{
  "filename": "rollout-2026-01-30T16-47-16-019c0fcd-4338-7a10-a9d8-490dc0305087.jsonl",
  "date": "2026-01-30",
  "session_id": "019c0fcd-4338-7a10-a9d8-490dc0305087",
  "first_timestamp": "2026-01-30T16:47:16.282Z",
  "last_timestamp": "2026-01-30T16:55:42.534Z",
  "duration_seconds": 506.3,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "xhigh",
  "raw_event_count": 171,
  "user_messages": [
    {
      "index": 0,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `read-only`: The sandbox only permits reading files. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter</permissions instructions>",
      "timestamp": "2026-01-30T16:47:16.282Z"
    },
    {
      "index": 1,
      "text": "# AGENTS.md instructions for /home/ubuntu/nikhil/cold-ranker\n\n<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- 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: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$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) 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  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  4) 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</INSTRUCTIONS>",
      "timestamp": "2026-01-30T16:47:16.282Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-30T16:47:16.282Z"
    },
    {
      "index": 3,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/training.py\n\n## Open tabs:\n- training.py: col/src/model/training.py\n- config.py: col/src/config/config.py\n- driver.py: col/src/driver.py\n- utils.py: col/src/utils/utils.py\n- dataset.py: col/src/model/dataset.py\n\n## My request for Codex:\n(nik_gpu) ubuntu@ip-192-168-1-56:~/nikhil/cold-ranker/col/src$ python driver.py \nDevice: cuda\nData loaded from ../../data/hi/cat_columns_to_mapper.pkl\nTraceback (most recent call last):\n  File \"/home/ubuntu/nikhil/cold-ranker/col/src/driver.py\", line 34, in <module>\n    main()\n  File \"/home/ubuntu/nikhil/cold-ranker/col/src/driver.py\", line 28, in main\n    training.driver()\n  File \"/home/ubuntu/nikhil/cold-ranker/col/src/model/training.py\", line 219, in driver\n    pos_series_arr = get_pos_series(train_shard_dir, test_shard_dir, MODE)\n  File \"/home/ubuntu/nikhil/cold-ranker/col/src/model/training.py\", line 198, in get_pos_series\n    pos_series_arr_shard = ints_data[:,0]\n  File \"/home/ubuntu/nikhil/pyG/nik_gpu/lib/python3.10/site-packages/numpy/lib/npyio.py\", line 263, in __getitem__\n    raise KeyError(f\"{key} is not a file in the archive\")\nKeyError: '(slice(None, None, None), 0) is not a file in the archive'\n\n\nwhat is the reason of this error\n",
      "timestamp": "2026-01-30T16:47:16.423Z"
    },
    {
      "index": 4,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-30T16:53:45.054Z"
    },
    {
      "index": 5,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `workspace-write`: The sandbox permits reading files, and editing files in `cwd` and `writable_roots`. Editing files in other directories requires approval. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter The writable roots are `/home/ubuntu/nikhil/cold-ranker`, `/home/ubuntu/nikhil/cold-ranker`, `/tmp`.</permissions instructions>",
      "timestamp": "2026-01-30T16:53:45.055Z"
    },
    {
      "index": 6,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/dataset.py\n\n## Open tabs:\n- dataset.py: col/src/model/dataset.py\n- training.py: col/src/model/training.py\n- config.py: col/src/config/config.py\n- driver.py: col/src/driver.py\n- utils.py: col/src/utils/utils.py\n\n## My request for Codex:\nyes do it anywhere necesssary. what baout floats.npz\n",
      "timestamp": "2026-01-30T16:53:45.124Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Reason: `np.load(\".../ints.npz\")` is returning an `NpzFile` (a zip archive), not a NumPy array. When you do `ints_data[:, 0]` in `col/src/model/training.py:198`, NumPy treats the tuple `(slice(None), 0)` as a **key** into the archive and raises `KeyError` because that key doesn’t exist.\n\nYour `ints.npz` file actually contains an array under the key `arr_0`, so you need to pull that out before slicing:\n\n```python\nints_data = np.load(f\"{train_shard_path}/ints.npz\")[\"arr_0\"]\npos_series_arr_shard = ints_data[:, 0]\n```\n\nYou’ll hit the same issue anywhere else you do `np.load(.../ints.npz)` and then use `.shape` or slicing (e.g., `col/src/model/dataset.py`, `col/src/model/recommend.py`). Either:\n\n1) Update loads to use `[\"arr_0\"]`, or  \n2) Regenerate data so `ints` are saved as plain `.npy` (e.g., `np.save(\"ints.npy\", ints)`) and load with `np.load` directly.\n\nIf you want, I can patch the loaders to handle both `.npz` and `.npy` cleanly.",
      "timestamp": "2026-01-30T16:49:20.546Z"
    },
    {
      "index": 1,
      "text": "Added a small loader that unwraps single‑array `.npz` files (the `arr_0` key) and replaced direct `np.load` calls so both `ints.npz` and `floats.npz` behave like normal ndarrays. This fixes the `KeyError` you hit and also prevents the next shape/slicing error you’d get with `floats.npz`.\n\nDetails:\n- New helper in `col/src/utils/utils.py` returns the array for both `.npy` and single‑array `.npz`.\n- Updated loaders in `col/src/model/training.py`, `col/src/model/dataset.py`, and `col/src/model/recommend.py` to use it.\n- `floats.npz` is saved via `np.savez_compressed`, so it *does* need the same `arr_0` unwrap; it’s covered now.\n\nIf you want, I can also standardize the writer to always use `.npy` or always use `.npz` to keep formats consistent.\n\nNext steps:\n1) Rerun `python driver.py` to confirm the error is gone.\n2) (Optional) Normalize the shard writer format to avoid mixed `.npy`/`.npz` behavior.",
