": "system",
      "message": "<permissions instructions>\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n</permissions instructions><skills_instructions>\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- openai-docs: Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.\n  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.\n  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.\n  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.\n  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n</skills_instructions>"
    },
    {
      "step_id": 2,
      "timestamp": "2026-06-25T19:16:44.388Z",
      "source": "user",
      "message": "<environment_context>\n  <cwd>/app</cwd>\n  <shell>bash</shell>\n  <current_date>2026-06-25</current_date>\n  <timezone>Etc/UTC</timezone>\n</environment_context>"
    },
    {
      "step_id": 3,
      "timestamp": "2026-06-25T19:16:44.388Z",
      "source": "user",
      "message": "Read the image file `/app/image.png` to answer the question.\n\nThis image was taken in 2022. What city was this image taken in?\n\nWrite your answer to `/logs/artifacts/response.txt` in exactly this format:\n\n```\nExplanation: <your reasoning>\nAnswer: <your final answer>\nConfidence: <your confidence as a percentage, e.g. 85%>\n```\n\nYou have up to 30 minutes (1800 seconds) to complete this task. Do not cheat by using online solutions or hints specific to this task.\n"
    },
    {
      "step_id": 4,
      "timestamp": "2026-06-25T19:16:47.696Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "I\u2019ll inspect the local image and any embedded metadata, then write the requested response file."
    },
    {
      "step_id": 5,
      "timestamp": "2026-06-25T19:16:47.881Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed view_image call_7CfenyMbz5jRFRO6IGdcYNVJ",
      "tool_calls": [
        {
          "tool_call_id": "call_7CfenyMbz5jRFRO6IGdcYNVJ",
          "function_name": "view_image",
          "arguments": {
            "path": "/app/image.png"
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_7CfenyMbz5jRFRO6IGdcYNVJ",
            "content": "[{'type': 'input_image', 'image_url': 'data:image/png;base64,[encoded data, 916516 characters]'}]"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"path\":\"/app/image.png\"}"
      }
    },
    {
      "step_id": 6,
      "timestamp": "2026-06-25T19:16:52.317Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "The scene suggests a Pacific Northwest beach; I\u2019ll check metadata and image details for any local clues."
    },
    {
      "step_id": 7,
      "timestamp": "2026-06-25T19:16:53.110Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_XT5TSKxuS9LMjIBX9MFZmbud",
      "tool_calls": [
        {
          "tool_call_id": "call_XT5TSKxuS9LMjIBX9MFZmbud",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "file /app/image.png && exiftool /app/image.png 2>/dev/null || identify -verbose /app/image.png | head -120",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 12000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_XT5TSKxuS9LMjIBX9MFZmbud",
            "content": "Command: /bin/bash -lc 'file /app/image.png && exiftool /app/image.png 2>/dev/null || identify -verbose /app/image.png | head -120'\nChunk ID: e7aa71\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 23\nOutput:\n/bin/bash: line 1: file: command not found\n/bin/bash: line 1: identify: command not found\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"file /app/image.png && exiftool /app/image.png 2>/dev/null || identify -verbose /app/image.png | head -120\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}"
      }
    },
    {
      "step_id": 8,
      "timestamp": "2026-06-25T19:16:55.931Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Those utilities aren\u2019t installed, so I\u2019ll use Python to inspect the PNG metadata."
    },
    {
      "step_id": 9,
      "timestamp": "2026-06-25T19:16:56.286Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_4ejdZejVBtcHhjIEqf9OaGYq",
      "tool_calls": [
        {
          "tool_call_id": "call_4ejdZejVBtcHhjIEqf9OaGYq",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "python3 - <<'PY'\nfrom PIL import Image\nimg=Image.open('/app/image.png')\nprint(img.format, img.size, img.mode)\nprint('info keys:', img.info.keys())\nfor k,v in img.info.items():\n    print(repr(k), repr(v)[:500])\nPY",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 12000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_4ejdZejVBtcHhjIEqf9OaGYq",
            "content": "Command: /bin/bash -lc \"python3 - <<'PY'\nfrom PIL import Image\nimg=Image.open('/app/image.png')\nprint(img.format, img.size, img.mode)\nprint('info keys:', img.info.keys())\nfor k,v in img.info.items():\n    print(repr(k), repr(v)[:500])\nPY\"\nChunk ID: d91a9d\nWall time: 0.0000 seconds\nProcess exited with code 1\nOriginal token count: 29\nOutput:\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'PIL'\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"python3 - <<'PY'\\nfrom PIL import Image\\nimg=Image.open('/app/image.png')\\nprint(img.format, img.size, img.mode)\\nprint('info keys:', img.info.keys())\\nfor k,v in img.info.items():\\n    print(repr(k), repr(v)[:500])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}"
      }
    },
    {
      "step_id": 10,
      "timestamp": "2026-06-25T19:16:59.547Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "No image library either; I\u2019ll parse the PNG chunks directly for hidden text metadata."
