{
  "format": "ta-amps.real-run-step-receipt.v1",
  "run_id": "20261031T065341Z",
  "step": 2,
  "id": "build",
  "hash": {
    "value": "a30b48b17ba2a20c60a3fdabcb67874760e96c4215d58ed4141288d763f5d34e",
    "kind": "digest",
    "recompute": "sha256 of json.dumps({path: sha256}, sort_keys=True, separators=(',', ':')) over the four kit files, as the build brief's output contract defines it."
  },
  "copied_from": {
    "trace": "../trace.jsonl",
    "tessera": "../tessera-readback.json"
  },
  "trace": [
    {
      "seq": 1,
      "t": 1793429623.749225,
      "utc": "2026-10-31T06:53:43.749Z",
      "from": "muse-code (builder)",
      "to": "meta model api (catalog)",
      "op": "model catalog read",
      "kind": "request",
      "bytes": 0
    },
    {
      "seq": 2,
      "t": 1793429624.085785,
      "utc": "2026-10-31T06:53:44.085Z",
      "from": "meta model api (catalog)",
      "to": "muse-code (builder)",
      "op": "model catalog read",
      "kind": "response",
      "corr": 1,
      "status": 200,
      "bytes": 4900,
      "latency_ms": 336,
      "outcome": "ok"
    },
    {
      "seq": 3,
      "t": 1793429624.572924,
      "utc": "2026-10-31T06:53:44.572Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "initialize",
      "kind": "request",
      "bytes": 152
    },
    {
      "seq": 4,
      "t": 1793429624.582117,
      "utc": "2026-10-31T06:53:44.582Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "initialize",
      "kind": "response",
      "corr": 3,
      "status": 200,
      "bytes": 1078,
      "latency_ms": 6,
      "outcome": "ok"
    },
    {
      "seq": 5,
      "t": 1793429624.585685,
      "utc": "2026-10-31T06:53:44.585Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "notifications/initialized",
      "kind": "request",
      "bytes": 66
    },
    {
      "seq": 6,
      "t": 1793429624.588939,
      "utc": "2026-10-31T06:53:44.588Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "notifications/initialized",
      "kind": "response",
      "corr": 5,
      "status": 202,
      "bytes": 0,
      "outcome": "ok"
    },
    {
      "seq": 7,
      "t": 1793429624.590932,
      "utc": "2026-10-31T06:53:44.590Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "tools/list",
      "kind": "request",
      "bytes": 58
    },
    {
      "seq": 8,
      "t": 1793429624.597224,
      "utc": "2026-10-31T06:53:44.597Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "tools/list",
      "kind": "response",
      "corr": 7,
      "status": 200,
      "bytes": 2756,
      "latency_ms": 6,
      "outcome": "ok",
      "tools": 5
    },
    {
      "seq": 9,
      "t": 1793429624.62987,
      "utc": "2026-10-31T06:53:44.629Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 123341,
      "call": 1,
      "api": "responses"
    },
    {
      "seq": 10,
      "t": 1793429627.43001,
      "utc": "2026-10-31T06:53:47.430Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 9,
      "call": 1,
      "status": 200,
      "bytes": 275042,
      "latency_ms": 2799,
      "outcome": "completed",
      "input_tokens": 26691,
      "cached_tokens": 0,
      "output_tokens": 120,
      "reasoning_tokens": 8,
      "cost_usd_estimate": 0.002693
    },
    {
      "seq": 11,
      "t": 1793429627.455617,
      "utc": "2026-10-31T06:53:47.455Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "claim",
      "kind": "request",
      "bytes": 368,
      "queue": "eq02"
    },
    {
      "seq": 12,
      "t": 1793429627.464162,
      "utc": "2026-10-31T06:53:47.464Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "claim",
      "kind": "response",
      "corr": 11,
      "status": 200,
      "bytes": 174,
      "latency_ms": 8,
      "outcome": "no_lease",
      "note": "no lease available"
    },
    {
      "seq": 13,
      "t": 1793429627.481013,
      "utc": "2026-10-31T06:53:47.481Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 125290,
      "call": 2,
      "api": "responses"
    },
    {
      "seq": 14,
      "t": 1793429632.958237,
      "utc": "2026-10-31T06:53:52.958Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 13,
      "call": 2,
      "status": 200,
      "bytes": 276421,
      "latency_ms": 5467,
      "outcome": "completed",
      "input_tokens": 26854,
      "cached_tokens": 26609,
      "output_tokens": 102,
      "reasoning_tokens": 11,
      "cost_usd_estimate": 9.8e-05
    },
    {
      "seq": 15,
      "t": 1793429639.037824,
      "utc": "2026-10-31T06:53:59.037Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 127544,
      "call": 3,
      "api": "responses"
    },
    {
      "seq": 16,
      "t": 1793429642.6509,
      "utc": "2026-10-31T06:54:02.650Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 15,
      "call": 3,
      "status": 200,
      "bytes": 271198,
      "latency_ms": 3609,
      "outcome": "completed",
      "input_tokens": 27044,
      "cached_tokens": 26737,
      "output_tokens": 89,
      "reasoning_tokens": 0,
      "cost_usd_estimate": 0.000102
    },
    {
      "seq": 17,
      "t": 1793429642.673839,
      "utc": "2026-10-31T06:54:02.673Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "claim",
      "kind": "request",
      "bytes": 341,
      "queue": "eq02"
    },
    {
      "seq": 18,
      "t": 1793429642.686524,
      "utc": "2026-10-31T06:54:02.686Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "claim",
      "kind": "response",
      "corr": 17,
      "status": 200,
      "bytes": 9242,
      "latency_ms": 13,
      "outcome": "ok",
      "verb_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "fencing_token": 1
    },
    {
      "seq": 19,
      "t": 1793429642.715633,
      "utc": "2026-10-31T06:54:02.715Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 132583,
      "call": 4,
      "api": "responses"
    },
    {
      "seq": 20,
      "t": 1793429644.74999,
      "utc": "2026-10-31T06:54:04.749Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 19,
      "call": 4,
      "status": 200,
      "bytes": 274791,
      "latency_ms": 2024,
      "outcome": "completed",
      "input_tokens": 28303,
      "cached_tokens": 26993,
      "output_tokens": 192,
      "reasoning_tokens": 94,
      "cost_usd_estimate": 0.000223
    },
    {
      "seq": 21,
      "t": 1793429644.760022,