      "timestamp": "2026-01-30T16:55:42.530Z"
    }
  ],
  "tool_calls": [
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (17618) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRAC..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to list project files to inspect the error source; sandbox blocked the comma...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol\ndata\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubunt..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '150,260p' col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to inspect training.py around get_p...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n            batches += 1\n\n            with torch.no_grad():\n                probs = torch.sigmoid(logits.detach())\n                preds = (probs >= 0.5).floa..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"savez|npz\\\" -S col/src\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Search for where ints.npz is written to determine its...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/model/dataset.py:35:        self.series_meta_ints_data = np.load(f\"{series_meta_dir}/shard-00000/ints.npz\")\ncol/src/model/dataset.py:36:        self.s..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,140p' col/src/utils/utils.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect utils.py to see how ints.npz is saved...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport json\nfrom pathlib import Path\nfrom typing import List, Optional\n\ndef chunked(iterable, size):\n    ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,160p' col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect dataset.py for how ints.npz is load...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nimport json\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, IterableDataset\nfrom config.config import EMBEDDING_COLUMNS_TO_..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls data/hi/train | head\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check data shards existence to understand file naming.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nshard-00000\nshard-00001\nshard-00002\nshard-00003\nshard-00004\nshard-00005\nshard-00006\nshard-00007\nshard-00008\nshard-00009\n/home/ubuntu/.profile: line 28: /home/..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls data/hi/train/shard-00000\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect shard contents to confirm file extensions.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nfloats.npz\nints.npz\nmeta.json\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile:..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"python - <<'PY'\\nimport numpy as np\\np='data/hi/train/shard-00000/ints.npz'\\nobj=np.load(p)\\nprint(type(obj))\\n# if np.load returns ndarray, it won't have .files\\nprint(getattr(obj,'files'...",
      "output_summary": "Exit code: 0\nWall time: 0.5 seconds\nOutput:\n<class 'numpy.lib.npyio.NpzFile'>\n['arr_0']\n('arr_0', (1000000, 4), dtype('int32'))\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/co..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,140p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect recommend.py for how ints/floats ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport json\nimport os\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom model.model import CTRModelWithCosineSignals\nfrom utils.utils import load_pic..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"ints.npz\\\" col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (18032) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRAC..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"ints.npz\\\" col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Search file for ints.npz load lines to ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n197:        ints_data = np.load(f\"{train_shard_path}/ints.npz\")[\"arr_0\"]\n239:            ints_data = np.load(f\"{shard_path}/ints.npz\")\n/home/ubuntu/.profile: ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,60p' col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check imports in training.py to add load_np...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nimport math\nfrom model.sampler import PopularitySampler\nimport time\nfrom typing import Optional\nfrom typing import Dict, Tuple\nimport numpy as np\nfr..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"load\\\\(.*(ints|floats)\\\\.npz\\\" col/src\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (18112) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRAC..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"load\\\\(.*(ints|floats)\\\\.npz\\\" col/src\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Verify remaining np.load calls for in...",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"ints\\\\.npz|floats\\\\.npz\\\" col/src\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Find all remaining mentions of ints.npz/fl...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/model/dataset.py:36:        self.series_meta_ints_data = load_npz_array(f\"{series_meta_dir}/shard-00000/ints.npz\")\ncol/src/model/dataset.py:37:       ..."
    }
  ],
  "turn_count": 7,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 7,
    "struggle_score": 4,
    "prompt_specificity": 5,
    "time_of_day": "afternoon"
  }
}