    },
    {
      "step_id": 11,
      "timestamp": "2026-06-25T19:17:00.728Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_9C62PP8rGQ1q9AsRtplKUXWP",
      "tool_calls": [
        {
          "tool_call_id": "call_9C62PP8rGQ1q9AsRtplKUXWP",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "python3 - <<'PY'\nimport struct, zlib\npath='/app/image.png'\nwith open(path,'rb') as f:\n    data=f.read()\nprint(data[:8])\npos=8\nwhile pos+8<=len(data):\n    length=struct.unpack('>I', data[pos:pos+4])[0]\n    ctype=data[pos+4:pos+8].decode('latin1')\n    chunk=data[pos+8:pos+8+length]\n    print(ctype, length)\n    if ctype in ('tEXt','zTXt','iTXt'):\n        print(chunk[:1000])\n        if ctype=='zTXt':\n            # keyword\\0 compression method then compressed\n            try:\n                key, rest = chunk.split(b'\\0',1)\n                method = rest[0]\n                comp = rest[1:]\n                print('key', key, 'method', method, 'text', zlib.decompress(comp)[:1000])\n            except Exception as e:\n                print('zTXt err',e)\n    pos += 12+length\nPY",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 12000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_9C62PP8rGQ1q9AsRtplKUXWP",
            "content": "Command: /bin/bash -lc \"python3 - <<'PY'\nimport struct, zlib\npath='/app/image.png'\nwith open(path,'rb') as f:\n    data=f.read()\nprint(data[:8])\npos=8\nwhile pos+8<=len(data):\n    length=struct.unpack('>I', data[pos:pos+4])[0]\n    ctype=data[pos+4:pos+8].decode('latin1')\n    chunk=data[pos+8:pos+8+length]\n    print(ctype, length)\n    if ctype in ('tEXt','zTXt','iTXt'):\n        print(chunk[:1000])\n        if ctype=='zTXt':\n            # keyword\\\\0 compression method then compressed\n            try:\n                key, rest = chunk.split(b'\\\\0',1)\n                method = rest[0]\n                comp = rest[1:]\n                print('key', key, 'method', method, 'text', zlib.decompress(comp)[:1000])\n            except Exception as e:\n                print('zTXt err',e)\n    pos += 12+length\nPY\"\nChunk ID: 57d580\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 284\nOutput:\nb'\\x89PNG\\r\\n\\x1a\\n'\nIHDR 13\nsRGB 1\neXIf 150\npHYs 9\niTXt 470\nb'XML:com.adobe.xmp\\x00\\x00\\x00\\x00\\x00<x:xmpmeta xmlns:x=\"adobe:ns:meta/\" x:xmptk=\"XMP Core 6.0.0\">\\n   <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\\n      <rdf:Description rdf:about=\"\"\\n            xmlns:exif=\"http://ns.adobe.com/exif/1.0/\">\\n         <exif:PixelYDimension>910</exif:PixelYDimension>\\n         <exif:PixelXDimension>926</exif:PixelXDimension>\\n         <exif:UserComment>Screenshot</exif:UserComment>\\n      </rdf:Description>\\n   </rdf:RDF>\\n</x:xmpmeta>\\n'\niDOT 28\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 2834\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 16384\nIDAT 14842\nIEND 0\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"python3 - <<'PY'\\nimport struct, zlib\\npath='/app/image.png'\\nwith open(path,'rb') as f:\\n    data=f.read()\\nprint(data[:8])\\npos=8\\nwhile pos+8<=len(data):\\n    length=struct.unpack('>I', data[pos:pos+4])[0]\\n    ctype=data[pos+4:pos+8].decode('latin1')\\n    chunk=data[pos+8:pos+8+length]\\n    print(ctype, length)\\n    if ctype in ('tEXt','zTXt','iTXt'):\\n        print(chunk[:1000])\\n        if ctype=='zTXt':\\n            # keyword\\\\0 compression method then compressed\\n            try:\\n                key, rest = chunk.split(b'\\\\0',1)\\n                method = rest[0]\\n                comp = rest[1:]\\n                print('key', key, 'method', method, 'text', zlib.decompress(comp)[:1000])\\n            except Exception as e:\\n                print('zTXt err',e)\\n    pos += 12+length\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}"
      }
    },
    {
      "step_id": 12,
      "timestamp": "2026-06-25T19:17:11.073Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "I\u2019ll brighten the image locally to reveal shoreline details without using web lookup."
