{
  "filename": "rollout-2026-01-29T22-48-55-019c0bf2-00e0-74e2-8ab1-1a83ee3d73c1.jsonl",
  "date": "2026-01-29",
  "session_id": "019c0bf2-00e0-74e2-8ab1-1a83ee3d73c1",
  "first_timestamp": "2026-01-29T22:48:55.266Z",
  "last_timestamp": "2026-01-29T22:57:40.617Z",
  "duration_seconds": 525.4,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "xhigh",
  "raw_event_count": 228,
  "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": "2026-01-29T22:48:55.266Z"
    },
    {
      "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": "2026-01-29T22:48:55.266Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-29T22:48:55.266Z"
    },
    {
      "index": 3,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/data_prep/user_meta.py\n\n## Open tabs:\n- user_meta.py: col/src/data_prep/user_meta.py\n- data_prep.py: col/src/data_prep/data_prep.py\n- config.py: col/src/config/config.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nthis is my main file\n\ncol/src/driver.py\n\nlook at all the files and function it triggers , if there are any logical error or any errors in my prohram\n",
      "timestamp": "2026-01-29T22:48:55.417Z"
    },
    {
      "index": 4,
      "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- data_prep.py: col/src/data_prep/data_prep.py\n- user_meta.py: col/src/data_prep/user_meta.py\n- config.py: col/src/config/config.py\n- driver.py: col/src/driver.py\n\n## My request for Codex:\nCrash in training dataloader: age_days and total_interactions are read from ints.npy, but they are written to floats.npy in data prep. This will raise a KeyError on self.shard_int_idx[\"age_days\"] / [\"total_interactions\"] (or silently read the wrong column if indices were ever present). Use batch_floats_data with self.shard_float_slices instead. dataset.py (line 114), dataset.py (line 116)\n\nCehcek now\n",
      "timestamp": "2026-01-29T22:57:18.576Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Found several concrete issues in the files the main driver triggers. The most serious one will break training; a few others are crash-on-empty or data-type risks.\n\n**Findings**\n- **Crash in training dataloader:** `age_days` and `total_interactions` are read from `ints.npy`, but they are written to `floats.npy` in data prep. This will raise a `KeyError` on `self.shard_int_idx[\"age_days\"]` / `[\"total_interactions\"]` (or silently read the wrong column if indices were ever present). Use `batch_floats_data` with `self.shard_float_slices` instead. `col/src/model/dataset.py:114`, `col/src/model/dataset.py:116`\n- **Hardcoded secrets in source:** API keys and DB credentials are committed and set at import-time, which is a severe security risk and overrides real env config in any environment. `col/src/driver.py:3`, `col/src/driver.py:16`\n- **Crash when Athena/MySQL queries return no rows:** `pd.concat(dfs)` will raise `ValueError: No objects to concatenate` if a query yields no rows or the input list is empty. This can happen for empty datasets or bad filters. Add a guard that returns an empty DataFrame with expected columns. `col/src/data_prep/user_meta.py:108`, `col/src/data_prep/user_meta.py:152`, `col/src/data_prep/series_meta.py:33`, `col/src/data_prep/series_meta.py:52`, `col/src/data_prep/series_meta.py:103`\n- **Potential datetime parsing error:** `pd.to_datetime(..., unit=\"s\")` will throw if `date_created` is already datetime (common for Athena) or a non-numeric string. Consider checking dtype or using `pd.to_datetime` without `unit` when the column is already datetime-like. `col/src/data_prep/user_meta.py:184`\n- **Potential crash on empty pref fetch:** If `user_ids` is empty, `get_pref_multithreaded` returns a DataFrame with no columns; `genre_pref_df[\"value\"]` then raises `KeyError`. Also, `.str` accessor fails if `value` is non-string. `col/src/data_prep/user_meta.py:116`\n\n**Open questions / assumptions**\n- If `date_created` is stored as epoch seconds, the `unit=\"s\"` call is fine; if it’s an Athena timestamp (datetime), it will error.\n- If there’s any chance `user_ids` or `unique_series_list` can be empty (e.g., test runs), the concat guards become necessary.\n\nIf you want, I can patch the dataloader bug and add the empty-DataFrame guards.",
      "timestamp": "2026-01-29T22:54:39.530Z"
    },
    {
      "index": 1,
      "text": "Checked `col/src/model/dataset.py` — it already reads `age_days` and `total_interactions` from `batch_floats_data` with `self.shard_float_slices`. So the crash you described is already fixed in this file.\n\n- `col/src/model/dataset.py:114` uses floats for `age_days`\n- `col/src/model/dataset.py:116` uses floats for `total_interactions`\n\nIf you want me to harden this further (e.g., reshape/astype consistency or guards when slices are missing), say the word.",
      "timestamp": "2026-01-29T22:57:40.615Z"
    }
  ],
  "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' (8170) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRACE..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need filesystem access to inspect project files for logical errors.\"}",
      "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 '1,200p' col/src/driver.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Read driver.py to review program flow.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom datetime import datetime\nos.environ[\"AWS_DEFAULT_REGION\"] = \"ap-south-1\"\nos.environ[\"OPENAI_API_KEY\"] = \"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"def driver\\\" col/src -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Locate driver functions to trace execution flow.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/model/training.py:208:def driver():\ncol/src/model/recommend.py:261:def driver(\ncol/src/data_prep/data_prep.py:13:def driver():\n/home/ubuntu/.profile: ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/data_prep/data_prep.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect data prep driver for logical ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nimport gc\nfrom pandarallel import pandarallel\nfrom data_prep.series_meta import get_series_meta\nfrom data_prep.user_met..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/data_prep/user_meta.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect user_meta functions for logic...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nfrom utils.athena import query_from_athena\nfrom utils.utils import chunked\nimport mysql.connector\nfrom concurrent.futur..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/data_prep/series_meta.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect series_meta data prep.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nfrom utils.utils import chunked\nfrom utils.athena import query_from_athena\nimport pandas as pd\nfrom config.config import BATCH_SIZE, get_openai_client\nfrom tq..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/data_prep/mapping.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect mapping/normalization logic.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport numpy as np\nfrom config.config import CATEGORICAL_COLUMNS, CONTINUOUS_COLUMNS, EMBEDDING_COLUMNS_TO_SIZE\n\nclass Normalizer:\n    def __init__(self, list..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/data_prep/interaction.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect interaction extraction logi...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nfrom pandarallel import pandarallel\n\ndef get_user_series_interaction(language, language_key):\n    up_df = pd.read_parqu..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/utils/utils.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect utility functions used in data prep/t...",
