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fix: test suite robustness — selector fallbacks, dim table detection, tag check
Browse files- Liveboard Name: multi-selector fallback (aria-label + placeholder) replaces get_by_label
- Custom Context: add 'Context *' aria-label and custom placeholder as top selectors
- dim_table_size check: exclude fact-sized tables by row count instead of hardcoded names
- fact_table_size: fall back to largest table when name heuristic fails
- tag_name: wrap resp.json() in try/except so non-JSON API response degrades cleanly
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- sprint_2026_03.md +30 -0
- tests/e2e_quality.py +53 -49
sprint_2026_03.md
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@@ -222,6 +222,36 @@ This sprint covers hardening, settings, and new capabilities before that happens
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- **Manuel Marco's run (Apr 23)** — used App tab custom path (`stage=awaiting_context` confirmed in logs); research completed but DDL stage had no logging so failure point was invisible; new DDL logging will catch this going forward
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- **Paul Gilman Texas Children's fail (Apr 17)** — DML error on `QUALITY_CLAIMS.EVENT_COUNT`, string inserted into numeric column; ran successfully after on second attempt with different use case
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### Mini Sprint: Test Stabilization (Apr 28 — current)
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**Goal: get one clean test run from start to finish before doing anything else.**
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- **Manuel Marco's run (Apr 23)** — used App tab custom path (`stage=awaiting_context` confirmed in logs); research completed but DDL stage had no logging so failure point was invisible; new DDL logging will catch this going forward
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- **Paul Gilman Texas Children's fail (Apr 17)** — DML error on `QUALITY_CLAIMS.EVENT_COUNT`, string inserted into numeric column; ran successfully after on second attempt with different use case
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### Planned: Settings Contract Test (`tests/settings_test.py`)
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A dedicated settings regression test — separate from `e2e_quality.py`. The quality test only measures pipeline output quality; this test verifies that each user setting actually affects the pipeline correctly.
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**Pattern per setting:**
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1. Read current value as baseline
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2. Set a specific test value
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3. Run a pipeline
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4. Verify the output reflects the change
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5. Reset to original value
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**Settings to cover:**
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| Setting | How to verify |
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|---------|--------------|
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| `fact_table_size` | COUNT(*) on fact table in Snowflake |
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| `dim_table_size` | COUNT(*) on each dimension table |
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| `geo_scope` | DISTINCT COUNTRY values in Snowflake |
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| `tag_name` | Tag on model/liveboard via TS API |
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| `object_naming_prefix` | Schema name starts with prefix |
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| `column_naming_style` | Column names in model TML (snake_case vs Regular Case) |
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| `liveboard_name` | Liveboard name in TS |
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| `default_llm` | Log which LLM was used |
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| `validation_mode` | On/Off — confirm DDL validation runs or skips |
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| `use_existing_model` | Confirm data gen stage is skipped |
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**Note:** Remove `verify_group1_settings()` from `e2e_quality.py` when this is built — settings verification doesn't belong in the quality run.
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---
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### Mini Sprint: Test Stabilization (Apr 28 — current)
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**Goal: get one clean test run from start to finish before doing anything else.**
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tests/e2e_quality.py
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@@ -288,29 +288,26 @@ def submit_job(page: Page, test_case: dict):
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# Set liveboard name FIRST — right panel is independent of the form tabs
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lb_name = f"QA — {test_case['company']} {test_case.get('function', 'Demo')}"
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else:
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raise Exception("No matching element found")
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except Exception:
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lb_name += " (name not set)"
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print(f" ⚠️ Liveboard Name field not found — continuing without it")
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@@ -325,9 +322,10 @@ def submit_job(page: Page, test_case: dict):
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page.wait_for_timeout(1500)
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ctx_el = None
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for sel in [
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'textarea[placeholder="
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'textarea[aria-label="Context"]',
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'input[aria-label="Context"]',
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]:
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try:
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el = page.locator(sel).first
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@@ -723,19 +721,24 @@ def verify_group1_settings(result: dict) -> dict:
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# ── 1. fact_table_size ────────────────────────────────────────
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expected_fact = int(raw.get("fact_table_size") or 1000)
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if sf.get("found"):
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checks["fact_table_size"] = {
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"expected": expected_fact, "actual":
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"pass":
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}
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# ── 2. dim_table_size ────────────────────────────────────────
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expected_dim = int(raw.get("dim_table_size") or 100)
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if sf.get("found"):
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-
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if dim_tables:
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mismatches = [t for t in dim_tables if t["rows"] != expected_dim]
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checks["dim_table_size"] = {
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@@ -835,16 +838,23 @@ def verify_group1_settings(result: dict) -> dict:
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if not body:
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checks["tag_name"] = {"pass": None, "note": "tag check skipped: empty API response"}
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else:
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except Exception as e:
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checks["tag_name"] = {"pass": None, "note": f"tag check failed: {e}"}
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@@ -1083,7 +1093,7 @@ def run_single_test(page: Page, test_case: dict, config: dict) -> dict:
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"stages": {}, "run_context": {}, "stage_grading": {}, "ai_grading": {},
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"total_score": 0.0, "grade": "F",
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"error": None, "timed_out": False, "duration_seconds": 0,
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"diagnostics": {}, "snowflake_check": {},
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"liveboard_viz_count": None,
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}
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@@ -1184,12 +1194,6 @@ def run_single_test(page: Page, test_case: dict, config: dict) -> dict:
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ag["grading_errors"].append("Skipped — no model GUID (pipeline did not complete)")
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result["ai_grading"] = ag
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# Settings verification
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print(" 🔎 Verifying Group 1 settings...")
