altimate-code

Analyze and optimize SQL queries for better performance

Cloudflare-style AI code review for dbt/SQL pull requests. Produces a signed APPROVE/COMMENT/REQUEST_CHANGES verdict where every blocking finding is backed by a deterministic engine call — column-lineage blast radius, query equivalence, PII classification, and A–F grade. Use to review a dbt PR or the working-tree changes before merge.

REQUIRED before writing or modifying ANY dbt model. Invoke this skill FIRST whenever a task says "create", "build", "add", "modify", "update", "fix", or "refactor" a dbt model, staging file, mart, incremental, or snapshot. Skipping this skill is the leading cause of silent-correctness bugs — models that compile and `dbt build` cleanly but produce wrong values. It contains the patterns that prevent the most common such bugs encountered in real dbt projects: • Incremental high-water marks (`>=` vs `>` ties → silent row dropout) • Snapshot strategy selection (timestamp vs check, `unique_key` choice) • `LEFT JOIN + COUNT(*)` phantom rows from unmatched parents • Type harmonization in `COALESCE` / `CASE` / `UNION` legs • Date-spine completeness (every period present, even empty ones) • Off-by-one window boundaries (`BETWEEN d - (N-1) AND d` for N-wide) • Uniqueness enforcement when schema implies a key • Window-function `LIMIT` with deterministic tiebreaker • Verifying transformation correctness with dbt unit te

REQUIRED after building or modifying ANY dbt model that has columns declared in `schema.yml` / `_models.yml`. Run `altimate-dbt schema-verify --model <name>` to diff actual columns against the spec, and treat any `mismatch` verdict as "not done." The most common reason "the build is green but the tests still fail" is that the model produces the right *data values* in the wrong *column shape* — extra columns, missing columns, wrong order, wrong types. Many dbt equality tests grade the column tuple `(name, type, position)` exactly, and the agent's prior bias is to add "helpful" extras (`p1`/`p2`/`p3` rank breakdowns, name-resolved variants, lineage metadata) or reorder columns "more logically." Both break the contract. This skill enforces the mechanical check that catches those bugs before declaring done. Use it before declaring any model task complete.

Generate dbt unit tests automatically for any model. Analyzes SQL logic (CASE/WHEN, JOINs, window functions, NULLs), creates type-correct mock inputs from manifest schema, and assembles complete YAML. Use when a user says "generate tests", "add unit tests", "test this model", or "test coverage" for dbt models.

Validate that two tables or query results are identical — or diagnose exactly how they differ. Discover schema, identify keys, profile cheaply, then diff. Use for migration validation, ETL regression, and query refactor verification.

Add schema tests, unit tests, and data quality checks to dbt models. Use when validating data integrity, adding test definitions to schema.yml, writing unit tests, or practicing test-driven development in dbt. Powered by altimate-dbt.

Analyze downstream impact of dbt model changes using column-level lineage and the dependency graph. Use when evaluating the blast radius of a change before shipping. Powered by altimate-dbt.

Document dbt models and columns in schema.yml with business context — model descriptions, column definitions, and doc blocks. Use when adding or improving documentation for discoverability. Powered by altimate-dbt.

Debug dbt errors — compilation failures, runtime database errors, test failures, wrong data, and performance issues. Use when something is broken, producing wrong results, or failing to build. Powered by altimate-dbt.

Build modern, interactive data visualizations and dashboards using code-based component libraries (shadcn/ui, Recharts, Tremor, Nivo, D3, Victory, visx). Use this skill whenever the user asks to visualize data, build dashboards, create analytics views, chart metrics, tell a data story, build a reporting interface, create KPI cards, plot graphs, or explore a dataset — even if they mention PowerBI, Tableau, Streamlit, Metabase, Looker, Grafana, or similar tools. Also trigger when the user says "make a dashboard", "show me the data", "chart this", "visualize trends", "build an analytics page", "data story", or anything involving turning raw data into interactive visual interfaces. If the task involves presenting data visually — this is the skill. Always prefer building a real, interactive, code-based UI over exporting to or recommending a BI platform.

Translate SQL queries between database dialects (Snowflake, BigQuery, PostgreSQL, MySQL, etc.)

Configure Altimate platform credentials for datamate and API access

Analyze Snowflake query costs and identify optimization opportunities

Classify schema columns for PII (SSN, email, phone, name, address, credit card) and check whether queries expose them. Use for GDPR/CCPA/HIPAA compliance audits.

Analyze DDL migrations for data loss risks — type narrowing, missing defaults, dropped constraints, breaking column changes. Use before applying schema changes to production.

Pre-merge SQL quality gate — lint 26 anti-patterns, grade readability/performance A-F, validate syntax, and scan for injection threats. Use before committing or reviewing SQL changes.

Teach your AI teammate a pattern by showing it an example file from your codebase

Train your AI teammate on team standards from a document or style guide

Show what your AI teammate has learned — patterns, rules, glossary, and standards

Compare column-level lineage between two versions of a SQL query to show added, removed, and changed data flow edges.