      "utc": "2026-10-31T06:54:04.760Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "get_record",
      "kind": "request",
      "bytes": 360,
      "record": {
        "store": "kit.inputs",
        "key": "availability-20261031t065341z",
        "version": 1
      }
    },
    {
      "seq": 22,
      "t": 1793429644.770037,
      "utc": "2026-10-31T06:54:04.770Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "get_record",
      "kind": "response",
      "corr": 21,
      "status": 200,
      "bytes": 2871,
      "latency_ms": 10,
      "outcome": "ok",
      "record": {
        "store": "kit.inputs",
        "key": "availability-20261031t065341z",
        "version": 1
      }
    },
    {
      "seq": 23,
      "t": 1793429644.792873,
      "utc": "2026-10-31T06:54:04.792Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 135901,
      "call": 5,
      "api": "responses"
    },
    {
      "seq": 24,
      "t": 1793429651.135528,
      "utc": "2026-10-31T06:54:11.135Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 23,
      "call": 5,
      "status": 200,
      "bytes": 274596,
      "latency_ms": 6339,
      "outcome": "completed",
      "input_tokens": 29057,
      "cached_tokens": 28273,
      "output_tokens": 182,
      "reasoning_tokens": 102,
      "cost_usd_estimate": 0.000171
    },
    {
      "seq": 25,
      "t": 1793429651.188962,
      "utc": "2026-10-31T06:54:11.188Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 138178,
      "call": 6,
      "api": "responses"
    },
    {
      "seq": 26,
      "t": 1793429654.224704,
      "utc": "2026-10-31T06:54:14.224Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 25,
      "call": 6,
      "status": 200,
      "bytes": 274052,
      "latency_ms": 3025,
      "outcome": "completed",
      "input_tokens": 29383,
      "cached_tokens": 29041,
      "output_tokens": 412,
      "reasoning_tokens": 0,
      "cost_usd_estimate": 0.000175
    },
    {
      "seq": 27,
      "t": 1793429654.282897,
      "utc": "2026-10-31T06:54:14.282Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 139359,
      "call": 7,
      "api": "responses"
    },
    {
      "seq": 28,
      "t": 1793429668.94258,
      "utc": "2026-10-31T06:54:28.942Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 27,
      "call": 7,
      "status": 200,
      "bytes": 297160,
      "latency_ms": 14674,
      "outcome": "completed",
      "input_tokens": 29847,
      "cached_tokens": 29297,
      "output_tokens": 1363,
      "reasoning_tokens": 1216,
      "cost_usd_estimate": 0.000386
    },
    {
      "seq": 29,
      "t": 1793429668.996948,
      "utc": "2026-10-31T06:54:28.996Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "heartbeat",
      "kind": "request",
      "bytes": 348,
      "verb_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70"
    },
    {
      "seq": 30,
      "t": 1793429669.009978,
      "utc": "2026-10-31T06:54:29.009Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "heartbeat",
      "kind": "response",
      "corr": 29,
      "status": 200,
      "bytes": 9074,
      "latency_ms": 11,
      "outcome": "ok",
      "verb_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "fencing_token": 1
    },
    {
      "seq": 31,
      "t": 1793429669.056083,
      "utc": "2026-10-31T06:54:29.056Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 155424,
      "call": 8,
      "api": "responses"
    },
    {
      "seq": 32,
      "t": 1793429683.191211,
      "utc": "2026-10-31T06:54:43.191Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 31,
      "call": 8,
      "status": 200,
      "bytes": 310535,
      "latency_ms": 14061,
      "outcome": "completed",
      "input_tokens": 32290,
      "cached_tokens": 29809,
      "output_tokens": 1914,
      "reasoning_tokens": 6,
      "cost_usd_estimate": 0.000691
    },
    {
      "seq": 33,
      "t": 1793429683.243657,
      "utc": "2026-10-31T06:54:43.243Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 166397,
      "call": 9,
      "api": "responses"
    },
    {
      "seq": 34,
      "t": 1793429686.651005,
      "utc": "2026-10-31T06:54:46.651Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 33,
      "call": 9,
      "status": 200,
      "bytes": 274938,
      "latency_ms": 3404,
      "outcome": "completed",
      "input_tokens": 34257,
      "cached_tokens": 32241,
      "output_tokens": 108,
      "reasoning_tokens": 9,
      "cost_usd_estimate": 0.000288
    },
    {
      "seq": 35,
      "t": 1793429687.060605,
      "utc": "2026-10-31T06:54:47.060Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 170284,
      "call": 10,
      "api": "responses"
    },
    {
      "seq": 36,
      "t": 1793429704.507096,
      "utc": "2026-10-31T06:55:04.507Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 35,
      "call": 10,
      "status": 200,
      "bytes": 318940,
      "latency_ms": 17440,
      "outcome": "completed",
      "input_tokens": 35098,
      "cached_tokens": 34161,
      "output_tokens": 2338,
      "reasoning_tokens": 105,
      "cost_usd_estimate": 0.00063
    },
    {
      "seq": 37,
      "t": 1793429704.608278,
      "utc": "2026-10-31T06:55:04.608Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 183017,
      "call": 11,
      "api": "responses"
    },
    {
      "seq": 38,
      "t": 1793429707.140858,
      "utc": "2026-10-31T06:55:07.140Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 37,
      "call": 11,
      "status": 200,
      "bytes": 274527,
      "latency_ms": 2534,
      "outcome": "completed",
      "input_tokens": 37538,
      "cached_tokens": 35057,
      "output_tokens": 152,
      "reasoning_tokens": 52,
      "cost_usd_estimate": 0.000349
    },
    {
      "seq": 39,
      "t": 1793429707.152933,
      "utc": "2026-10-31T06:55:07.152Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "heartbeat",
      "kind": "request",
      "bytes": 348,
      "verb_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70"
    },
    {
      "seq": 40,
      "t": 1793429707.171174,
      "utc": "2026-10-31T06:55:07.171Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "heartbeat",
      "kind": "response",
      "corr": 39,
      "status": 200,
      "bytes": 9074,
      "latency_ms": 16,
      "outcome": "ok",