    },
    {
      "step_id": 13,
      "timestamp": "2026-06-25T19:17:11.381Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_Yvaig5fTOgXDu6NuABcCkx1J",
      "tool_calls": [
        {
          "tool_call_id": "call_Yvaig5fTOgXDu6NuABcCkx1J",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "python3 - <<'PY'\nmods=['cv2','numpy','matplotlib','imageio','skimage']\nfor m in mods:\n    try:\n        __import__(m)\n        print(m,'ok')\n    except Exception as e:\n        print(m,'no',type(e).__name__)\nPY",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 2000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_Yvaig5fTOgXDu6NuABcCkx1J",
            "content": "Command: /bin/bash -lc \"python3 - <<'PY'\nmods=['cv2','numpy','matplotlib','imageio','skimage']\nfor m in mods:\n    try:\n        __import__(m)\n        print(m,'ok')\n    except Exception as e:\n        print(m,'no',type(e).__name__)\nPY\"\nChunk ID: b0e0f0\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 38\nOutput:\ncv2 no ModuleNotFoundError\nnumpy no ModuleNotFoundError\nmatplotlib no ModuleNotFoundError\nimageio no ModuleNotFoundError\nskimage no ModuleNotFoundError\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"python3 - <<'PY'\\nmods=['cv2','numpy','matplotlib','imageio','skimage']\\nfor m in mods:\\n    try:\\n        __import__(m)\\n        print(m,'ok')\\n    except Exception as e:\\n        print(m,'no',type(e).__name__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}"
      }
    },
    {
      "step_id": 14,
      "timestamp": "2026-06-25T19:17:14.440Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "I\u2019ll see what basic image tools are available for local enhancement."
    },
    {
      "step_id": 15,
      "timestamp": "2026-06-25T19:17:15.218Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_W3JjsJQiuPN7SruLVtGl56nl",
      "tool_calls": [
        {
          "tool_call_id": "call_W3JjsJQiuPN7SruLVtGl56nl",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "for c in convert magick ffmpeg ffprobe gm pngtopnm pnmtopng python3; do command -v $c && echo $c; done",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 2000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_W3JjsJQiuPN7SruLVtGl56nl",
            "content": "Command: /bin/bash -lc 'for c in convert magick ffmpeg ffprobe gm pngtopnm pnmtopng python3; do command -v $c && echo $c; done'\nChunk ID: 455c44\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 8\nOutput:\n/usr/local/bin/python3\npython3\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"for c in convert magick ffmpeg ffprobe gm pngtopnm pnmtopng python3; do command -v $c && echo $c; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}"
      }
    },
    {
      "step_id": 16,
      "timestamp": "2026-06-25T19:17:18.963Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "I\u2019ll decode the PNG with a small script and generate brighter crops for closer visual inspection."
    },
    {
      "step_id": 17,
      "timestamp": "2026-06-25T19:17:30.095Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_ko9TzHr5y0WL5Q1boOgahfsy",
      "tool_calls": [
        {
          "tool_call_id": "call_ko9TzHr5y0WL5Q1boOgahfsy",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "python3 - <<'PY'\nimport struct,zlib,os,math\npath='/app/image.png'\ndata=open(path,'rb').read()\npos=8\nidat=b''\nwidth=height=bit_depth=color_type=None\nwhile pos < len(data):\n    length=struct.unpack('>I',data[pos:pos+4])[0]\n    ctype=data[pos+4:pos+8]\n    chunk=data[pos+8:pos+8+length]\n    if ctype==b'IHDR':\n        width,height,bit_depth,color_type,comp,flt,inter=struct.unpack('>IIBBBBB',chunk)\n        print(width,height,bit_depth,color_type,comp,flt,inter)\n    elif ctype==b'IDAT':\n        idat += chunk\n    pos += 12+length\nraw=zlib.decompress(idat)\nif bit_depth!=8 or color_type not in (2,6):\n    raise SystemExit('unsupported')\nchannels=3 if color_type==2 else 4\nstride=width*channels\nrows=[]\ni=0\nprev=[0]*stride\nfor y in range(height):\n    f=raw[i]; i+=1\n    cur=list(raw[i:i+stride]); i+=stride\n    recon=[0]*stride\n    for x in range(stride):\n        a=recon[x-channels] if x>=channels else 0\n        b=prev[x]\n        c=prev[x-channels] if x>=channels else 0\n        val=cur[x]\n        if f==0: r=val\n        elif f==1: r=(val+a)&255\n        elif f==2: r=(val+b)&255\n        elif f==3: r=(val+((a+b)//2))&255\n        elif f==4:\n            p=a+b-c\n            pa=abs(p-a); pb=abs(p-b); pc=abs(p-c)\n            pr=a if pa<=pb and pa<=pc else (b if pb<=pc else c)\n            r=(val+pr)&255\n        else: raise SystemExit('bad filter')\n        recon[x]=r\n    rows.append(recon)\n    prev=recon\n\ndef write_png(out, rows2, w, h, channels=3):\n    ct=2 if channels==3 else 6\n    raw2=b''.join(bytes([0])+bytes(row) for row in rows2)\n    def chunk(t,d):\n        return struct.pack('>I',len(d))+t+d+struct.pack('>I',zlib.crc32(t+d)&0xffffffff)\n    png=b'\\x89PNG\\r\\n\\x1a\\n'+chunk(b'IHDR',struct.pack('>IIBBBBB',w,h,8,ct,0,0,0))+chunk(b'IDAT',zlib.compress(raw2,9))+chunk(b'IEND',b'')\n    open(out,'wb').write(png)\n\ndef transform(row, gain=2.5, gamma=0.7):\n    out=[]\n    for j in range(0,len(row),channels):\n        for k in range(3):\n            v=row[j+k]/255.0\n            # lift shadows and gamma\n            nv=min(1.0,(v**gamma)*gain)\n            out.append(int(nv*255+0.5))\n        if channels==4:\n            pass\n    return out\n# full brightened RGB\nbright=[transform(r,2.0,0.6) for r in rows]\nwrite_png('/app/bright.png', bright, width, height, 3)\n# crop right beach/shore, bright and upsample 2x maybe nearest\ndef crop_resize(x0,y0,x1,y1,scale,out):\n    crop=[]\n    for y in range(y0,y1):\n        src=[]\n        row=rows[y]\n        for x in range(x0,x1):\n            idx=x*channels\n            for k in range(3):\n                v=row[idx+k]/255.0\n                nv=min(1.0,(v**0.5)*3.0)\n                src.append(int(nv*255+0.5))\n        # nearest scale horizontal\n        scaled=[]\n        for x in range(x1-x0):\n            pix=src[x*3:x*3+3]\n            for _ in range(scale): scaled += pix\n        for _ in range(scale): crop.append(scaled)\n    write_png(out,crop,(x1-x0)*scale,(y1-y0)*scale,3)\n\ncrop_resize(350,120,926,650,2,'/app/crop_beach.png')\ncrop_resize(0,0,500,350,2,'/app/crop_water.png')\ncrop_resize(0,500,926,910,2,'/app/crop_fence.png')\nprint('wrote /app/bright.png and crops')\nPY",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 4000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_ko9TzHr5y0WL5Q1boOgahfsy",
            "content": "Command: /bin/bash -lc \"python3 - <<'PY'\nimport struct,zlib,os,math\npath='/app/image.png'\ndata=open(path,'rb').read()\npos=8\nidat=b''\nwidth=height=bit_depth=color_type=None\nwhile pos < len(data):\n    length=struct.unpack('>I',data[pos:pos+4])[0]\n    ctype=data[pos+4:pos+8]\n    chunk=data[pos+8:pos+8+length]\n    if ctype==b'IHDR':\n        width,height,bit_depth,color_type,comp,flt,inter=struct.unpack('>IIBBBBB',chunk)\n        print(width,height,bit_depth,color_type,comp,flt,inter)\n    elif ctype==b'IDAT':\n        idat += chunk\n    pos += 12+length\nraw=zlib.decompress(idat)\nif bit_depth\"'!=8 or color_type not in (2,6):\n    raise SystemExit('\"'unsupported')\nchannels=3 if color_type==2 else 4\nstride=width*channels\nrows=[]\ni=0\nprev=[0]*stride\nfor y in range(height):\n    f=raw[i]; i+=1\n    cur=list(raw[i:i+stride]); i+=stride\n    recon=[0]*stride\n    for x in range(stride):\n        a=recon[x-channels] if x>=channels else 0\n        b=prev[x]\n        c=prev[x-channels] if x>=channels else 0\n        val=cur[x]\n        if f==0: r=val\n        elif f==1: r=(val+a)&255\n        elif f==2: r=(val+b)&255\n        elif f==3: r=(val+((a+b)//2))&255\n        elif f==4:\n            p=a+b-c\n            pa=abs(p-a); pb=abs(p-b); pc=abs(p-c)\n            pr=a if pa<=pb and pa<=pc else (b if pb<=pc else c)\n            r=(val+pr)&255\n        else: raise SystemExit('bad filter')\n        recon[x]=r\n    rows.append(recon)\n    prev=recon\n\ndef write_png(out, rows2, w, h, channels=3):\n    ct=2 if channels==3 else 6\n    raw2=b''.join(bytes([0])+bytes(row) for row in rows2)\n    def chunk(t,d):\n        return struct.pack('>I',len(d))+t+d+struct.pack('>I',zlib.crc32(t+d)&0xffffffff)\n    png=b'\\\\x89PNG\\\\r\\\\n\\\\x1a\\\\n'+chunk(b'IHDR',struct.pack('>IIBBBBB',w,h,8,ct,0,0,0))+chunk(b'IDAT',zlib.compress(raw2,9))+chunk(b'IEND',b'')\n    open(out,'wb').write(png)\n\ndef transform(row, gain=2.5, gamma=0.7):\n    out=[]\n    for j in range(0,len(row),channels):\n        for k in range(3):\n            v=row[j+k]/255.0\n            # lift shadows and gamma\n            nv=min(1.0,(v**gamma)*gain)\n            out.append(int(nv*255+0.5))\n        if channels==4:\n            pass\n    return out\n# full brightened RGB\nbright=[transform(r,2.0,0.6) for r in rows]\nwrite_png('/app/bright.png', bright, width, height, 3)\n# crop right beach/shore, bright and upsample 2x maybe nearest\ndef crop_resize(x0,y0,x1,y1,scale,out):\n    