      "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 '1,260p' col/src/utils/athena.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect Athena query helper for errors.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport awswrangler as wr\nimport os\n\ndef query_from_athena(sql_query, database=\"pratilipi_deep\",\n                      s3_output=f\"s3://datascience-projects/at..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect training logic for errors.\"}",
      "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\":\"sed -n '260,620p' col/src/model/training.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue inspecting training driver.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n        input_dim=TOTAL_INPUT_DIM_WITH_COSINE,\n        mlp_layers=MLP_LAYERS,\n        dropout_rate=DROPOUT_RATE,\n        col_to_emb_dim=COL_TO_EMB_DIM,\n      ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,340p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect recommendation logic for errors.\"...",
      "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\":\"sed -n '340,760p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue inspecting recommend driver.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n                series_mapper.idx_to_val_dict.get(int(sid), int(sid)) for sid in top_ids\n            ]\n\n            row = {\n                \"user_id\": int(ori..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/config/config.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect config to understand data columns a...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom openai import OpenAI\n\nLANGUAGE=\"english\"\nLANGUAGE_KEY=\"en\"\n\nMODE='prod'\n\ndef _env_flag(name: str, default: bool = False) -> bool:\n    value = o..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/model/model.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect model definition for logical errors.\"...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass CTRModelWithCosineSignals(nn.Module):\n    def __init__(\n        self,\n        in..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect dataset loading and batching logic....",
      "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\":\"sed -n '1,260p' col/src/model/sampler.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect negative sampler for errors.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport numpy as np\n\n\nclass PopularitySampler:\n    def __init__(self, vocab_arr, positive_series_arr, seed=None):\n        values, counts = np.unique(positive_s..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls -a\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check repository files for AGENTS instructions.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n.\n..\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/..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/model/dataset.py | sed -n '70,200p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers for dataset issu...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n    70\t        self.sampler = sampler\n    71\t        self.neg_per_pos = neg_per_pos\n    72\t        self.shuffle_shards = shuffle_shards\n    73\t        self.sh..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/data_prep/user_meta.py | sed -n '1,240p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers for user_me...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n     1\timport pandas as pd\n     2\timport numpy as np\n     3\tfrom utils.athena import query_from_athena\n     4\tfrom utils.utils import chunked\n     5\timport my..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/data_prep/series_meta.py | sed -n '1,200p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers for serie...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n     1\tfrom utils.utils import chunked\n     2\tfrom utils.athena import query_from_athena\n     3\timport pandas as pd\n     4\tfrom config.config import BATCH_SIZ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/driver.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers for driver.py issues.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n     1\timport os\n     2\tfrom datetime import datetime\n     3\tos.environ[\"AWS_DEFAULT_REGION\"] = \"ap-south-1\"\n     4\tos.environ[\"OPENAI_API_KEY\"] = \"sk-proj-qR..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nl -ba col/src/data_prep/data_prep.py | sed -n '1,260p'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Get line numbers for data_pr...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n     1\timport pandas as pd\n     2\timport numpy as np\n     3\timport gc\n     4\tfrom pandarallel import pandarallel\n     5\tfrom data_prep.series_meta import get_..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '106,130p' col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Re-open dataset.py around age_days/total_...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n                user_id = batch_ints_data[:, self.shard_int_idx[\"user_id\"]]\n                gender = batch_ints_data[:, self.shard_int_idx[\"gender\"]]\n        ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"age_days\\\" col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Confirm age_days references in dataset.p...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n114:                age_days = batch_floats_data[:, self.shard_float_slices[\"age_days\"]].astype(np.float32, copy=False)[:, None]\n130:                age_days ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"total_interactions\\\" col/src/model/dataset.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Confirm total_interactions ref...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n116:                total_interactions = batch_floats_data[:, self.shard_float_slices[\"total_interactions\"]].astype(np.float32, copy=False)[:, None]\n131:     ..."
    }
  ],
  "turn_count": 5,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
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    "session_type": "bug-fix",
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    "prompt_specificity": 5,
    "time_of_day": "evening"
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}