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-e\n\n# Install required Python packages\npython3 -m pip install --upgrade pip\npip install \\\n numpy \\\n pandas \\\n jupyterlab \\\n ipywidgets \\\n statsmodels \\\n openpyxl \\\n matplotlib \\\n streamlit \\\n fastapi \\\n uvicorn \\\n hypothesis\n\necho \"✅ Python environment ready\"","mode","on","preset_allowlist","allowlist_domains","pypi.org, github.com, files.pythonhosted.org, deb.debian.org, security.ubuntu.com, raw.githubusercontent.com","allowlist_rules","denylist_domains","safe_methods_only","number",3145,"https://github.com/stranske/Trend_Model_Project/pull/3145","state","closed","merged","mergeable","draft","title","chore(codex): bootstrap PR for issue #3144","body","\n## Summary\nEnsure that test coverage exceeds 95% for all program files.\n\n\n**Scope:** Add test coverage for any program functionality with test coverage under 95% or for essential program functionality that does not currently have test coverage\n\n\n\n\n## Automated Status Summary\n#### Scope\nAdd test coverage for any program functionality with test coverage under 95% or for essential program functionality that does not currently have test coverage\n\n#### Tasks\n- [ ] Run soft coverage and prepare a list of the files with lowest coverage from least coverage on up for any file with less than 95% test coverage or any file with significant functionality that isn't covered.\n- [ ] Increase test coverage incrementally for one set of related issues or 1 file below at a time\n - [ ] __init__.py\n - [ ] data.py\n - [ ] presets.py\n - [ ] harness.py\n - [ ] regimes.py\n - [ ] pipeline.py\n - [ ] validators.py\n - [ ] run_analysis.py\n - [ ] market_data.py\n - [ ] signal_presets.py\n - [ ] frequency.py\n - [ ] signals.py\n - [ ] bootstrap.p\n - [ ] risk.py\n - [ ] bundle.py\n - [ ] cli.py\n - [ ] optimizer.py\n - [ ] model.py\n - [ ] engine.py\n\n#### Acceptance criteria\n- [ ] Test coverage exceeds 95% for each file\n- [ ] Essential functions for the program have full test coverage\n\n**Head SHA:** 6863acab0a842c81668d5d462bfda00fea603b71\n**Latest Runs:** ❔ in progress — Gate\n**Required:** gate: ❔ in progress\n\n| Workflow / Job | Result | Logs |\n|----------------|--------|------|\n| Agents PR meta manager | ❔ in progress | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18921980132) |\n| Gate | ❔ in progress | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18921980159) |\n| Health 40 Sweep | ❔ in progress | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18921980121) |\n| Health 44 Gate Branch Protection | ❔ in progress | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18921980092) |\n| Health 45 Agents Guard | ❔ in progress | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18921980111) |\n","base","head","base_sha","521ebc4bfc3fba7fb349a6622053d795910af23c","head_sha","6863acab0a842c81668d5d462bfda00fea603b71","head_repo_id","head_repo_full_name","merge_commit_sha","23738591dae4c04b97c9b34929e1d528d82926e8","comments","diff","user",{"_260":261,"_159":261,"_308":-5,"_264":265,"_53":262},"commits",5,"changed_files","additions","deletions","email","events",[],[],"messages",[],{"_104":467,"_468":469,"_470":471,"_472":473,"_474":-5},{"_104":442,"_108":105,"_332":443},{"_104":317,"_318":319},"partial_repo_snapshot","files",[320],{"_321":322,"_323":-5,"_324":325,"_326":-5},"path","tests/test_util_frequency_internal.py","contents","line_range_contents",[327],