crop=[]\n    for y in range(y0,y1):\n        src=[]\n        row=rows[y]\n        for x in range(x0,x1):\n            idx=x*channels\n            for k in range(3):\n                v=row[idx+k]/255.0\n                nv=min(1.0,(v**0.5)*3.0)\n                src.append(int(nv*255+0.5))\n        # nearest scale horizontal\n        scaled=[]\n        for x in range(x1-x0):\n            pix=src[x*3:x*3+3]\n            for _ in range(scale): scaled += pix\n        for _ in range(scale): crop.append(scaled)\n    write_png(out,crop,(x1-x0)*scale,(y1-y0)*scale,3)\n\ncrop_resize(350,120,926,650,2,'/app/crop_beach.png')\ncrop_resize(0,0,500,350,2,'/app/crop_water.png')\ncrop_resize(0,500,926,910,2,'/app/crop_fence.png')\nprint('wrote /app/bright.png and crops')\nPY\"\nChunk ID: 09624b\nWall time: 1.0004 seconds\nProcess running with session ID 36498\nOriginal token count: 0\nOutput:\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"python3 - <<'PY'\\nimport struct,zlib,os,math\\npath='/app/image.png'\\ndata=open(path,'rb').read()\\npos=8\\nidat=b''\\nwidth=height=bit_depth=color_type=None\\nwhile pos < len(data):\\n    length=struct.unpack('>I',data[pos:pos+4])[0]\\n    ctype=data[pos+4:pos+8]\\n    chunk=data[pos+8:pos+8+length]\\n    if ctype==b'IHDR':\\n        width,height,bit_depth,color_type,comp,flt,inter=struct.unpack('>IIBBBBB',chunk)\\n        print(width,height,bit_depth,color_type,comp,flt,inter)\\n    elif ctype==b'IDAT':\\n        idat += chunk\\n    pos += 12+length\\nraw=zlib.decompress(idat)\\nif bit_depth!=8 or color_type not in (2,6):\\n    raise SystemExit('unsupported')\\nchannels=3 if color_type==2 else 4\\nstride=width*channels\\nrows=[]\\ni=0\\nprev=[0]*stride\\nfor y in range(height):\\n    f=raw[i]; i+=1\\n    cur=list(raw[i:i+stride]); i+=stride\\n    recon=[0]*stride\\n    for x in range(stride):\\n        a=recon[x-channels] if x>=channels else 0\\n        b=prev[x]\\n        c=prev[x-channels] if x>=channels else 0\\n        val=cur[x]\\n        if f==0: r=val\\n        elif f==1: r=(val+a)&255\\n        elif f==2: r=(val+b)&255\\n        elif f==3: r=(val+((a+b)//2))&255\\n        elif f==4:\\n            p=a+b-c\\n            pa=abs(p-a); pb=abs(p-b); pc=abs(p-c)\\n            pr=a if pa<=pb and pa<=pc else (b if pb<=pc else c)\\n            r=(val+pr)&255\\n        else: raise SystemExit('bad filter')\\n        recon[x]=r\\n    rows.append(recon)\\n    prev=recon\\n\\ndef write_png(out, rows2, w, h, channels=3):\\n    ct=2 if channels==3 else 6\\n    raw2=b''.join(bytes([0])+bytes(row) for row in rows2)\\n    def chunk(t,d):\\n        return struct.pack('>I',len(d))+t+d+struct.pack('>I',zlib.crc32(t+d)&0xffffffff)\\n    png=b'\\\\x89PNG\\\\r\\\\n\\\\x1a\\\\n'+chunk(b'IHDR',struct.pack('>IIBBBBB',w,h,8,ct,0,0,0))+chunk(b'IDAT',zlib.compress(raw2,9))+chunk(b'IEND',b'')\\n    open(out,'wb').write(png)\\n\\ndef transform(row, gain=2.5, gamma=0.7):\\n    out=[]\\n    for j in range(0,len(row),channels):\\n        for k in range(3):\\n            v=row[j+k]/255.0\\n            # lift shadows and gamma\\n            nv=min(1.0,(v**gamma)*gain)\\n            out.append(int(nv*255+0.5))\\n        if channels==4:\\n            pass\\n    return out\\n# full brightened RGB\\nbright=[transform(r,2.0,0.6) for r in rows]\\nwrite_png('/app/bright.png', bright, width, height, 3)\\n# crop right beach/shore, bright and upsample 2x maybe nearest\\ndef crop_resize(x0,y0,x1,y1,scale,out):\\n    crop=[]\\n    for y in range(y0,y1):\\n        src=[]\\n        row=rows[y]\\n        for x in range(x0,x1):\\n            idx=x*channels\\n            for k in range(3):\\n                v=row[idx+k]/255.0\\n                nv=min(1.0,(v**0.5)*3.0)\\n                src.append(int(nv*255+0.5))\\n        # nearest scale horizontal\\n        scaled=[]\\n        for x in range(x1-x0):\\n            pix=src[x*3:x*3+3]\\n            for _ in range(scale): scaled += pix\\n        for _ in range(scale): crop.append(scaled)\\n    write_png(out,crop,(x1-x0)*scale,(y1-y0)*scale,3)\\n\\ncrop_resize(350,120,926,650,2,'/app/crop_beach.png')\\ncrop_resize(0,0,500,350,2,'/app/crop_water.png')\\ncrop_resize(0,500,926,910,2,'/app/crop_fence.png')\\nprint('wrote /app/bright.png and crops')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000}"
      }
    },
    {
      "step_id": 18,
      "timestamp": "2026-06-25T19:17:33.826Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "The decoder is still finishing compression; I\u2019ll collect its output and inspect the enhanced versions."
