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Train your AI teammate on team standards from a document or style guide
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | train |
| description | Train your AI teammate on team standards from a document or style guide |
Learn team standards and conventions from a document (style guide, review checklist, coding standards, etc.). Extracts actionable rules and saves them as training.
Get the document: The user provides either:
@docs/sql-style-guide.mdRead and analyze: Parse the document and extract:
Categorize: Group findings by training kind:
rule — Specific do/don't rules (e.g., "Never use SELECT *")standard — Broader conventions (e.g., "SQL style guide compliance")glossary — Term definitions (e.g., "ARR = Annual Recurring Revenue")Present summary: Show the user what you extracted:
Save via training_save: Save each item using the training_save tool. For documents with many rules, consolidate related rules into logical groups (e.g., "sql-naming-rules" with 5 rules, rather than 5 separate entries).
scope: project unless the user specifies global./train @docs/sql-style-guide.md
/train https://wiki.company.com/data-team/review-checklist
/train (then paste content inline)
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.