用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/AltimateAI/altimate-code --skill teach命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
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
正在显示 SKILL.md
基于 SOC 职业分类
| name | teach |
| description | Teach your AI teammate a pattern by showing it an example file from your codebase |
Learn a reusable pattern from an example file. The user shows you a well-written artifact (model, query, config), and you extract the patterns worth following.
Identify the file: The user provides a file reference (e.g., @models/staging/stg_orders.sql). Read the file.
Analyze patterns: Extract the structural patterns, NOT the specific content. Focus on:
Present findings: Show the user what you learned in a structured list. Be specific:
Ask for confirmation: Let the user confirm, modify, or reject your findings before saving.
Save via training_save: Use the training_save tool with:
kind: "pattern"name: A descriptive slug (e.g., "staging-model", "incremental-config")content: The extracted patterns as a concise, actionable checklistscope: "project" (default — shared with team via git)source: The file path you learned fromcitations: Reference to the source file{{ source() }} macro" is a pattern. "Query the orders table" is content./teach @models/staging/stg_orders.sql
/teach staging-model @models/staging/stg_customers.sql
/teach @dbt_project.yml
If the user provides a name (first argument before the @file), use that as the pattern name. Otherwise, infer a name from the file type and purpose.