    },
    {
      "step_id": 19,
      "timestamp": "2026-06-25T19:17:34.513Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed write_stdin call_vP3c9k1DdGTWqJYRZtvAUrCQ",
      "tool_calls": [
        {
          "tool_call_id": "call_vP3c9k1DdGTWqJYRZtvAUrCQ",
          "function_name": "write_stdin",
          "arguments": {
            "session_id": 36498,
            "chars": "",
            "yield_time_ms": 1000,
            "max_output_tokens": 4000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_vP3c9k1DdGTWqJYRZtvAUrCQ",
            "content": "Command: /bin/bash -lc \"python3 - <<'PY'\nimport struct,zlib,os,math\npath='/app/image.png'\ndata=open(path,'rb').read()\npos=8\nidat=b''\nwidth=height=bit_depth=color_type=None\nwhile pos < len(data):\n    length=struct.unpack('>I',data[pos:pos+4])[0]\n    ctype=data[pos+4:pos+8]\n    chunk=data[pos+8:pos+8+length]\n    if ctype==b'IHDR':\n        width,height,bit_depth,color_type,comp,flt,inter=struct.unpack('>IIBBBBB',chunk)\n        print(width,height,bit_depth,color_type,comp,flt,inter)\n    elif ctype==b'IDAT':\n        idat += chunk\n    pos += 12+length\nraw=zlib.decompress(idat)\nif bit_depth\"'!=8 or color_type not in (2,6):\n    raise SystemExit('\"'unsupported')\nchannels=3 if color_type==2 else 4\nstride=width*channels\nrows=[]\ni=0\nprev=[0]*stride\nfor y in range(height):\n    f=raw[i]; i+=1\n    cur=list(raw[i:i+stride]); i+=stride\n    recon=[0]*stride\n    for x in range(stride):\n        a=recon[x-channels] if x>=channels else 0\n        b=prev[x]\n        c=prev[x-channels] if x>=channels else 0\n        val=cur[x]\n        if f==0: r=val\n        elif f==1: r=(val+a)&255\n        elif f==2: r=(val+b)&255\n        elif f==3: r=(val+((a+b)//2))&255\n        elif f==4:\n            p=a+b-c\n            pa=abs(p-a); pb=abs(p-b); pc=abs(p-c)\n            pr=a if pa<=pb and pa<=pc else (b if pb<=pc else c)\n            r=(val+pr)&255\n        else: raise SystemExit('bad filter')\n        recon[x]=r\n    rows.append(recon)\n    prev=recon\n\ndef write_png(out, rows2, w, h, channels=3):\n    ct=2 if channels==3 else 6\n    raw2=b''.join(bytes([0])+bytes(row) for row in rows2)\n    def chunk(t,d):\n        return struct.pack('>I',len(d))+t+d+struct.pack('>I',zlib.crc32(t+d)&0xffffffff)\n    png=b'\\\\x89PNG\\\\r\\\\n\\\\x1a\\\\n'+chunk(b'IHDR',struct.pack('>IIBBBBB',w,h,8,ct,0,0,0))+chunk(b'IDAT',zlib.compress(raw2,9))+chunk(b'IEND',b'')\n    open(out,'wb').write(png)\n\ndef transform(row, gain=2.5, gamma=0.7):\n    out=[]\n    for j in range(0,len(row),channels):\n        for k in range(3):\n            v=row[j+k]/255.0\n            # lift shadows and gamma\n            nv=min(1.0,(v**gamma)*gain)\n            out.append(int(nv*255+0.5))\n        if channels==4:\n            pass\n    return out\n# full brightened RGB\nbright=[transform(r,2.0,0.6) for r in rows]\nwrite_png('/app/bright.png', bright, width, height, 3)\n# crop right beach/shore, bright and upsample 2x maybe nearest\ndef crop_resize(x0,y0,x1,y1,scale,out):\n    crop=[]\n    for y in range(y0,y1):\n        src=[]\n        row=rows[y]\n        for x in range(x0,x1):\n            idx=x*channels\n            for k in range(3):\n                v=row[idx+k]/255.0\n                nv=min(1.0,(v**0.5)*3.0)\n                src.append(int(nv*255+0.5))\n        # nearest scale horizontal\n        scaled=[]\n        for x in range(x1-x0):\n            pix=src[x*3:x*3+3]\n            for _ in range(scale): scaled += pix\n        for _ in range(scale): crop.append(scaled)\n    write_png(out,crop,(x1-x0)*scale,(y1-y0)*scale,3)\n\ncrop_resize(350,120,926,650,2,'/app/crop_beach.png')\ncrop_resize(0,0,500,350,2,'/app/crop_water.png')\ncrop_resize(0,500,926,910,2,'/app/crop_fence.png')\nprint('wrote /app/bright.png and crops')\nPY\"\nChunk ID: 734b0d\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 13\nOutput:\n926 910 8 6 0 0 0\nwrote /app/bright.png and crops\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"session_id\":36498,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":4000}"
      }
    },
    {
      "step_id": 20,
      "timestamp": "2026-06-25T19:17:36.218Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "The enhanced versions are ready; I\u2019ll view them for any location-specific features."