      "verb_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "fencing_token": 1
    },
    {
      "seq": 41,
      "t": 1793429707.212034,
      "utc": "2026-10-31T06:55:07.212Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 189525,
      "call": 12,
      "api": "responses"
    },
    {
      "seq": 42,
      "t": 1793429709.027821,
      "utc": "2026-10-31T06:55:09.027Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 41,
      "call": 12,
      "status": 200,
      "bytes": 271487,
      "latency_ms": 1806,
      "outcome": "completed",
      "input_tokens": 38817,
      "cached_tokens": 37489,
      "output_tokens": 102,
      "reasoning_tokens": 0,
      "cost_usd_estimate": 0.000228
    },
    {
      "seq": 43,
      "t": 1793429709.470229,
      "utc": "2026-10-31T06:55:09.470Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 191244,
      "call": 13,
      "api": "responses"
    },
    {
      "seq": 44,
      "t": 1793429712.154314,
      "utc": "2026-10-31T06:55:12.154Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 43,
      "call": 13,
      "status": 200,
      "bytes": 271546,
      "latency_ms": 2659,
      "outcome": "completed",
      "input_tokens": 39250,
      "cached_tokens": 38769,
      "output_tokens": 96,
      "reasoning_tokens": 0,
      "cost_usd_estimate": 0.000145
    },
    {
      "seq": 45,
      "t": 1793429712.375759,
      "utc": "2026-10-31T06:55:12.375Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 192680,
      "call": 14,
      "api": "responses"
    },
    {
      "seq": 46,
      "t": 1793429720.190234,
      "utc": "2026-10-31T06:55:20.190Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 45,
      "call": 14,
      "status": 200,
      "bytes": 282986,
      "latency_ms": 7709,
      "outcome": "completed",
      "input_tokens": 39743,
      "cached_tokens": 39153,
      "output_tokens": 522,
      "reasoning_tokens": 278,
      "cost_usd_estimate": 0.000242
    },
    {
      "seq": 47,
      "t": 1793429720.487537,
      "utc": "2026-10-31T06:55:20.487Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 197580,
      "call": 15,
      "api": "responses"
    },
    {
      "seq": 48,
      "t": 1793429723.812505,
      "utc": "2026-10-31T06:55:23.812Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 47,
      "call": 15,
      "status": 200,
      "bytes": 274704,
      "latency_ms": 3245,
      "outcome": "completed",
      "input_tokens": 40651,
      "cached_tokens": 0,
      "output_tokens": 213,
      "reasoning_tokens": 118,
      "cost_usd_estimate": 0.004108
    },
    {
      "seq": 49,
      "t": 1793429724.090767,
      "utc": "2026-10-31T06:55:24.090Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 199845,
      "call": 16,
      "api": "responses"
    },
    {
      "seq": 50,
      "t": 1793429726.865639,
      "utc": "2026-10-31T06:55:26.865Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 49,
      "call": 16,
      "status": 200,
      "bytes": 274654,
      "latency_ms": 2855,
      "outcome": "completed",
      "input_tokens": 40981,
      "cached_tokens": 40561,
      "output_tokens": 203,
      "reasoning_tokens": 0,
      "cost_usd_estimate": 0.000164
    },
    {
      "seq": 51,
      "t": 1793429727.100621,
      "utc": "2026-10-31T06:55:27.100Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 221284,
      "call": 17,
      "api": "responses"
    },
    {
      "seq": 52,
      "t": 1793429744.02638,
      "utc": "2026-10-31T06:55:44.026Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 51,
      "call": 17,
      "status": 200,
      "bytes": 286622,
      "latency_ms": 17019,
      "outcome": "completed",
      "input_tokens": 46519,
      "cached_tokens": 40945,
      "output_tokens": 746,
      "reasoning_tokens": 585,
      "cost_usd_estimate": 0.000788
    },
    {
      "seq": 53,
      "t": 1793429744.159858,
      "utc": "2026-10-31T06:55:44.159Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 227942,
      "call": 18,
      "api": "responses"
    },
    {
      "seq": 54,
      "t": 1793429750.266217,
      "utc": "2026-10-31T06:55:50.266Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 53,
      "call": 18,
      "status": 200,
      "bytes": 278830,
      "latency_ms": 6120,
      "outcome": "completed",
      "input_tokens": 47403,
      "cached_tokens": 46449,
      "output_tokens": 361,
      "reasoning_tokens": 245,
      "cost_usd_estimate": 0.00026
    },
    {
      "seq": 55,
      "t": 1793429750.390709,
      "utc": "2026-10-31T06:55:50.390Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "heartbeat",
      "kind": "request",
      "bytes": 348,
      "verb_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70"
    },
    {
      "seq": 56,
      "t": 1793429750.409558,
      "utc": "2026-10-31T06:55:50.409Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "heartbeat",
      "kind": "response",
      "corr": 55,
      "status": 200,
      "bytes": 9074,
      "latency_ms": 24,
      "outcome": "ok",
      "verb_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "fencing_token": 1
    },
    {
      "seq": 57,
      "t": 1793429750.452182,
      "utc": "2026-10-31T06:55:50.452Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 235815,
      "call": 19,
      "api": "responses"
    },
    {
      "seq": 58,
      "t": 1793429758.251296,
      "utc": "2026-10-31T06:55:58.251Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 57,
      "call": 19,
      "status": 200,
      "bytes": 286335,
      "latency_ms": 7782,
      "outcome": "completed",
      "input_tokens": 48875,
      "cached_tokens": 47345,
      "output_tokens": 828,
      "reasoning_tokens": 43,
      "cost_usd_estimate": 0.000413
    },
    {
      "seq": 59,
      "t": 1793429758.267471,
      "utc": "2026-10-31T06:55:58.267Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "put_record",
      "kind": "request",
      "bytes": 2980,
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "docs.README.md",
        "expected_version": 0
      }
    },
    {
      "seq": 60,
      "t": 1793429758.276561,
      "utc": "2026-10-31T06:55:58.276Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "put_record",
      "kind": "response",
      "corr": 59,
      "status": 200,
      "bytes": 6040,
      "latency_ms": 10,
      "outcome": "ok",