"missing_reason",{"_328":329,"_330":331,"_332":333,"_334":70},"line_range_start",1,"line_range_end",161,"content",[335,177,336,177,337,338,339,177,340,341,342,343,344,345,346,347,348,177,177,349,350,177,351,352,353,354,355,356,357,177,358,359,360,361,362,363,177,177,364,365,177,366,367,368,177,177,369,370,177,371,372,177,177,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,348,388,389,177,390,177,177,391,392,177,393,177,177,394,395,177,396,397,177,177,398,399,177,400,401,177,177,402,403,177,404,405,406,407,408,409,410,357,177,177,411,412,177,413,414,415,177,177,416,417,177,418,419,414,177,177,420,421,177,422,177,423,424,177,425,177,426,427,428,415,177,177,429,430,177,431,352,432,433,434,435,436,356,357,177,437,438,177,439,177,426,440,441],"contains_end_of_file","\"\"\"Additional coverage for `trend_analysis.util.frequency` helpers.\"\"\"","from __future__ import annotations","import numpy as np","import pandas as pd","import pytest","from trend_analysis.util.frequency import ("," FREQUENCY_LABELS,"," FrequencySummary,"," _as_datetime_index,"," _classify_from_diffs,"," _map_inferred,"," _summary_from_code,"," detect_frequency,",")","def test_as_datetime_index_normalises_iterables() -> None:"," \"\"\"Non-index iterables should be converted and sorted chronologically.\"\"\""," idx = _as_datetime_index("," ["," \"2024-01-03\","," \"2024-01-01\","," pd.Timestamp(\"2024-01-02\"),"," ]"," )"," assert isinstance(idx, pd.DatetimeIndex)"," assert list(idx) == ["," pd.Timestamp(\"2024-01-01\"),"," pd.Timestamp(\"2024-01-02\"),"," pd.Timestamp(\"2024-01-03\"),"," ]","def test_as_datetime_index_sorts_existing_index() -> None:"," \"\"\"DatetimeIndex inputs should be returned sorted without modification.\"\"\""," raw = pd.DatetimeIndex([\"2024-01-03\", \"2024-01-01\", \"2024-01-02\"])"," idx = _as_datetime_index(raw)"," assert idx.tolist() == sorted(raw.tolist())","def test_as_datetime_index_rejects_non_datetimes() -> None:"," \"\"\"Invalid inputs should trigger a ValueError rather than NaT leakage.\"\"\""," with pytest.raises(ValueError):"," _as_datetime_index([\"2024-01-01\", \"not-a-date\", 10])","@pytest.mark.parametrize("," (\"freq\", \"expected\"),"," ["," (None, None),"," (\"\", None),"," (\"W-WED\", \"W\"),"," (\"bd\", \"D\"),"," (\"C\", \"D\"),"," (\"15D\", \"D\"),"," (\"SM-15\", \"M\"),"," (\"BM\", \"M\"),"," (\"Q-DEC\", \"Q\"),"," (\"A-DEC\", \"Y\"),"," (\"foo\", None),"," ],","def test_map_inferred_normalises_pandas_codes(freq: str | None, expected: str | None) -> None:"," \"\"\"The inference mapper should collapse Pandas frequency aliases to our codes.\"\"\""," assert _map_inferred(freq) == expected","def test_classify_from_diffs_defaults_to_monthly_when_empty() -> None:"," \"\"\"Empty diffs should return the default monthly cadence.\"\"\""," assert _classify_from_diffs(np.array([])) == \"M\"","def test_classify_from_diffs_requires_signal() -> None:"," \"\"\"Classifying intervals with no recognised cadence should error.\"\"\""," with pytest.raises(ValueError, match=\"irregular spacing\"):"," _classify_from_diffs(np.array([0.0]))","def test_classify_from_diffs_detects_inconsistent_cadence() -> None:"," \"\"\"Mixed cadences without a dominant bucket should raise an error.\"\"\""," with pytest.raises(ValueError, match=\"too irregular\"):"," _classify_from_diffs(np.array([6.0, 250.0]))","def test_summary_from_code_tracks_resampling_targets() -> None:"," \"\"\"The summary helper should describe both the detected cadence and target.