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chore: remove cost estimation and complexity scoring bindings (#31)
* chore: remove `guard_complexity_score` and `guard_estimate_cost` - Remove both functions from guard.py (bindings removed in altimate-core-internal) - Remove complexity_score and estimate_cost endpoints from server.py - Replace `guard_complexity_score` call in feedback_store.py with length heuristic - Remove associated tests from test_guard_new.py Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: remove static cost heuristic from feedback store Remove `_static_heuristic` method and dialect-specific cost profiles that depended on the now-removed `guard_complexity_score`. Tier 4 now returns a `no_data` response instead of fabricating estimates. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: remove complexity/cost dispatch tests, fix tier3 assertion - Remove `TestSqlGuardComplexityDispatch` (2 tests for removed endpoint) - Remove `TestSqlGuardCostDispatch` (2 tests for removed endpoint) - Fix tier3 test to handle no_data fallback when table extraction fails Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: remove `FeedbackStore` and cost prediction system Remove the entire `sql.predict_cost` / `sql.record_feedback` system: - Delete `feedback_store.py` and its tests - Remove `SqlPredictCostTool` and `SqlRecordFeedbackTool` TypeScript tools - Remove protocol types, agent permissions, and bridge methods - Clean up all prompt files, docs, and progress references The static cost heuristic depended on the now-removed Rust `complexity_score` binding. The observation-based tiers (1-3) had no production usage. Removing entirely. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: remove dead `sqlguard_cost`, `sqlguard_complexity`, and `ci_cost_gate` tools These tools called bridge methods whose server-side handlers were already removed (they depended on the Rust `estimate_cost` and `complexity_score` bindings). - Delete 3 TypeScript tools and their registry entries - Delete `ci/cost_gate.py` module and tests - Remove protocol types and Pydantic models - Clean up docs references Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: rename `sqlguard` to `altimate-core` across entire codebase - Rename 33 TypeScript tool files: `sqlguard-*.ts` → `altimate-core-*.ts` - Update class names: `SqlGuard*Tool` → `AltimateCore*Tool` - Update tool IDs: `sqlguard_*` → `altimate_core_*` - Update bridge methods: `"sqlguard.*"` → `"altimate_core.*"` - Update protocol interfaces: `SqlGuard*Params` → `AltimateCore*Params` - Update agent permissions in all 4 agent configs - Update Python constant: `SQLGUARD_AVAILABLE` → `ALTIMATE_CORE_AVAILABLE` - Update Python models and server dispatch method names - Update all test files (test_guard, test_guard_new, test_server_guard, test_server_guard_new) - Update prompt files, docs, and dbt lineage module comments 51 files changed, 654 substitutions (zero remaining `sqlguard` references) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: update cost-report skill to use finops tools instead of removed predict/feedback tools Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: anandgupta42 <anand@altimate.ai>
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‎.altimate-code/skills/cost-report/SKILL.md‎

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@@ -7,7 +7,7 @@ description: Analyze Snowflake query costs and identify optimization opportuniti
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## Requirements
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**Agent:** any (read-only analysis)
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**Tools used:** sql_execute, sql_analyze, sql_predict_cost, sql_record_feedback
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**Tools used:** sql_execute, sql_analyze, finops_analyze_credits, finops_expensive_queries, finops_warehouse_advice
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Analyze Snowflake warehouse query costs, identify the most expensive queries, detect anti-patterns, and recommend optimizations.
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3. **Analyze the top offenders** - For each of the top 10 most expensive queries:
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- Run `sql_analyze` on the query text to detect anti-patterns (SELECT *, missing LIMIT, cartesian products, correlated subqueries, etc.)
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- Run `sql_predict_cost` to get the cost tier prediction based on historical feedback data
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- Summarize anti-patterns found and their severity
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4. **Classify each query into a cost tier**:
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| 3 | $1.00 - $100.00 | Expensive | Optimize or review warehouse sizing |
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| 4 | > $100.00 | Dangerous | Immediate review required |
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5. **Record feedback** - For each query analyzed, call `sql_record_feedback` to store the execution metrics so future predictions improve:
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- Pass `bytes_scanned`, `execution_time_ms`, `credits_used`, and `warehouse_size` from the query history results
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5. **Warehouse analysis** - Run `finops_warehouse_advice` to check if warehouses used by the top offenders are right-sized.
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6. **Output the final report** as a structured markdown document:
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2. Add LIMIT clause
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3. Consider partitioning strategy
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**Cost prediction:** Tier 1 (fingerprint match, high confidence)
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**Cost tier:** Tier 1 (based on credits used)
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...
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- `/cost-report` -- Analyze the last 30 days
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- `/cost-report 7` -- Analyze the last 7 days (adjust the DATEADD interval)
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Use the tools: `sql_execute`, `sql_analyze`, `sql_predict_cost`, `sql_record_feedback`.
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Use the tools: `sql_execute`, `sql_analyze`, `finops_analyze_credits`, `finops_expensive_queries`, `finops_warehouse_advice`.