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "docs.README.md",
        "version": 1
      }
    },
    {
      "seq": 61,
      "t": 1793429758.322244,
      "utc": "2026-10-31T06:55:58.322Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 243567,
      "call": 20,
      "api": "responses"
    },
    {
      "seq": 62,
      "t": 1793429765.572977,
      "utc": "2026-10-31T06:56:05.572Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 61,
      "call": 20,
      "status": 200,
      "bytes": 279367,
      "latency_ms": 7205,
      "outcome": "completed",
      "input_tokens": 50530,
      "cached_tokens": 48753,
      "output_tokens": 543,
      "reasoning_tokens": 13,
      "cost_usd_estimate": 0.000384
    },
    {
      "seq": 63,
      "t": 1793429765.595392,
      "utc": "2026-10-31T06:56:05.595Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "put_record",
      "kind": "request",
      "bytes": 1343,
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "data.availability.csv",
        "expected_version": 0
      }
    },
    {
      "seq": 64,
      "t": 1793429765.616536,
      "utc": "2026-10-31T06:56:05.616Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "put_record",
      "kind": "response",
      "corr": 63,
      "status": 200,
      "bytes": 2663,
      "latency_ms": 20,
      "outcome": "ok",
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "data.availability.csv",
        "version": 1
      }
    },
    {
      "seq": 65,
      "t": 1793429765.689231,
      "utc": "2026-10-31T06:56:05.689Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 247836,
      "call": 21,
      "api": "responses"
    },
    {
      "seq": 66,
      "t": 1793429778.917952,
      "utc": "2026-10-31T06:56:18.917Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 65,
      "call": 21,
      "status": 200,
      "bytes": 317344,
      "latency_ms": 13185,
      "outcome": "completed",
      "input_tokens": 51606,
      "cached_tokens": 50417,
      "output_tokens": 2390,
      "reasoning_tokens": 20,
      "cost_usd_estimate": 0.000698
    },
    {
      "seq": 67,
      "t": 1793429779.006159,
      "utc": "2026-10-31T06:56:19.006Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "put_record",
      "kind": "request",
      "bytes": 9585,
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "code.shift_scheduler.py",
        "expected_version": 0
      }
    },
    {
      "seq": 68,
      "t": 1793429779.049179,
      "utc": "2026-10-31T06:56:19.049Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "put_record",
      "kind": "response",
      "corr": 67,
      "status": 200,
      "bytes": 20399,
      "latency_ms": 88,
      "outcome": "ok",
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "code.shift_scheduler.py",
        "version": 1
      }
    },
    {
      "seq": 69,
      "t": 1793429779.50268,
      "utc": "2026-10-31T06:56:19.502Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 271093,
      "call": 22,
      "api": "responses"
    },
    {
      "seq": 70,
      "t": 1793429790.671789,
      "utc": "2026-10-31T06:56:30.671Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 69,
      "call": 22,
      "status": 200,
      "bytes": 313435,
      "latency_ms": 11527,
      "outcome": "completed",
      "input_tokens": 56603,
      "cached_tokens": 51569,
      "output_tokens": 1934,
      "reasoning_tokens": 4,
      "cost_usd_estimate": 0.000993
    },
    {
      "seq": 71,
      "t": 1793429790.861937,
      "utc": "2026-10-31T06:56:30.861Z",
      "from": "muse-code (builder)",
      "to": "tessera (MCP)",
      "op": "put_record",
      "kind": "request",
      "bytes": 8344,
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "tests.test_shift_scheduler.py",
        "expected_version": 0
      }
    },
    {
      "seq": 72,
      "t": 1793429790.989244,
      "utc": "2026-10-31T06:56:30.989Z",
      "from": "tessera (MCP)",
      "to": "muse-code (builder)",
      "op": "put_record",
      "kind": "response",
      "corr": 71,
      "status": 200,
      "bytes": 18181,
      "latency_ms": 139,
      "outcome": "ok",
      "record": {
        "store": "kit.volunteer-shifts",
        "key": "tests.test_shift_scheduler.py",
        "version": 1
      }
    },
    {
      "seq": 73,
      "t": 1793429791.202232,
      "utc": "2026-10-31T06:56:31.202Z",
      "from": "muse-code (builder)",
      "to": "meta model api (muse-spark-1.3-contributor)",
      "op": "model call",
      "kind": "request",
      "bytes": 292396,
      "call": 23,
      "api": "responses"
    },
    {
      "seq": 74,
      "t": 1793429821.213981,
      "utc": "2026-10-31T06:57:01.213Z",
      "from": "meta model api (muse-spark-1.3-contributor)",
      "to": "muse-code (builder)",
      "op": "model call",
      "kind": "response",
      "corr": 73,
      "call": 23,
      "status": 200,
      "bytes": 330604,
      "latency_ms": 30173,
      "outcome": "completed",
      "input_tokens": 60792,
      "cached_tokens": 56561,
      "output_tokens": 2238,
      "reasoning_tokens": 1449,
      "cost_usd_estimate": 0.000984
    }
  ],
  "tessera_events": [
    {
      "seq": 13,
      "ts": 1793429642.68,
      "type": "claimed",
      "task_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "worker": "muse-code-builder",
      "fencing_token": 1,
      "detail": {
        "attempt": 1
      }
    },
    {
      "seq": 14,
      "ts": 1793429669.002,
      "type": "heartbeat",
      "task_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "worker": "muse-code-builder",
      "fencing_token": 1,
      "detail": {}
    },
    {
      "seq": 15,
      "ts": 1793429707.162,
      "type": "heartbeat",
      "task_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "worker": "muse-code-builder",
      "fencing_token": 1,
      "detail": {}
    },
    {
      "seq": 16,
      "ts": 1793429750.401,
      "type": "heartbeat",
      "task_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "queue": "eq02",
      "worker": "muse-code-builder",
      "fencing_token": 1,
      "detail": {}
    },
    {
      "seq": 17,
      "ts": 1793429758.27,
      "type": "records_written",
      "task_id": null,
      "queue": null,
      "worker": null,
      "fencing_token": null,
      "detail": {
        "writes": [
          {