\"\"\""," summary = _summary_from_code(\"D\")"," assert summary == FrequencySummary("," code=\"D\","," label=FREQUENCY_LABELS[\"D\"],"," resampled=True,"," target=\"M\","," target_label=FREQUENCY_LABELS[\"M\"],","def test_summary_from_code_for_monthly() -> None:"," \"\"\"Monthly series should not be flagged for resampling.\"\"\""," summary = _summary_from_code(\"M\")"," assert summary.resampled is False"," assert summary.target_label == FREQUENCY_LABELS[\"M\"]","def test_detect_frequency_handles_single_observation() -> None:"," \"\"\"Single point series default to monthly without calling infer_freq.\"\"\""," summary = detect_frequency([pd.Timestamp(\"2024-01-31\")])"," assert summary.code == \"M\"","def test_detect_frequency_uses_inferred_business_day(monkeypatch: pytest.MonkeyPatch) -> None:"," \"\"\"Business-day inference should collapse to the daily cadence.\"\"\""," idx = pd.date_range(\"2024-01-01\", periods=5, freq=\"B\")"," def fake_infer_freq(_: pd.DatetimeIndex) -> str:"," return \"bd\""," monkeypatch.setattr(pd, \"infer_freq\", fake_infer_freq)"," summary = detect_frequency(idx)"," assert summary.code == \"D\""," assert summary.label == FREQUENCY_LABELS[\"D\"]","def test_detect_frequency_falls_back_when_infer_freq_fails(monkeypatch: pytest.MonkeyPatch) -> None:"," \"\"\"Errors from ``pd.infer_freq`` should trigger the diff-based classifier.\"\"\""," idx = pd.DatetimeIndex("," \"2024-03-31\","," \"2024-03-31\", # duplicate should be ignored"," \"2024-06-30\","," \"2024-09-30\","," \"2024-12-31\","," def raise_error(_: pd.DatetimeIndex) -> str:"," raise ValueError(\"no freq\")"," monkeypatch.setattr(pd, \"infer_freq\", raise_error)"," assert summary.code == \"Q\""," assert summary.label == FREQUENCY_LABELS[\"Q\"]","message",[444,445,446,447,448,449,450,451,452,453],{"_454":57,"_57":466},{"_454":456,"_457":461,"_328":329,"_330":462},{"_454":57,"_57":465},{"_454":464,"_321":322,"_328":329,"_330":331},{"_454":456,"_457":458,"_328":329,"_330":459},{"_454":57,"_57":463},{"_454":456,"_457":461,"_328":329,"_330":462},{"_454":57,"_57":460},{"_454":456,"_457":458,"_328":329,"_330":459},{"_454":57,"_57":455},"content_type","\n\n### Scope\nAdd test coverage for any program functionality with test coverage under 95% or for essential program functionality that does not currently have test coverage\n\n### Tasks\n- [x] Run soft coverage and prepare a list of the files with lowest coverage from least coverage on up for any file with less than 95% test coverage or any file with significant functionality that isn't covered.\n- [ ] Increase test coverage incrementally for one set of related issues or 1 file below at a time\n - [ ] __init__.py\n - [ ] data.py\n - [ ] presets.py\n - [ ] harness.py\n - [ ] regimes.py\n - [ ] pipeline.py\n - [ ] validators.py\n - [ ] run_analysis.py\n - [ ] market_data.py\n - [ ] signal_presets.py\n - [x] frequency.py\n - [ ] signals.py\n - [ ] bootstrap.p\n - [ ] risk.py\n - [ ] bundle.py\n - [ ] cli.py\n - [ ] optimizer.py\n - [ ] model.py\n - [ ] engine.py\n\n### Acceptance criteria\n- [ ] Test coverage exceeds 95% for each file\n- [ ] Essential functions for the program have full test coverage","terminal_chunk_citation","terminal_chunk_id","829e4c",12,"\n* ✅ `pytest tests/test_util_frequency_internal.py --cov=trend_analysis.util.frequency --cov-report=term`","0d55cc",74,"\n\n**Testing**\n* ✅ `pytest tests/test_app_coverage.py --cov=src --cov-report=term`","repo_file_citation","\n* Added comprehensive unit tests for `trend_analysis.util.frequency` covering iterable normalization, frequency inference fallbacks, interval classification, summary metadata, and infer-frequency error handling, bringing the module to 98 % coverage.","**Summary**\n* Captured a soft coverage snapshot to identify modules currently below the 95 % target, providing a baseline for future work.","pr","pr_title","test: cover frequency helpers","pr_message","## Summary\n- add targeted unit tests covering `trend_analysis.util.frequency`\n\n## Testing\n- pytest tests/test_util_frequency_internal.py --cov=trend_analysis.util.frequency --cov-report=term","output_diff",{"_104":472,"_475":174,"_139":140,"_476":-5,"_300":477,"_478":479,"_480":329,"_481":331,"_482":43,"_483":469,"_484":-5},"pre_apply_patch","repo_id","preapply_patch_base_commit_sha","diff --git a/tests/test_util_frequency_internal.py b/tests/test_util_frequency_internal.py\nnew file mode 100644\nindex 0000000000000000000000000000000000000000..58aeb63d837ffca5a91674897ccac5e9b496f29d\n--- /dev/null\n+++ b/tests/test_util_frequency_internal.py\n@@ -0,0 +1,161 @@\n+\"\"\"Additional coverage for `trend_analysis.util.frequency` helpers.\"\"\"\n+\n+from __future__ import annotations\n+\n+import numpy as np\n+import pandas as pd\n+import pytest\n+\n+from trend_analysis.util.frequency import (\n+ FREQUENCY_LABELS,\n+ FrequencySummary,\n+ _as_datetime_index,\n+ _classify_from_diffs,\n+ _map_inferred,\n+ _summary_from_code,\n+ detect_frequency,\n+)\n+\n+\n+def test_as_datetime_index_normalises_iterables() -> None:\n+ \"\"\"Non-index iterables should be converted and sorted chronologically.\"\"\"\n+\n+ idx = _as_datetime_index(\n+ [\n+ \"2024-01-03\",\n+ \"2024-01-01\",\n+ pd.Timestamp(\"2024-01-02\"),\n+ ]\n+ )\n+\n+ assert isinstance(idx, pd.DatetimeIndex)\n+ assert list(idx) == [\n+ pd.Timestamp(\"2024-01-01\"),\n+ pd.Timestamp(\"2024-01-02\"),\n+ pd.Timestamp(\"2024-01-03\"),\n+ ]\n+\n+\n+def test_as_datetime_index_sorts_existing_index() -> None:\n+ \"\"\"DatetimeIndex inputs should be returned sorted without modification.\"\"\"\n+\n+ raw = pd.DatetimeIndex([\"2024-01-03\", \"2024-01-01\", \"2024-01-02\"])\n+ idx = _as_datetime_index(raw)\n+ assert idx.tolist() == sorted(raw.tolist())\n+\n+\n+def test_as_datetime_index_rejects_non_datetimes() -> None:\n+ \"\"\"Invalid inputs should trigger a ValueError rather than NaT leakage.\"\"\"\n+\n+ with pytest.raises(ValueError):\n+ _as_datetime_index([\"2024-01-01\", \"not-a-date\", 10])\n+\n+\n+@pytest.mark.parametrize(\n+ (\"freq\", \"expected\"),\n+ [\n+ (None, None),\n+ (\"\", None),\n+ (\"W-WED\", \"W\"),\n+ (\"bd\", \"D\"),\n+ (\"C\", \"D\"),\n+ (\"15D\", \"D\"),\n+ (\"SM-15\", \"M\"),\n+ (\"BM\", \"M\"),\n+ (\"Q-DEC\", \"Q\"),\n+ (\"A-DEC\", \"Y\"),\n+ (\"foo\", None),\n+ ],\n+)\n+def test_map_inferred_normalises_pandas_codes(freq: str | None, expected: str | None) -> None:\n+ \"\"\"The inference mapper should collapse Pandas frequency aliases to our codes.\"\"\"\n+\n+ assert _map_inferred(freq) == expected\n+\n+\n+def test_classify_from_diffs_defaults_to_monthly_when_empty() -> None:\n+ \"\"\"Empty diffs should return the default monthly cadence.\"\"\"\n+\n+ assert _classify_from_diffs(np.array([])) == \"M\"\n+\n+\n+def test_classify_from_diffs_requires_signal() -> None:\n+ \"\"\"Classifying intervals with no recognised cadence should error.