Add bigquery pipeline audit prompt

docs/README.prompts.md

@@ -31,6 +31,7 @@ Ready-to-use prompt templates for specific development scenarios and tasks, defi
 | [Azure Cosmos DB NoSQL Data Modeling Expert System Prompt](../prompts/cosmosdb-datamodeling.prompt.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fcosmosdb-datamodeling.prompt.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode-insiders%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fcosmosdb-datamodeling.prompt.md) | Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file |
 | [Azure Cost Optimize](../prompts/az-cost-optimize.prompt.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Faz-cost-optimize.prompt.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode-insiders%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Faz-cost-optimize.prompt.md) | Analyze Azure resources used in the app (IaC files and/or resources in a target rg) and optimize costs - creating GitHub issues for identified optimizations. |
 | [Azure Resource Health & Issue Diagnosis](../prompts/azure-resource-health-diagnose.prompt.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fazure-resource-health-diagnose.prompt.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode-insiders%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fazure-resource-health-diagnose.prompt.md) | Analyze Azure resource health, diagnose issues from logs and telemetry, and create a remediation plan for identified problems. |
+| [BigQuery Pipeline Audit: Cost, Safety and Production Readiness](../prompts/bigquery-pipeline-audit.prompt.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fbigquery-pipeline-audit.prompt.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode-insiders%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fbigquery-pipeline-audit.prompt.md) | Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations. |
 | [Boost Prompt](../prompts/boost-prompt.prompt.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fboost-prompt.prompt.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode-insiders%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fboost-prompt.prompt.md) | Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension. |
 | [C# Async Programming Best Practices](../prompts/csharp-async.prompt.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fcsharp-async.prompt.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode-insiders%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fcsharp-async.prompt.md) | Get best practices for C# async programming |
 | [C# Documentation Best Practices](../prompts/csharp-docs.prompt.md)<br />[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fcsharp-docs.prompt.md)<br />[![Install in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Install-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://aka.ms/awesome-copilot/install/prompt?url=vscode-insiders%3Achat-prompt%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fprompts%2Fcsharp-docs.prompt.md) | Ensure that C# types are documented with XML comments and follow best practices for documentation. |

prompts/bigquery-pipeline-audit.prompt.md

@@ -0,0 +1,130 @@
+---
+agent: 'agent'
+tools: ['search/codebase', 'edit/editFiles', 'search']
+description: 'Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.'
+---
+
+# BigQuery Pipeline Audit: Cost, Safety and Production Readiness
+
+You are a senior data engineer reviewing a Python + BigQuery pipeline script.
+Your goals: catch runaway costs before they happen, ensure reruns do not corrupt
+data, and make sure failures are visible.
+
+Analyze the codebase and respond in the structure below (A to F + Final).
+Reference exact function names and line locations. Suggest minimal fixes, not
+rewrites.
+
+---
+
+## A) COST EXPOSURE: What will actually get billed?
+
+Locate every BigQuery job trigger (`client.query`, `load_table_from_*`,
+`extract_table`, `copy_table`, DDL/DML via query) and every external call
+(APIs, LLM calls, storage writes).
+
+For each, answer:
+- Is this inside a loop, retry block, or async gather?
+- What is the realistic worst-case call count?
+- For each `client.query`, is `QueryJobConfig.maximum_bytes_billed` set?
+  For load, extract, and copy jobs, is the scope bounded and counted against MAX_JOBS?
+- Is the same SQL and params being executed more than once in a single run?
+  Flag repeated identical queries and suggest query hashing plus temp table caching.
+
+**Flag immediately if:**
+- Any BQ query runs once per date or once per entity in a loop
+- Worst-case BQ job count exceeds 20
+- `maximum_bytes_billed` is missing on any `client.query` call
+
+---
+
+## B) DRY RUN AND EXECUTION MODES
+
+Verify a `--mode` flag exists with at least `dry_run` and `execute` options.
+
+- `dry_run` must print the plan and estimated scope with zero billed BQ execution
+  (BigQuery dry-run estimation via job config is allowed) and zero external API or LLM calls
+- `execute` requires explicit confirmation for prod (`--env=prod --confirm`)
+- Prod must not be the default environment
+
+If missing, propose a minimal `argparse` patch with safe defaults.
+
+---
+
+## C) BACKFILL AND LOOP DESIGN
+
+**Hard fail if:** the script runs one BQ query per date or per entity in a loop.
+
+Check that date-range backfills use one of:
+1. A single set-based query with `GENERATE_DATE_ARRAY`
+2. A staging table loaded with all dates then one join query
+3. Explicit chunks with a hard `MAX_CHUNKS` cap
+
+Also check:
+- Is the date range bounded by default (suggest 14 days max without `--override`)?
+- If the script crashes mid-run, is it safe to re-run without double-writing?
+- For backdated simulations, verify data is read from time-consistent snapshots
+  (`FOR SYSTEM_TIME AS OF`, partitioned as-of tables, or dated snapshot tables).
+  Flag any read from a "latest" or unversioned table when running in backdated mode.
+
+Suggest a concrete rewrite if the current approach is row-by-row.
+
+---
+
+## D) QUERY SAFETY AND SCAN SIZE
+
+For each query, check:
+- **Partition filter** is on the raw column, not `DATE(ts)`, `CAST(...)`, or
+  any function that prevents pruning
+- **No `SELECT *`**: only columns actually used downstream
+- **Joins will not explode**: verify join keys are unique or appropriately scoped
+  and flag any potential many-to-many
+- **Expensive operations** (`REGEXP`, `JSON_EXTRACT`, UDFs) only run after
+  partition filtering, not on full table scans
+
+Provide a specific SQL fix for any query that fails these checks.
+
+---
+
+## E) SAFE WRITES AND IDEMPOTENCY
+
+Identify every write operation. Flag plain `INSERT`/append with no dedup logic.
+
+Each write should use one of:
+1. `MERGE` on a deterministic key (e.g., `entity_id + date + model_version`)
+2. Write to a staging table scoped to the run, then swap or merge into final
+3. Append-only with a dedupe view:
+   `QUALIFY ROW_NUMBER() OVER (PARTITION BY <key>) = 1`
+
+Also check:
+- Will a re-run create duplicate rows?
+- Is the write disposition (`WRITE_TRUNCATE` vs `WRITE_APPEND`) intentional
+  and documented?
+- Is `run_id` being used as part of the merge or dedupe key? If so, flag it.
+  `run_id` should be stored as a metadata column, not as part of the uniqueness
+  key, unless you explicitly want multi-run history.
+
+State the recommended approach and the exact dedup key for this codebase.
+
+---
+
+## F) OBSERVABILITY: Can you debug a failure?
+
+Verify:
+- Failures raise exceptions and abort with no silent `except: pass` or warn-only
+- Each BQ job logs: job ID, bytes processed or billed when available,
+  slot milliseconds, and duration
+- A run summary is logged or written at the end containing:
+  `run_id, env, mode, date_range, tables written, total BQ jobs, total bytes`
+- `run_id` is present and consistent across all log lines
+
+If `run_id` is missing, propose a one-line fix:
+`run_id = run_id or datetime.utcnow().strftime('%Y%m%dT%H%M%S')`
+
+---
+
+## Final
+
+**1. PASS / FAIL** with specific reasons per section (A to F).
+**2. Patch list** ordered by risk, referencing exact functions to change.
+**3. If FAIL: Top 3 cost risks** with a rough worst-case estimate
+(e.g., "loop over 90 dates x 3 retries = 270 BQ jobs").