            "key": "docs.README.md",
            "store": "kit.volunteer-shifts",
            "version": 1
          }
        ]
      }
    },
    {
      "seq": 18,
      "ts": 1793429765.598,
      "type": "records_written",
      "task_id": null,
      "queue": null,
      "worker": null,
      "fencing_token": null,
      "detail": {
        "writes": [
          {
            "key": "data.availability.csv",
            "store": "kit.volunteer-shifts",
            "version": 1
          }
        ]
      }
    },
    {
      "seq": 19,
      "ts": 1793429779.015,
      "type": "records_written",
      "task_id": null,
      "queue": null,
      "worker": null,
      "fencing_token": null,
      "detail": {
        "writes": [
          {
            "key": "code.shift_scheduler.py",
            "store": "kit.volunteer-shifts",
            "version": 1
          }
        ]
      }
    },
    {
      "seq": 20,
      "ts": 1793429790.871,
      "type": "records_written",
      "task_id": null,
      "queue": null,
      "worker": null,
      "fencing_token": null,
      "detail": {
        "writes": [
          {
            "key": "tests.test_shift_scheduler.py",
            "store": "kit.volunteer-shifts",
            "version": 1
          }
        ]
      }
    }
  ],
  "records": {
    "kit nouns": [
      {
        "namespace": "main",
        "store": "kit.volunteer-shifts",
        "key": "code.shift_scheduler.py",
        "version": 1,
        "value": "{\"content\": \"\\\"\\\"\\\"Weekly volunteer shift scheduler (synthetic data only).\\n\\nReads a volunteer availability CSV, builds a weekly schedule that meets\\nevery coverage rule using deterministic backtracking search, and writes\\nthe schedule as CSV with columns shift,volunteer_id,display_name,role.\\n\\\"\\\"\\\"\\n\\nimport argparse\\nimport csv\\nimport itertools\\nimport sys\\n\\nDAYS = [\\\"Mon\\\", \\\"Tue\\\", \\\"Wed\\\", \\\"Thu\\\", \\\"Fri\\\", \\\"Sat\\\"]\\nSLOTS = [\\\"AM\\\", \\\"PM\\\"]\\nSHIFTS = [d + \\\"-\\\" + s for d in DAYS for s in SLOTS]\\n\\nSCHEDULE_FIELDS = [\\\"shift\\\", \\\"volunteer_id\\\", \\\"display_name\\\", \\\"role\\\"]\\n\\n\\ndef load_volunteers(path):\\n    \\\"\\\"\\\"Read the availability CSV into a list of volunteer dicts.\\\"\\\"\\\"\\n    volunteers = []\\n    with open(path, newline=\\\"\\\", encoding=\\\"utf-8\\\") as fh:\\n        reader = csv.DictReader(fh)\\n        for row in reader:\\n            available = set()\\n            raw = (row.get(\\\"available\\\") or \\\"\\\").strip()\\n            if raw:\\n                for part in raw.split(\\\";\\\"):\\n                    part = part.strip()\\n                    if part:\\n                        available.add(part)\\n            volunteers.append(\\n                {\\n                    \\\"volunteer_id\\\": (row.get(\\\"volunteer_id\\\") or \\\"\\\").strip(),\\n                    \\\"display_name\\\": (row.get(\\\"display_name\\\") or \\\"\\\").strip(),\\n                    \\\"role\\\": (row.get(\\\"role\\\") or \\\"\\\").strip(),\\n                    \\\"max_shifts\\\": int((row.get(\\\"max_shifts\\\") or \\\"0\\\").strip()),\\n                    \\\"available\\\": available,\\n                }\\n            )\\n    volunteers.sort(key=lambda v: v[\\\"volunteer_id\\\"])\\n    return volunteers\\n\\n\\ndef check_coverage(schedule, volunteers):\\n    \\\"\\\"\\\"Return a list of violation strings; empty when every rule holds.\\\"\\\"\\\"\\n    violations = []\\n    by_id = {v[\\\"volunteer_id\\\"]: v for v in volunteers}\\n    by_shift = {s: [] for s in SHIFTS}\\n    for entry in schedule or []:\\n        shift = entry.get(\\\"shift\\\")\\n        vid = entry.get(\\\"volunteer_id\\\")\\n        if shift not in by_shift:\\n            violations.append(\\\"unknown shift: %r\\\" % (shift,))\\n            continue\\n        if vid not in by_id:\\n            violations.append(\\\"unknown volunteer %r on %s\\\" % (vid, shift))\\n            continue\\n        by_shift[shift].append(vid)\\n\\n    for shift in SHIFTS:\\n        assigned = by_shift[shift]\\n        if len(assigned) < 2:\\n            violations.append(\\n                \\\"%s has %d volunteer(s); at least 2 required\\\"\\n                % (shift, len(assigned))\\n            )\\n        if not any(\\n            by_id[vid][\\\"role\\\"] == \\\"lead\\\" for vid in assigned if vid in by_id\\n        ):\\n            violations.append(\\\"%s has no lead volunteer\\\" % shift)\\n\\n    counts = {}\\n    day_of = {}\\n    for entry in schedule or []:\\n        shift = entry.get(\\\"shift\\\")\\n        vid = entry.get(\\\"volunteer_id\\\")\\n        if shift not in by_shift or vid not in by_id:\\n            continue\\n        counts[vid] = counts.get(vid, 0) + 1\\n        day = shift.split(\\\"-\\\")[0]\\n        day_of.setdefault(vid, []).append((day, shift))\\n        if shift not in by_id[vid][\\\"available\\\"]:\\n            violations.append(\\n                \\\"%s works %s but is not available for it\\\" % (vid, shift)\\n            )\\n\\n    for vid, n in sorted(counts.items()):\\n        if n > by_id[vid][\\\"max_shifts\\\"]:\\n            violations.append(\\n                \\\"%s works %d shifts; max is %d\\\"\\n                % (vid, n, by_id[vid][\\\"max_shifts\\\"])\\n            )\\n\\n    for vid in sorted(day_of):\\n        seen_days = {}\\n        for day, shift in day_of[vid]:\\n            seen_days.setdefault(day, []).append(shift)\\n        for day in sorted(seen_days):\\n            if len(seen_days[day]) > 1:\\n                violations.append(\\n                    \\\"%s works both shifts on %s\\\" % (vid, day)\\n                )\\n\\n    return violations\\n\\n\\ndef build_schedule(volunteers):\\n    \\\"\\\"\\\"Build a valid schedule or return None when impossible.\\n\\n    Assigns exactly 2 volunteers to each of the 12 shifts using\\n    deterministic depth-first backtracking. Shifts are attempted in order\\n    of fewest eligible volunteers (ties broken by canonical shift order);\\n    candidate pairs are tried in sorted volunteer_id order.