\"\"\"\n+\n+ with pytest.raises(ValueError, match=\"irregular spacing\"):\n+ _classify_from_diffs(np.array([0.0]))\n+\n+\n+def test_classify_from_diffs_detects_inconsistent_cadence() -> None:\n+ \"\"\"Mixed cadences without a dominant bucket should raise an error.\"\"\"\n+\n+ with pytest.raises(ValueError, match=\"too irregular\"):\n+ _classify_from_diffs(np.array([6.0, 250.0]))\n+\n+\n+def test_summary_from_code_tracks_resampling_targets() -> None:\n+ \"\"\"The summary helper should describe both the detected cadence and target.\"\"\"\n+\n+ summary = _summary_from_code(\"D\")\n+ assert summary == FrequencySummary(\n+ code=\"D\",\n+ label=FREQUENCY_LABELS[\"D\"],\n+ resampled=True,\n+ target=\"M\",\n+ target_label=FREQUENCY_LABELS[\"M\"],\n+ )\n+\n+\n+def test_summary_from_code_for_monthly() -> None:\n+ \"\"\"Monthly series should not be flagged for resampling.\"\"\"\n+\n+ summary = _summary_from_code(\"M\")\n+ assert summary.resampled is False\n+ assert summary.target_label == FREQUENCY_LABELS[\"M\"]\n+\n+\n+def test_detect_frequency_handles_single_observation() -> None:\n+ \"\"\"Single point series default to monthly without calling infer_freq.\"\"\"\n+\n+ summary = detect_frequency([pd.Timestamp(\"2024-01-31\")])\n+ assert summary.code == \"M\"\n+ assert summary.resampled is False\n+\n+\n+def test_detect_frequency_uses_inferred_business_day(monkeypatch: pytest.MonkeyPatch) -> None:\n+ \"\"\"Business-day inference should collapse to the daily cadence.\"\"\"\n+\n+ idx = pd.date_range(\"2024-01-01\", periods=5, freq=\"B\")\n+\n+ def fake_infer_freq(_: pd.DatetimeIndex) -> str:\n+ return \"bd\"\n+\n+ monkeypatch.setattr(pd, \"infer_freq\", fake_infer_freq)\n+\n+ summary = detect_frequency(idx)\n+ assert summary.code == \"D\"\n+ assert summary.label == FREQUENCY_LABELS[\"D\"]\n+ assert summary.target_label == FREQUENCY_LABELS[\"M\"]\n+\n+\n+def test_detect_frequency_falls_back_when_infer_freq_fails(monkeypatch: pytest.MonkeyPatch) -> None:\n+ \"\"\"Errors from ``pd.infer_freq`` should trigger the diff-based classifier.\"\"\"\n+\n+ idx = pd.DatetimeIndex(\n+ [\n+ \"2024-03-31\",\n+ \"2024-03-31\", # duplicate should be ignored\n+ \"2024-06-30\",\n+ \"2024-09-30\",\n+ \"2024-12-31\",\n+ ]\n+ )\n+\n+ def raise_error(_: pd.DatetimeIndex) -> str:\n+ raise ValueError(\"no freq\")\n+\n+ monkeypatch.setattr(pd, \"infer_freq\", raise_error)\n+\n+ summary = detect_frequency(idx)\n+ assert summary.code == \"Q\"\n+ assert summary.label == FREQUENCY_LABELS[\"Q\"]\n","external_storage_diff",{"_485":486,"_487":-5},"files_modified","lines_added","lines_removed","commit_message","additional_parent_commit_shas","file_id","file_00000000d7b061f7bd0ddcbb6eb76509","ttl",{"_104":301,"_53":96,"_108":301,"_490":491,"_492":493,"_494":495,"_143":-5,"_130":496},[95],"input_items",[515,516,517,518,519,520],"external_post_config",{"_497":498,"_238":174,"_499":278,"_249":-5,"_475":232,"_500":18,"_501":-5,"_502":-5,"_503":-5,"_504":505,"_506":-5,"_507":-5,"_508":509,"_510":18,"_511":70,"_512":65,"_513":18,"_514":-5},"user_model_slug","slug",1761764279.854398,"installation_id",71148695,"pr_number","enable_internal_github_app_routing","gitlab_service_account_project_id","review_trigger","comment_id","comment_type","issue","pr_write_guard_token","pending_pr_reaction_id","pending_comment_reaction_id",311415700,"comment_if_no_findings","comment_on_error","share_url","is_parallel_review","review_summary_id",{"_104":442,"_108":301,"_332":533},{"_285":286,"_287":528,"_529":530,"_531":140,"_297":-5,"_104":532},{"_104":521,"_287":527,"_321":-5,"_523":-5,"_524":-5},{"_104":521,"_287":526,"_321":-5,"_523":-5,"_524":-5},{"_104":521,"_287":525,"_321":-5,"_523":-5,"_524":-5},{"_104":521,"_287":522,"_321":-5,"_523":-5,"_524":-5},"pull_request_comment","## Pull Request Overview\n\nThis PR introduces a bootstrap file for Codex agent functionality related to issue #3144. The file serves as an initialization marker or configuration entry point for the Codex system to handle a specific issue.