‎PROGRESS.md‎

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2020
- [x] Phase 1 TODO: ConfidenceTracker with 7 AST detection rules — all 7 working
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- [x] Phase 1 TODO: Confidence signals on lineage.check — 4 signal types
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- [x] Phase 2A: Snowflake connector — password + key-pair auth, registered in ConnectionRegistry
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- [x] Phase 2B: Feedback store + cost report skill — SQLite-based, 4-tier prediction, bridge methods working
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- [x] Phase 2B: Cost report skill
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- [x] Phase 2C: dbt manifest parser — columns, sources, test/snapshot/seed counting
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- [x] Phase 3A: Impact analysis skill — downstream dependency graph + column-level impact classification
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- [x] Phase 3C: SQL translation skill — sqlglot transpile, 10 dialect-pair warnings, TS tool + skill
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| 0 | Rules with known accuracy | **19/19** | 19/19 |
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| 0 | Analyzer overall accuracy | **100.00%** | measured |
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| 0 | Lineage edge match rate | **100.0%** | measured |
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| 1-7 | Working bridge methods | **34/34** | 34/34 |
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| 1-7 | Working bridge methods | **32/32** | 32/32 |
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| 1 | ConfidenceTracker rules | **7/7** | 7/7 |
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| 2 | Snowflake connector | **imports OK** | live test |
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| 2 | Feedback store observations | **working** | >0 |
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| 3 | Skills functional end-to-end | **6** | 5+ |
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| 4 | Rules with published benchmarks | **19/19** | 19/19 |
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| 5 | Schema cache tests | **20/20** | 20/20 |
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- `packages/altimate-engine/src/altimate_engine/sql/analyzer.py` — 19 rules + ConfidenceTracker
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- `packages/altimate-engine/src/altimate_engine/sql/confidence.py` — 7 AST detection rules
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- `packages/altimate-engine/src/altimate_engine/lineage/check.py` — lineage + 4 confidence signals + Func/Window/Case edges
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- `packages/altimate-engine/src/altimate_engine/server.py` — JSON-RPC dispatch (34 methods)
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- `packages/altimate-engine/src/altimate_engine/server.py` — JSON-RPC dispatch (32 methods)
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- `packages/altimate-engine/src/altimate_engine/models.py` — All Pydantic models
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**Phase 2 (connectors + parsers + feedback):**
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- `packages/altimate-engine/src/altimate_engine/connectors/snowflake.py` — password + key-pair auth
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- `packages/altimate-engine/src/altimate_engine/connections.py` — ConnectionRegistry with Snowflake
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- `packages/altimate-engine/src/altimate_engine/dbt/manifest.py` — Enhanced manifest parser
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- `packages/altimate-engine/src/altimate_engine/sql/feedback_store.py` — SQLite feedback + 4-tier prediction
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**Phase 3 (skills + tools):**
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- `packages/altimate-engine/src/altimate_engine/sql/translator.py` — sqlglot transpile with lossy warnings
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- `experiments/BENCHMARKS.md` — Published benchmark report
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- `experiments/benchmark_report.json` — Machine-readable benchmark data
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### Bridge Methods (34 total)
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### Bridge Methods (32 total)
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1. `ping` — Health check
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2. `sql.validate` — SQL syntax validation
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3. `sql.check` — Read-only/mutation safety check
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4. `sql.execute` — SQL execution (PG/DuckDB)
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5. `sql.analyze` — 19 anti-pattern checks with confidence
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6. `sql.translate` — Cross-dialect SQL translation
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7. `sql.optimize` — Query optimization with suggestions
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8. `sql.record_feedback` — Record query execution metrics
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9. `sql.predict_cost` — Predict query cost (4-tier hierarchy)
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10. `schema.inspect` — Table schema inspection
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11. `lineage.check` — Column-level lineage with confidence
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12. `dbt.run` — dbt CLI execution
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13. `dbt.manifest` — Manifest parsing