    },
    {
      "step_id": 21,
      "timestamp": "2026-06-25T19:17:36.483Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed view_image call_uYlau1ABwNSMZIY6OFXacEnT",
      "tool_calls": [
        {
          "tool_call_id": "call_uYlau1ABwNSMZIY6OFXacEnT",
          "function_name": "view_image",
          "arguments": {
            "path": "/app/bright.png"
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_uYlau1ABwNSMZIY6OFXacEnT",
            "content": "[{'type': 'input_image', 'image_url': 'data:image/png;base64,[encoded data, 1096380 characters]'}]"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"path\":\"/app/bright.png\"}"
      }
    },
    {
      "step_id": 22,
      "timestamp": "2026-06-25T19:17:39.640Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "The scene has a distinctive fenced bluff-and-beach layout, so I\u2019m checking the crops for additional cues."
    },
    {
      "step_id": 23,
      "timestamp": "2026-06-25T19:17:39.947Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed view_image call_T0YRPhYbhWgc0LMAyDzh3Yr4",
      "tool_calls": [
        {
          "tool_call_id": "call_T0YRPhYbhWgc0LMAyDzh3Yr4",
          "function_name": "view_image",
          "arguments": {
            "path": "/app/crop_beach.png"
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_T0YRPhYbhWgc0LMAyDzh3Yr4",
            "content": "[{'type': 'input_image', 'image_url': 'data:image/png;base64,[encoded data, 1065624 characters]'}]"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"path\":\"/app/crop_beach.png\"}"
      }
    },
    {
      "step_id": 24,
      "timestamp": "2026-06-25T19:17:42.538Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "I\u2019ll view the waterline crop too; the opposite shore silhouette can help distinguish the city."
    },
    {
      "step_id": 25,
      "timestamp": "2026-06-25T19:17:42.864Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed view_image call_mNYEBbyTEJ1qis4bvCo1k2dI",
      "tool_calls": [
        {
          "tool_call_id": "call_mNYEBbyTEJ1qis4bvCo1k2dI",
          "function_name": "view_image",
          "arguments": {
            "path": "/app/crop_water.png"
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_mNYEBbyTEJ1qis4bvCo1k2dI",
            "content": "[{'type': 'input_image', 'image_url': 'data:image/png;base64,[encoded data, 64204 characters]'}]"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"path\":\"/app/crop_water.png\"}"
      }
    },
    {
      "step_id": 26,
      "timestamp": "2026-06-25T19:18:17.316Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "I found no GPS metadata, so I\u2019ll rely on visual geolocation and then save the answer."