\n\n### Key Changes\n- Added a new bootstrap file for Codex agent operation on issue #3144\n\n\n\n","side","line","\n**Codex activation blocked**\n\nNo `agent:*` label found on this pull request.\n\nAdd exactly one `agent:*` label (for example, `agent:codex`) and re-run the command.","\n## Automated Status Summary\n**Head SHA:** adedff7aa7e3005e89c37f27040601a225d12c84\n**Latest Runs:** ⏳ pending — Gate\n**Required contexts:** Gate / gate, Health 45 Agents Guard / Enforce agents workflow protections\n**Required:** core tests (3.11): ⏳ pending, core tests (3.12): ⏳ pending, docker smoke: ⏳ pending, gate: ⏳ pending\n\n| Workflow / Job | Result | Logs |\n|----------------|--------|------|\n| _(no jobs reported)_ | ⏳ pending | — |\n\n_Updated automatically; will refresh on subsequent CI/Docker completions._\n\n\n---\n### Keepalive checklist\n\n#### Scope\nAdd test coverage for any program functionality with test coverage under 95% or for essential program functionality that does not currently have test coverage\n\n#### Tasks\n- [ ] Run soft coverage and prepare a list of the files with lowest coverage from least coverage on up for any file with less than 95% test coverage or any file with significant functionality that isn't covered.\n- [ ] Increase test coverage incrementally for one set of related issues or 1 file below at a time\n - [ ] __init__.py\n - [ ] data.py\n - [ ] presets.py\n - [ ] harness.py\n - [ ] regimes.py\n - [ ] pipeline.py\n - [ ] validators.py\n - [ ] run_analysis.py\n - [ ] market_data.py\n - [ ] signal_presets.py\n - [ ] frequency.py\n - [ ] signals.py\n - [ ] bootstrap.p\n - [ ] risk.py\n - [ ] bundle.py\n - [ ] cli.py\n - [ ] optimizer.py\n - [ ] model.py\n - [ ] engine.py\n\n#### Acceptance criteria\n- [ ] Test coverage exceeds 95% for each file\n- [ ] Essential functions for the program have full test coverage","Gate fast-pass: docs-only change detected; heavy checks skipped.\n\n","\n## Summary\nEnsure that test coverage exceeds 95% for all program files.\n\n\n**Scope:** Add test coverage for any program functionality with test coverage under 95% or for essential program functionality that does not currently have test coverage\n\n\n\n\n## Automated Status Summary\n#### Scope\nAdd test coverage for any program functionality with test coverage under 95% or for essential program functionality that does not currently have test coverage\n\n#### Tasks\n- [ ] Run soft coverage and prepare a list of the files with lowest coverage from least coverage on up for any file with less than 95% test coverage or any file with significant functionality that isn't covered.