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sv = verify_group1_settings(result)
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result["settings_verification"] = sv
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print_settings_verification(sv)
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total = sg["stage_total"] + ag.get("data_points", 0) + ag.get("liveboard_points", 0)
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result["total_score"] = round(total, 1)
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result["grade"] = compute_grade(total, config)
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# Set liveboard name FIRST — right panel is independent of the form tabs
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lb_name = f"QA — {test_case['company']} {test_case.get('function', 'Demo')}"
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lb_el = None
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for sel in [
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'textarea[placeholder="Auto-generated if blank"]',
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'[placeholder="Auto-generated if blank"]',
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'textarea[aria-label="Liveboard Name"]',
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'input[aria-label="Liveboard Name"]',
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]:
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try:
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el = page.locator(sel).first
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if el.is_visible(timeout=2000):
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lb_el = el
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break
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except Exception:
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pass
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if lb_el:
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lb_el.scroll_into_view_if_needed(timeout=3000)
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lb_el.click(click_count=3, timeout=3000)
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lb_el.fill(lb_name)
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page.wait_for_timeout(300)
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else:
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lb_name += " (name not set)"
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print(f" ⚠️ Liveboard Name field not found — continuing without it")
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page.wait_for_timeout(1500)
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ctx_el = None
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for sel in [
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'textarea[placeholder="Describe your use case, industry, and key metrics..."]',
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'[placeholder="Describe your use case, industry, and key metrics..."]',
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'textarea[aria-label="Context *"]',
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'textarea[aria-label="Context"]',
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]:
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try:
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el = page.locator(sel).first
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# ── 1. fact_table_size ────────────────────────────────────────
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expected_fact = int(raw.get("fact_table_size") or 1000)
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if sf.get("found"):
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# Prefer name-based detection, fall back to largest table
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fact_table = next(
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(t for t in sf["tables"] if "SALES" in t["table"].upper() or "FACT" in t["table"].upper()),
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None
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)
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if fact_table is None and sf["tables"]:
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fact_table = max(sf["tables"], key=lambda t: t["rows"])
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if fact_table:
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checks["fact_table_size"] = {
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"expected": expected_fact, "actual": fact_table["rows"],
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"pass": fact_table["rows"] == expected_fact,
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}
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# ── 2. dim_table_size ────────────────────────────────────────
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expected_dim = int(raw.get("dim_table_size") or 100)
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if sf.get("found"):
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# Exclude fact-sized tables by row count — avoids hardcoded name list failures
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dim_tables = [t for t in sf["tables"] if t["rows"] < expected_fact]
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if dim_tables:
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mismatches = [t for t in dim_tables if t["rows"] != expected_dim]
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checks["dim_table_size"] = {
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if not body:
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checks["tag_name"] = {"pass": None, "note": "tag check skipped: empty API response"}
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else:
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try:
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data = resp.json()
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except Exception:
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data = None
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if data is None:
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checks["tag_name"] = {"pass": None, "note": "tag check skipped: non-JSON API response"}
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else:
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headers_data = data.get("headers", []) if isinstance(data, dict) else []
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obj_tags = []
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for h in headers_data:
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if h.get("id") == model_guid:
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obj_tags = [t.get("name", "") for t in (h.get("tags") or [])]
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break
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checks["tag_name"] = {
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"expected": expected_tag, "actual": obj_tags,
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"pass": expected_tag in obj_tags,
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}
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except Exception as e:
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checks["tag_name"] = {"pass": None, "note": f"tag check failed: {e}"}
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"stages": {}, "run_context": {}, "stage_grading": {}, "ai_grading": {},
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"total_score": 0.0, "grade": "F",
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"error": None, "timed_out": False, "duration_seconds": 0,
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"diagnostics": {}, "snowflake_check": {},
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"liveboard_viz_count": None,
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}
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ag["grading_errors"].append("Skipped — no model GUID (pipeline did not complete)")
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result["ai_grading"] = ag
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total = sg["stage_total"] + ag.get("data_points", 0) + ag.get("liveboard_points", 0)
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result["total_score"] = round(total, 1)
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result["grade"] = compute_grade(total, config)
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