    },
    {
      "step_id": 27,
      "timestamp": "2026-06-25T19:18:17.655Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_YYkbdD2LWLnRAPAlGXAlKBjD",
      "tool_calls": [
        {
          "tool_call_id": "call_YYkbdD2LWLnRAPAlGXAlKBjD",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "python3 - <<'PY'\nimport struct\npath='/app/image.png'\ndata=open(path,'rb').read()\npos=8\nwhile pos < len(data):\n    length=struct.unpack('>I',data[pos:pos+4])[0]\n    ctype=data[pos+4:pos+8]\n    chunk=data[pos+8:pos+8+length]\n    if ctype==b'eXIf':\n        print(chunk.hex())\n        print(chunk[:200])\n    pos += 12+length\nPY",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 4000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_YYkbdD2LWLnRAPAlGXAlKBjD",
            "content": "Command: /bin/bash -lc \"python3 - <<'PY'\nimport struct\npath='/app/image.png'\ndata=open(path,'rb').read()\npos=8\nwhile pos < len(data):\n    length=struct.unpack('>I',data[pos:pos+4])[0]\n    ctype=data[pos+4:pos+8]\n    chunk=data[pos+8:pos+8+length]\n    if ctype==b'eXIf':\n        print(chunk.hex())\n        print(chunk[:200])\n    pos += 12+length\nPY\"\nChunk ID: 68734e\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 209\nOutput:\n4d4d002a000000080004011a0005000000010000003e011b0005000000010000004601280003000000010002000087690004000000010000004e00000000000000900000000100000090000000010004928600070000001200000084a00100030000000100010000a0020004000000010000039ea0030004000000010000038e00000000415343494900000053637265656e73686f74\nb'MM\\x00*\\x00\\x00\\x00\\x08\\x00\\x04\\x01\\x1a\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00>\\x01\\x1b\\x00\\x05\\x00\\x00\\x00\\x01\\x00\\x00\\x00F\\x01(\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x02\\x00\\x00\\x87i\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x00N\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x90\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x90\\x00\\x00\\x00\\x01\\x00\\x04\\x92\\x86\\x00\\x07\\x00\\x00\\x00\\x12\\x00\\x00\\x00\\x84\\xa0\\x01\\x00\\x03\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xa0\\x02\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x03\\x9e\\xa0\\x03\\x00\\x04\\x00\\x00\\x00\\x01\\x00\\x00\\x03\\x8e\\x00\\x00\\x00\\x00ASCII\\x00\\x00\\x00Screenshot'\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"python3 - <<'PY'\\nimport struct\\npath='/app/image.png'\\ndata=open(path,'rb').read()\\npos=8\\nwhile pos < len(data):\\n    length=struct.unpack('>I',data[pos:pos+4])[0]\\n    ctype=data[pos+4:pos+8]\\n    chunk=data[pos+8:pos+8+length]\\n    if ctype==b'eXIf':\\n        print(chunk.hex())\\n        print(chunk[:200])\\n    pos += 12+length\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000}"
      }
    },
    {
      "step_id": 28,
      "timestamp": "2026-06-25T19:18:31.588Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "I\u2019ll save the requested response with Seattle as the most likely city."
    },
    {
      "step_id": 29,
      "timestamp": "2026-06-25T19:18:34.150Z",
      "source": "agent",
      "model_name": "gpt-5.5",
      "message": "Executed exec_command call_wppXBOdBb79mkeGUehPw4Hio",
      "tool_calls": [
        {
          "tool_call_id": "call_wppXBOdBb79mkeGUehPw4Hio",
          "function_name": "exec_command",
          "arguments": {
            "cmd": "mkdir -p /logs/artifacts && cat > /logs/artifacts/response.txt <<'EOF'\nExplanation: The image shows a Pacific Northwest shoreline at sunset with evergreen-covered bluffs, a Puget Sound-like water view, beach fire activity, and chain-link fencing typical of Seattle coastal parks such as Golden Gardens or Carkeek Park.\nAnswer: Seattle\nConfidence: 82%\nEOF\ncat /logs/artifacts/response.txt",
            "workdir": "/app",
            "yield_time_ms": 1000,
            "max_output_tokens": 2000
          }
        }
      ],
      "observation": {
        "results": [
          {
            "source_call_id": "call_wppXBOdBb79mkeGUehPw4Hio",
            "content": "Command: /bin/bash -lc \"mkdir -p /logs/artifacts && cat > /logs/artifacts/response.txt <<'EOF'\nExplanation: The image shows a Pacific Northwest shoreline at sunset with evergreen-covered bluffs, a Puget Sound-like water view, beach fire activity, and chain-link fencing typical of Seattle coastal parks such as Golden Gardens or Carkeek Park.\nAnswer: Seattle\nConfidence: 82%\nEOF\ncat /logs/artifacts/response.txt\"\nChunk ID: 8880d8\nWall time: 0.0000 seconds\nProcess exited with code 0\nOriginal token count: 70\nOutput:\nExplanation: The image shows a Pacific Northwest shoreline at sunset with evergreen-covered bluffs, a Puget Sound-like water view, beach fire activity, and chain-link fencing typical of Seattle coastal parks such as Golden Gardens or Carkeek Park.\nAnswer: Seattle\nConfidence: 82%\n"
          }
        ]
      },
      "extra": {
        "raw_arguments": "{\"cmd\":\"mkdir -p /logs/artifacts && cat > /logs/artifacts/response.txt <<'EOF'\\nExplanation: The image shows a Pacific Northwest shoreline at sunset with evergreen-covered bluffs, a Puget Sound-like water view, beach fire activity, and chain-link fencing typical of Seattle coastal parks such as Golden Gardens or Carkeek Park.\\nAnswer: Seattle\\nConfidence: 82%\\nEOF\\ncat /logs/artifacts/response.txt\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}"
      }
    },
    {
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