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14. `warehouse.list` — List configured warehouses
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15. `warehouse.test` — Test warehouse connection
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16. `schema.index` — Index warehouse metadata into SQLite cache
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17. `schema.search` — Search indexed metadata (tables/columns) with natural language
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18. `schema.cache_status` — Show cache status (warehouses indexed, counts, timestamps)
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19. `sql.explain` — Run EXPLAIN on a query (Snowflake/PG/DuckDB dialect-specific syntax)
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20. `sql.format` — Format/beautify SQL via sqlglot pretty-print
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21. `sql.fix` — Diagnose SQL errors and suggest fixes (syntax, patterns, resolution)
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22. `sql.autocomplete` — Schema-aware auto-complete suggestions from cache
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23. `sql.diff` — Compare two SQL queries (unified diff, similarity score)
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24. `finops.query_history` — Query execution history (Snowflake QUERY_HISTORY, PG pg_stat_statements)
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25. `finops.analyze_credits` — Credit consumption analysis with recommendations
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26. `finops.expensive_queries` — Identify most expensive queries by bytes scanned
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27. `finops.warehouse_advice` — Warehouse sizing recommendations (scale up/down/burst)
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28. `finops.unused_resources` — Find stale tables and idle warehouses
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29. `finops.role_grants` — Query RBAC grants on objects/roles
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30. `finops.role_hierarchy` — Map role inheritance hierarchy
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31. `finops.user_roles` — List user-to-role assignments
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32. `schema.detect_pii` — Scan columns for PII patterns (30+ regex, 15 categories)
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33. `schema.tags` — Query metadata/governance tags on objects (Snowflake TAG_REFERENCES)
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34. `schema.tags_list` — List all available tags with usage counts
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8. `schema.inspect` — Table schema inspection
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9. `lineage.check` — Column-level lineage with confidence
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10. `dbt.run` — dbt CLI execution
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11. `dbt.manifest` — Manifest parsing
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12. `warehouse.list` — List configured warehouses
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13. `warehouse.test` — Test warehouse connection
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14. `schema.index` — Index warehouse metadata into SQLite cache
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15. `schema.search` — Search indexed metadata (tables/columns) with natural language
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16. `schema.cache_status` — Show cache status (warehouses indexed, counts, timestamps)
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17. `sql.explain` — Run EXPLAIN on a query (Snowflake/PG/DuckDB dialect-specific syntax)
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18. `sql.format` — Format/beautify SQL via sqlglot pretty-print
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19. `sql.fix` — Diagnose SQL errors and suggest fixes (syntax, patterns, resolution)
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20. `sql.autocomplete` — Schema-aware auto-complete suggestions from cache
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21. `sql.diff` — Compare two SQL queries (unified diff, similarity score)
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22. `finops.query_history` — Query execution history (Snowflake QUERY_HISTORY, PG pg_stat_statements)
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23. `finops.analyze_credits` — Credit consumption analysis with recommendations
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24. `finops.expensive_queries` — Identify most expensive queries by bytes scanned
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25. `finops.warehouse_advice` — Warehouse sizing recommendations (scale up/down/burst)
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26. `finops.unused_resources` — Find stale tables and idle warehouses
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27. `finops.role_grants` — Query RBAC grants on objects/roles
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28. `finops.role_hierarchy` — Map role inheritance hierarchy
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29. `finops.user_roles` — List user-to-role assignments
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30. `schema.detect_pii` — Scan columns for PII patterns (30+ regex, 15 categories)
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31. `schema.tags` — Query metadata/governance tags on objects (Snowflake TAG_REFERENCES)
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32. `schema.tags_list` — List all available tags with usage counts
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### Skills (11 total)
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1. `generate-tests` — Generate dbt test definitions