\\n    \\\"\\\"\\\"\\n    by_id = {v[\\\"volunteer_id\\\"]: v for v in volunteers}\\n\\n    def eligible(shift):\\n        return sorted(\\n            vid\\n            for vid, v in by_id.items()\\n            if shift in v[\\\"available\\\"]\\n        )\\n\\n    order = sorted(SHIFTS, key=lambda s: (len(eligible(s)), SHIFTS.index(s)))\\n\\n    assignment = {}\\n    counts = {vid: 0 for vid in by_id}\\n    worked_days = {vid: set() for vid in by_id}\\n\\n    def candidates_for(shift):\\n        day = shift.split(\\\"-\\\")[0]\\n        out = []\\n        for vid in sorted(by_id):\\n            v = by_id[vid]\\n            if shift not in v[\\\"available\\\"]:\\n                continue\\n            if counts[vid] >= v[\\\"max_shifts\\\"]:\\n                continue\\n            if day in worked_days[vid]:\\n                continue\\n            out.append(vid)\\n        return out\\n\\n    def feasible_remaining(pos):\\n        # Forward check: every unassigned shift must still be fillable.\\n        for j in range(pos, len(order)):\\n            shift = order[j]\\n            cands = candidates_for(shift)\\n            if len(cands) < 2:\\n                return False\\n            if not any(by_id[c][\\\"role\\\"] == \\\"lead\\\" for c in cands):\\n                return False\\n        return True\\n\\n    def dfs(pos):\\n        if pos == len(order):\\n            return True\\n        shift = order[pos]\\n        cands = candidates_for(shift)\\n        pairs = [\\n            p\\n            for p in itertools.combinations(cands, 2)\\n            if any(by_id[c][\\\"role\\\"] == \\\"lead\\\" for c in p)\\n        ]\\n        for a, b in pairs:\\n            day = shift.split(\\\"-\\\")[0]\\n            assignment[shift] = (a, b)\\n            counts[a] += 1\\n            counts[b] += 1\\n            worked_days[a].add(day)\\n            worked_days[b].add(day)\\n            if feasible_remaining(pos + 1) and dfs(pos + 1):\\n                return True\\n            counts[a] -= 1\\n            counts[b] -= 1\\n            worked_days[a].discard(day)\\n            worked_days[b].discard(day)\\n            del assignment[shift]\\n        return False\\n\\n    if not dfs(0):\\n        return None\\n\\n    schedule = []\\n    for shift in SHIFTS:\\n        for vid in sorted(assignment[shift]):\\n            v = by_id[vid]\\n            schedule.append(\\n                {\\n                    \\\"shift\\\": shift,\\n                    \\\"volunteer_id\\\": vid,\\n                    \\\"display_name\\\": v[\\\"display_name\\\"],\\n                    \\\"role\\\": v[\\\"role\\\"],\\n                }\\n            )\\n    return schedule\\n\\n\\ndef write_schedule(schedule, path):\\n    with open(path, \\\"w\\\", newline=\\\"\\\", encoding=\\\"utf-8\\\") as fh:\\n        writer = csv.DictWriter(fh, fieldnames=SCHEDULE_FIELDS)\\n        writer.writeheader()\\n        for entry in schedule:\\n            writer.writerow({k: entry.get(k, \\\"\\\") for k in SCHEDULE_FIELDS})\\n\\n\\ndef main(argv=None):\\n    parser = argparse.ArgumentParser(\\n        description=\\\"Build a weekly volunteer shift schedule.\\\"\\n    )\\n    parser.add_argument(\\\"input\\\", help=\\\"availability CSV path\\\")\\n    parser.add_argument(\\n        \\\"--out\\\", default=\\\"schedule.csv\\\", help=\\\"output schedule CSV path\\\"\\n    )\\n    args = parser.parse_args(argv)\\n\\n    try:\\n        volunteers = load_volunteers(args.input)\\n    except (OSError, ValueError) as exc:\\n        print(\\\"error: cannot read input: %s\\\" % exc)\\n        return 2\\n\\n    schedule = build_schedule(volunteers)\\n    if schedule is None:\\n        print(\\\"error: no valid schedule exists for this input\\\")\\n        return 2\\n\\n    write_schedule(schedule, args.out)\\n    for entry in schedule:\\n        print(\\n            \\\"%s,%s,%s,%s\\\"\\n            % (\\n                entry[\\\"shift\\\"],\\n                entry[\\\"volunteer_id\\\"],\\n                entry[\\\"display_name\\\"],\\n                entry[\\\"role\\\"],\\n            )\\n        )\\n    violations = check_coverage(schedule, volunteers)\\n    print(\\\"violations: %d\\\" % len(violations))\\n    for v in violations:\\n        print(\\\"violation: %s\\\" % v)\\n    return 0 if not violations else 1\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    sys.exit(main())\\n\", \"path\": \"shift_scheduler.py\", \"sha256\": \"b3c4b5867fa3e26b180a5c3185f9de27a13dc096c92a9ea9f4d2f316eef1908b\"}",
        "created_at": 1793429779.015,
        "producer_surface": "surface:eranos",
        "visibility_scope": "surface_only",
        "allowed_surfaces": [],
        "target_cohorts": [
          "all"
        ],
        "tags": [],
        "owner_key_id": "k_cc60da07d74a"
      },
      {
        "namespace": "main",
        "store": "kit.volunteer-shifts",
        "key": "data.availability.csv",
        "version": 1,
        "value": "{\"content\": \"volunteer_id,display_name,role,max_shifts,available\\nV01,Volunteer 01,lead,3,Mon-PM;Tue-PM;Thu-AM;Thu-PM;Fri-AM\\nV02,Volunteer 02,lead,3,Wed-AM;Wed-PM;Thu-PM;Sat-AM\\nV03,Volunteer 03,lead,3,Mon-AM;Mon-PM;Tue-AM;Wed-PM;Fri-AM\\nV04,Volunteer 04,lead,3,Mon-AM;Mon-PM;Wed-PM;Thu-PM;Sat-PM\\nV05,Volunteer 05,lead,3,Tue-AM;Tue-PM;Fri-PM\\nV06,Volunteer 06,general,2,Mon-AM;Fri-AM\\nV07,Volunteer 07,general,2,Thu-AM;Thu-PM;Fri-AM\\nV08,Volunteer 08,general,2,Mon-AM;Tue-AM;Fri-AM;Sat-AM\\nV09,Volunteer 09,general,2,Tue-AM;Wed-PM;Fri-PM;Sat-AM\\nV10,Volunteer 10,general,2,Mon-AM;Mon-PM\\nV11,Volunteer 11,general,2,Wed-AM;Thu-AM;Fri-PM\\nV12,Volunteer 12,general,2,Tue-PM;Thu-AM\\nV13,Volunteer 13,general,2,Mon-PM;Thu-AM;Sat-PM\\nV14,Volunteer 14,general,2,Mon-AM;Tue-AM;Wed-AM;Thu-AM\\n\", \"path\": \"data/availability.csv\", \"sha256\": \"6709f71fb10238ebe4ce06c987937b1ba5301646be9b5bd474f1a4844089f795\"}",
        "created_at": 1793429765.598,
        "producer_surface": "surface:eranos",
        "visibility_scope": "surface_only",
        "allowed_surfaces": [],
        "target_cohorts": [
          "all"
        ],
        "tags": [],
        "owner_key_id": "k_cc60da07d74a"
      },
      {
        "namespace": "main",
        "store": "kit.volunteer-shifts",
        "key": "docs.README.md",
        "version": 1,