\n- [ ] Increase test coverage incrementally for one set of related issues or 1 file below at a time\n - [ ] __init__.py\n - [ ] data.py\n - [ ] presets.py\n - [ ] harness.py\n - [ ] regimes.py\n - [ ] pipeline.py\n - [ ] validators.py\n - [ ] run_analysis.py\n - [ ] market_data.py\n - [ ] signal_presets.py\n - [ ] frequency.py\n - [ ] signals.py\n - [ ] bootstrap.p\n - [ ] risk.py\n - [ ] bundle.py\n - [ ] cli.py\n - [ ] optimizer.py\n - [ ] model.py\n - [ ] engine.py\n\n#### Acceptance criteria\n- [ ] Test coverage exceeds 95% for each file\n- [ ] Essential functions for the program have full test coverage\n\n**Head SHA:** debe269f554f288ca509955c293f21b378545db4\n**Latest Runs:** ⏳ queued — Gate\n**Required:** gate: ⏳ queued\n\n| Workflow / Job | Result | Logs |\n|----------------|--------|------|\n| Agents PR meta manager | ✅ success | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18918999591) |\n| CI Autofix Loop | ✅ success | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18919012843) |\n| Copilot code review | ⏳ queued | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18918998921) |\n| Gate | ⏳ queued | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18919012806) |\n| Health 40 Sweep | ✅ success | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18918999641) |\n| Health 44 Gate Branch Protection | ❔ in progress | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18918999625) |\n| Health 45 Agents Guard | ❌ failure | [View run](https://github.com/stranske/Trend_Model_Project/actions/runs/18919012816) |\n","base_ref","f3b0e5cb19804c9b0b45abca95dd5beec0fcf4d9","head_ref","pull_request_info",[534],{"_454":57,"_57":535},"@codex use the scope, acceptance criteria, and task list so the keepalive workflow continues nudging until everything is complete. Work through the tasks, checking them off only after each acceptance criterion is satisfied, but check during each comment implementation and check off tasks and acceptance criteria that have been satisfied and repost the current version of the initial scope, task list and acceptance criteria each time that any have been newly completed.",[566,567,568],[],{"_312":565},{"_309":564},{"_309":563},{"_277":278,"_266":279,"_280":281,"_282":70,"_283":18,"_284":18,"_285":286,"_287":288,"_289":242,"_290":138,"_291":292,"_293":294,"_295":-5,"_296":-5,"_297":298,"_299":-5,"_300":-5,"_301":562,"_303":304,"_305":-5,"_306":-5,"_307":-5},{"_53":134,"_171":172,"_173":174,"_175":18,"_176":177,"_178":179,"_180":551,"_182":-5,"_183":-5,"_184":18,"_185":552,"_187":553,"_189":554,"_191":555,"_193":556,"_195":557,"_197":-5,"_198":199,"_200":201,"_202":203,"_204":558,"_66":-5,"_130":206,"_207":-5,"_208":559,"_210":211,"_212":18,"_213":18},{"_144":544},[545,546,547],{"_155":550,"_157":168},{"_155":549,"_157":164},{"_155":548,"_157":158},{"_159":160,"_104":161,"_130":162},{"_159":165,"_104":161,"_130":166},{"_159":169,"_104":161,"_130":170},{"_269":270,"_271":80,"_272":273,"_274":-5,"_275":-5,"_276":-5},[232],[268],[177],{"_232":560},{"_216":217,"_218":219,"_220":221,"_222":223,"_224":225,"_226":227,"_228":229,"_230":231},{},[],{"_214":70,"_215":177},{"_53":234,"_159":235,"_236":561,"_238":174,"_239":240,"_241":242,"_243":244,"_245":246,"_247":248,"_249":75,"_250":-5,"_251":70,"_252":70,"_253":70,"_254":70,"_255":70,"_256":-5,"_257":258,"_259":-5,"_66":-5},{"_260":261,"_53":262,"_104":263,"_264":265,"_266":267},{"_260":261,"_159":261,"_308":-5,"_264":265,"_53":262},[],[],[],{"_104":467,"_468":469,"_470":471,"_472":585,"_474":-5},{"_104":442,"_108":105,"_332":574},{"_104":317,"_318":569},[570],{"_321":322,"_323":-5,"_324":571,"_326":-5},[572],{"_328":329,"_330":331,"_332":573,"_334":70},[335,177,336,177,337,338,339,177,340,341,342,343,344,345,346,347,348,177,177,349,350,177,351,352,353,354,355,