‎README.md‎

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### Warehouse Connectivity
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Connect directly to your data warehouse -- the AI can query schemas, run SQL, predict costs, and analyze query history:
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Connect directly to your data warehouse -- the AI can query schemas, run SQL, and analyze query history:
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- Snowflake (with IAM auth)
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- BigQuery (service account + ADC)

‎docs/docs/data-engineering/agent-modes.md‎

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Builder mode follows a strict pre-execution protocol for every SQL operation:
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1. `sql_analyze` — Check for anti-patterns
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2. `sql_predict_cost` — Estimate execution cost
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3. `sql_validate` — Verify syntax and schema references
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4. `sql_execute` — Run the query
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5. `sql_record_feedback` — Store metrics for future cost predictions
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2. `sql_validate` — Verify syntax and schema references
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3. `sql_execute` — Run the query
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### Example: Create a staging model
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- **SELECT only** — INSERT, UPDATE, DELETE, DROP are blocked by SQLGuard
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- **SELECT only** — INSERT, UPDATE, DELETE, DROP are blocked by altimate-core
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- **Cost-first** — Every query gets a cost prediction before execution
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- **LIMIT enforced** — Large scans are automatically limited
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- **No file writes** — Cannot modify your codebase
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Severity: medium (type change may affect downstream)
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> sql_predict_cost [fct_revenue.sql]
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Tier 2 match: ~12.3 credits per run (medium confidence)
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Previous version: ~11.8 credits — marginal increase
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Issues found:
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2. Type change from FLOAT to DECIMAL(18,2) — verify downstream consumers

‎docs/docs/data-engineering/guides/cost-optimization.md‎

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- Warehouses with no queries in 7+ days
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## Step 5: Predict before you execute
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Every query goes through cost prediction before execution:
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```
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You: How much will this query cost?
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> sql_predict_cost "SELECT * FROM raw_clickstream"
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Tier 3 estimate: ~45 credits
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Table size: 890GB, 12B rows
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```
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## Step 6: Build a cost feedback loop
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After each query, `sql_record_feedback` stores actual execution metrics. This trains the cost prediction model to be more accurate over time.
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Query executed: 0.84 credits (predicted: 0.79, Tier 2)
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Feedback recorded → next prediction will be more accurate
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```
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## Automation: CI cost gate
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Use `ci_cost_gate` in your CI/CD pipeline to block expensive queries from reaching production:
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> ci_cost_gate --threshold 10 models/marts/fct_revenue.sql
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Cost Gate Results:
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fct_revenue.sql: 2.3 credits (PASS — under 10 credit threshold)
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fct_orders.sql: 0.8 credits (PASS)
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fct_daily_snapshot.sql: 45.1 credits (FAIL — exceeds threshold)
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## Quick wins checklist
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| Action | Typical savings | Effort |

‎docs/docs/data-engineering/guides/using-with-claude-code.md‎

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sql_analyze: No anti-patterns detected ✓
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sql_predict_cost: ~2.3 credits per run (Tier 2, medium confidence)
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lineage_check: Sources stg_orders.amount, dim_products.category
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schema_diff: Added column revenue_category (low severity)
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```

‎docs/docs/data-engineering/tools/sql-tools.md‎

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## sql_predict_cost
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Estimate query cost before execution using a 4-tier prediction system.
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> sql_predict_cost "SELECT * FROM events WHERE event_date > '2026-01-01'"
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Cost Prediction:
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Estimated bytes scanned: 4.2 GB
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### How it works
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| Tier | Method | Confidence |
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| **1 — Fingerprint** | Exact query found in history | High |
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| **2 — Template** | Similar query structure matched | Medium |
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| **3 — Table scan** | Estimate from table metadata | Low |
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| **4 — Heuristic** | No data available, worst-case estimate | Very low |
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‎packages/altimate-code/src/agent/agent.ts‎

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altimate_core_lint: "allow",
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sqlguard_transpile: "allow",
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sqlguard_check: "allow",
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altimate_core_validate: "allow",
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altimate_core_lint: "allow",
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altimate_core_safety: "allow",
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altimate_core_validate: "allow",
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altimate_core_lint: "allow",
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altimate_core_transpile: "allow",
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altimate_core_check: "allow",
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edit: "allow",

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