        "value": "{\"content\": \"# Volunteer Shift Schedule Kit (synthetic data only)\\n\\nSmall kit for a fictional food pantry example. It turns a volunteer\\navailability CSV into a weekly shift schedule that meets the coverage\\nrules below. Everything here was built and tested on **synthetic data\\nonly** — all names are placeholders like \\\"Volunteer 01\\\". No real names\\nor contact details are used anywhere.\\n\\n## What the kit does\\n\\n`shift_scheduler.py` (Python 3 standard library only) reads the\\navailability CSV, searches for a weekly assignment with deterministic\\nbacktracking search, writes the schedule as CSV, and checks it against\\nevery coverage rule.\\n\\n## Input format\\n\\n`data/availability.csv` has columns:\\n\\n- `volunteer_id` — e.g. `V01`\\n- `display_name` — placeholder name, e.g. `Volunteer 01`\\n- `role` — `lead` or `general`\\n- `max_shifts` — most shifts this volunteer works in the week\\n- `available` — shifts separated by `;`, each written `Day-Slot`\\n  (e.g. `Mon-PM;Tue-PM;Thu-AM`)\\n\\n## Coverage rules\\n\\n- Shifts are Mon, Tue, Wed, Thu, Fri and Sat, each with an AM and a PM\\n  slot: 12 shifts, written `Day-Slot` (for example `Mon-AM`).\\n- Every shift has at least 2 volunteers.\\n- Every shift has at least 1 volunteer whose role is `lead`.\\n- A volunteer works only shifts listed in their `available` column.\\n- No volunteer works more shifts in the week than their `max_shifts`.\\n- No volunteer works both the AM and the PM shift on the same day.\\n\\n## How to run it\\n\\n```sh\\npython3 shift_scheduler.py data/availability.csv\\npython3 shift_scheduler.py data/availability.csv --out schedule.csv\\n```\\n\\nThe program prints the schedule and the number of violations. It exits\\n`0` only when there are no violations, and exits `2` with a clear\\nmessage when no valid schedule exists. Output CSV columns are\\n`shift,volunteer_id,display_name,role`.\\n\\n`check_coverage(schedule, volunteers)` is importable from\\n`shift_scheduler` and returns a list of violation strings (empty when\\nevery rule holds).\\n\\n## How to run the tests\\n\\n```sh\\npython3 -m unittest discover -s tests -v\\n```\\n\\nThe tests cover: the synthetic CSV yields a schedule with zero\\nviolations; `check_coverage` reports each broken rule (unavailable\\nvolunteer, over `max_shifts`, no lead, both shifts on one day, fewer\\nthan 2 people); and an impossible input is reported, not silently\\nscheduled.\\n\", \"path\": \"README.md\", \"sha256\": \"faa26f17fdde72804043644e45ad7684b23211a8bae724530834e189649099cb\"}",
        "created_at": 1793429758.27,
        "producer_surface": "surface:eranos",
        "visibility_scope": "surface_only",
        "allowed_surfaces": [],
        "target_cohorts": [
          "all"
        ],
        "tags": [],
        "owner_key_id": "k_cc60da07d74a"
      },
      {
        "namespace": "main",
        "store": "kit.volunteer-shifts",
        "key": "tests.test_shift_scheduler.py",
        "version": 1,
        "value": "{\"content\": \"\\\"\\\"\\\"unittest tests for shift_scheduler (synthetic data only).\\\"\\\"\\\"\\n\\nimport csv\\nimport os\\nimport subprocess\\nimport sys\\nimport tempfile\\nimport unittest\\n\\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), \\\"..\\\"))\\n\\nfrom shift_scheduler import (  # noqa: E402\\n    SHIFTS,\\n    build_schedule,\\n    check_coverage,\\n    load_volunteers,\\n)\\n\\nDATA_CSV = os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"data\\\",\\n                         \\\"availability.csv\\\")\\n\\n\\ndef make_volunteers():\\n    return [\\n        {\\\"volunteer_id\\\": \\\"L1\\\", \\\"display_name\\\": \\\"Lead One\\\", \\\"role\\\": \\\"lead\\\",\\n         \\\"max_shifts\\\": 6, \\\"available\\\": set(SHIFTS)},\\n        {\\\"volunteer_id\\\": \\\"L2\\\", \\\"display_name\\\": \\\"Lead Two\\\", \\\"role\\\": \\\"lead\\\",\\n         \\\"max_shifts\\\": 6, \\\"available\\\": set(SHIFTS)},\\n        {\\\"volunteer_id\\\": \\\"G1\\\", \\\"display_name\\\": \\\"Gen One\\\", \\\"role\\\": \\\"general\\\",\\n         \\\"max_shifts\\\": 6, \\\"available\\\": set(SHIFTS)},\\n        {\\\"volunteer_id\\\": \\\"G2\\\", \\\"display_name\\\": \\\"Gen Two\\\", \\\"role\\\": \\\"general\\\",\\n         \\\"max_shifts\\\": 6, \\\"available\\\": set(SHIFTS)},\\n    ]\\n\\n\\ndef full_schedule(volunteers):\\n    sched = []\\n    for shift in SHIFTS:\\n        for v in volunteers:\\n            sched.append({\\\"shift\\\": shift, \\\"volunteer_id\\\": v[\\\"volunteer_id\\\"],\\n                          \\\"display_name\\\": v[\\\"display_name\\\"], \\\"role\\\": v[\\\"role\\\"]})\\n    return sched\\n\\n\\nclass TestSyntheticSchedule(unittest.TestCase):\\n    def test_synthetic_csv_yields_zero_violations(self):\\n        volunteers = load_volunteers(DATA_CSV)\\n        schedule = build_schedule(volunteers)\\n        self.assertIsNotNone(schedule,\\n                             \\\"expected a valid schedule for synthetic CSV\\\")\\n        self.assertEqual(check_coverage(schedule, volunteers), [])\\n        # Deterministic: same input gives same schedule.\\n        again = build_schedule(volunteers)\\n        self.assertEqual(schedule, again)\\n\\n    def test_cli_and_output_csv(self):\\n        with tempfile.TemporaryDirectory() as tmp:\\n            out = os.path.join(tmp, \\\"schedule.csv\\\")\\n            proc = subprocess.run(\\n                [sys.executable, \\\"shift_scheduler.py\\\", DATA_CSV,\\n                 \\\"--out\\\", out],\\n                cwd=os.path.join(os.path.dirname(__file__), \\\"..