356,357,177,358,359,360,361,362,363,177,177,364,365,177,366,367,368,177,177,369,370,177,371,372,177,177,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,348,388,389,177,390,177,177,391,392,177,393,177,177,394,395,177,396,397,177,177,398,399,177,400,401,177,177,402,403,177,404,405,406,407,408,409,410,357,177,177,411,412,177,413,414,415,177,177,416,417,177,418,419,414,177,177,420,421,177,422,177,423,424,177,425,177,426,427,428,415,177,177,429,430,177,431,352,432,433,434,435,436,356,357,177,437,438,177,439,177,426,440,441],[575,576,577,578,579,580,581,582,583,584],{"_454":57,"_57":466},{"_454":456,"_457":461,"_328":329,"_330":462},{"_454":57,"_57":465},{"_454":464,"_321":322,"_328":329,"_330":331},{"_454":456,"_457":458,"_328":329,"_330":459},{"_454":57,"_57":463},{"_454":456,"_457":461,"_328":329,"_330":462},{"_454":57,"_57":460},{"_454":456,"_457":458,"_328":329,"_330":459},{"_454":57,"_57":455},{"_104":472,"_475":174,"_139":140,"_476":-5,"_300":477,"_478":586,"_480":329,"_481":331,"_482":43,"_483":469,"_484":-5},{"_485":486,"_487":-5},[617,618,619],[],{"_312":616},{"_309":615},{"_309":614},{"_277":278,"_266":279,"_280":281,"_282":70,"_283":18,"_284":18,"_285":286,"_287":288,"_289":242,"_290":138,"_291":292,"_293":294,"_295":-5,"_296":-5,"_297":298,"_299":-5,"_300":-5,"_301":613,"_303":304,"_305":-5,"_306":-5,"_307":-5},{"_53":134,"_171":172,"_173":174,"_175":18,"_176":177,"_178":179,"_180":602,"_182":-5,"_183":-5,"_184":18,"_185":603,"_187":604,"_189":605,"_191":606,"_193":607,"_195":608,"_197":-5,"_198":199,"_200":201,"_202":203,"_204":609,"_66":-5,"_130":206,"_207":-5,"_208":610,"_210":211,"_212":18,"_213":18},{"_144":595},[596,597,598],{"_155":601,"_157":168},{"_155":600,"_157":164},{"_155":599,"_157":158},{"_159":160,"_104":161,"_130":162},{"_159":165,"_104":161,"_130":166},{"_159":169,"_104":161,"_130":170},{"_269":270,"_271":80,"_272":273,"_274":-5,"_275":-5,"_276":-5},[232],[268],[177],{"_232":611},{"_216":217,"_218":219,"_220":221,"_222":223,"_224":225,"_226":227,"_228":229,"_230":231},{},[],{"_214":70,"_215":177},{"_53":234,"_159":235,"_236":612,"_238":174,"_239":240,"_241":242,"_243":244,"_245":246,"_247":248,"_249":75,"_250":-5,"_251":70,"_252":70,"_253":70,"_254":70,"_255":70,"_256":-5,"_257":258,"_259":-5,"_66":-5},{"_260":261,"_53":262,"_104":263,"_264":265,"_266":267},{"_260":261,"_159":261,"_308":-5,"_264":265,"_53":262},[],[],[],{"_104":467,"_468":469,"_470":471,"_472":636,"_474":-5},{"_104":442,"_108":105,"_332":625},{"_104":317,"_318":620},[621],{"_321":322,"_323":-5,"_324":622,"_326":-5},[623],{"_328":329,"_330":331,"_332":624,"_334":70},[335,177,336,177,337,338,339,177,340,341,342,343,344,345,346,347,348,177,177,349,350,177,351,352,353,354,355,356,357,177,358,359,360,361,362,363,177,177,364,365,177,366,367,368,177,177,369,370,177,371,372,177,177,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,348,388,389,177,390,177,177,391,392,177,393,177,177,394,395,177,396,397,177,177,398,399,177,400,401,177,177,402,403,177,404,405,406,407,408,409,410,357,177,177,411,412,177,413,414,415,177,177,416,417,177,418,419,414,177,177,420,421,177,422,177,423,424,177,425,177,426,427,428,415,177,177,429,430,177,431,352,432,433,434,435,436,356,357,177,437,438,177,439,177,426,440,441],[626,627,628,629,630,631,632,633,634,635],{"_454":57,"_57":466},{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Mention: chore(codex): bootstrap PR for issue 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