\\\"),\\n                capture_output=True, text=True)\\n            self.assertEqual(proc.returncode, 0, proc.stdout + proc.stderr)\\n            self.assertIn(\\\"violations: 0\\\", proc.stdout)\\n            with open(out, newline=\\\"\\\", encoding=\\\"utf-8\\\") as fh:\\n                rows = list(csv.DictReader(fh))\\n            self.assertEqual(\\n                [r for r in rows][:1][0].keys() if rows else set(),\\n                {\\\"shift\\\", \\\"volunteer_id\\\", \\\"display_name\\\", \\\"role\\\"})\\n            volunteers = load_volunteers(DATA_CSV)\\n            self.assertEqual(check_coverage(rows, volunteers), [])\\n\\n\\nclass TestCheckCoverageRules(unittest.TestCase):\\n    def test_unavailable_volunteer_reported(self):\\n        volunteers = make_volunteers()\\n        sched = full_schedule(volunteers)\\n        sched[0] = {\\\"shift\\\": \\\"Mon-AM\\\", \\\"volunteer_id\\\": \\\"G1\\\",\\n                    \\\"display_name\\\": \\\"Gen One\\\", \\\"role\\\": \\\"general\\\"}\\n        volunteers[2][\\\"available\\\"] = set(SHIFTS) - {\\\"Mon-AM\\\"}\\n        violations = check_coverage(sched, volunteers)\\n        self.assertTrue(any(\\\"not available\\\" in v for v in violations),\\n                        violations)\\n\\n    def test_over_max_shifts_reported(self):\\n        volunteers = make_volunteers()\\n        volunteers[0][\\\"max_shifts\\\"] = 1\\n        sched = full_schedule(volunteers)\\n        violations = check_coverage(sched, volunteers)\\n        self.assertTrue(any(\\\"max\\\" in v for v in violations), violations)\\n\\n    def test_no_lead_reported(self):\\n        volunteers = make_volunteers()\\n        sched = []\\n        for shift in SHIFTS:\\n            sched.append({\\\"shift\\\": shift, \\\"volunteer_id\\\": \\\"G1\\\",\\n                          \\\"display_name\\\": \\\"Gen One\\\", \\\"role\\\": \\\"general\\\"})\\n            sched.append({\\\"shift\\\": shift, \\\"volunteer_id\\\": \\\"G2\\\",\\n                          \\\"display_name\\\": \\\"Gen Two\\\", \\\"role\\\": \\\"general\\\"})\\n        violations = check_coverage(sched, volunteers)\\n        self.assertTrue(any(\\\"lead\\\" in v for v in violations), violations)\\n\\n    def test_both_shifts_same_day_reported(self):\\n        volunteers = make_volunteers()\\n        sched = []\\n        for shift in SHIFTS:\\n            if shift in (\\\"Mon-AM\\\", \\\"Mon-PM\\\"):\\n                sched.append({\\\"shift\\\": shift, \\\"volunteer_id\\\": \\\"L1\\\",\\n                              \\\"display_name\\\": \\\"Lead One\\\", \\\"role\\\": \\\"lead\\\"})\\n                sched.append({\\\"shift\\\": shift, \\\"volunteer_id\\\": \\\"G1\\\",\\n                              \\\"display_name\\\": \\\"Gen One\\\", \\\"role\\\": \\\"general\\\"})\\n            else:\\n                sched.append({\\\"shift\\\": shift, \\\"volunteer_id\\\": \\\"L2\\\",\\n                              \\\"display_name\\\": \\\"Lead Two\\\", \\\"role\\\": \\\"lead\\\"})\\n                sched.append({\\\"shift\\\": shift, \\\"volunteer_id\\\": \\\"G2\\\",\\n                              \\\"display_name\\\": \\\"Gen Two\\\", \\\"role\\\": \\\"general\\\"})\\n        violations = check_coverage(sched, volunteers)\\n        self.assertTrue(any(\\\"both shifts\\\" in v for v in violations),\\n                        violations)\\n\\n    def test_fewer_than_two_reported(self):\\n        volunteers = make_volunteers()\\n        sched = [{\\\"shift\\\": \\\"Mon-AM\\\", \\\"volunteer_id\\\": \\\"L1\\\",\\n                  \\\"display_name\\\": \\\"Lead One\\\", \\\"role\\\": \\\"lead\\\"}]\\n        violations = check_coverage(sched, volunteers)\\n        self.assertTrue(any(\\\"Mon-AM\\\" in v for v in violations), violations)\\n\\n    def test_impossible_input_reported_not_silently_scheduled(self):\\n        volunteers = [\\n            {\\\"volunteer_id\\\": \\\"L1\\\", \\\"display_name\\\": \\\"Lead One\\\",\\n             \\\"role\\\": \\\"lead\\\", \\\"max_shifts\\\": 1, \\\"available\\\": {\\\"Mon-AM\\\"}},\\n        ]\\n        self.assertIsNone(build_schedule(volunteers))\\n        with tempfile.TemporaryDirectory() as tmp:\\n            inp = os.path.join(tmp, \\\"impossible.csv\\\")\\n            with open(inp, \\\"w\\\", newline=\\\"\\\", encoding=\\\"utf-8\\\") as fh:\\n                w = csv.writer(fh)\\n                w.writerow([\\\"volunteer_id\\\", \\\"display_name\\\", \\\"role\\\",\\n                            \\\"max_shifts\\\", \\\"available\\\"])\\n                w.writerow([\\\"L1\\\", \\\"Lead One\\\", \\\"lead\\\", \\\"1\\\", \\\"Mon-AM\\\"])\\n            proc = subprocess.run(\\n                [sys.executable, \\\"shift_scheduler.py\\\", inp,\\n                 \\\"--out\\\", os.path.join(tmp, \\\"o.csv\\\")],\\n                cwd=os.path.join(os.path.dirname(__file__), \\\"..\\\"),\\n                capture_output=True, text=True)\\n            self.assertEqual(proc.returncode, 2,\\n                             proc.stdout + proc.stderr)\\n            self.assertIn(\\\"no valid schedule\\\", proc.stdout.lower())\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    unittest.main()\\n\", \"path\": \"tests/test_shift_scheduler.py\", \"sha256\": \"ec0e90bfaf5f0edb51f289168e689932b628d5c86fa46f98005670eb85f29a76\"}",
        "created_at": 1793429790.871,
        "producer_surface": "surface:eranos",
        "visibility_scope": "surface_only",
        "allowed_surfaces": [],
        "target_cohorts": [
          "all"
        ],
        "tags": [],
        "owner_key_id": "k_cc60da07d74a"
      }
    ],
    "build result, which pins the four nouns and their digest": {
      "task_id": "t_06e3ef2acaa54c7297b5c0dfca3e2c70",
      "fencing_token": 1,
      "idempotency_key": "t_06e3ef2acaa54c7297b5c0dfca3e2c70:build",
      "ok": true,
      "output": "{\"blob_sha256\": \"a30b48b17ba2a20c60a3fdabcb67874760e96c4215d58ed4141288d763f5d34e\", \"draft\": {\"files\": {\"README.md\": {\"key\": \"docs.README.md\", \"sha256\": \"faa26f17fdde72804043644e45ad7684b23211a8bae724530834e189649099cb\", \"version\": 1}, \"data/availability.csv\": {\"key\": \"data.availability.csv\", \"sha256\": \"6709f71fb10238ebe4ce06c987937b1ba5301646be9b5bd474f1a4844089f795\", \"version\": 1}, \"shift_scheduler.py\": {\"key\": \"code.shift_scheduler.py\", \"sha256\": \"b3c4b5867fa3e26b180a5c3185f9de27a13dc096c92a9ea9f4d2f316eef1908b\", \"version\": 1}, \"tests/test_shift_scheduler.py\": {\"key\": \"tests.test_shift_scheduler.py\", \"sha256\": \"ec0e90bfaf5f0edb51f289168e689932b628d5c86fa46f98005670eb85f29a76\", \"version\": 1}}, \"kit_id\": \"kit-volunteer-shifts-20261031t065341z\", \"store\": \"kit.volunteer-shifts\"}, \"kit_id\": \"kit-volunteer-shifts-20261031t065341z\", \"node\": \"draft\", \"verification\": {\"commands\": [\"python3 -m unittest discover -s tests -v -> exit 0\", \"python3 shift_scheduler.py data/availability.csv -> exit 0\"], \"coverage_violations\": 0, \"passed\": true, \"tests_run\": 8}}",
      "error": null,
      "receipt_hash": "aee19212277a83ff5c2e69964b9bf7a19a7e32455cb6ecb9d21d5d6c06fc0491",
      "state_after": "done",
      "completed_by_key": "k_cc60da07d74a",
      "labels": {},
      "created_at": 1793429827.76
    }
  }
}