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CoCo Foundations","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003ESnowflake CoCo (CoCo) is Snowflake's AI coding agent built to accelerate data and AI workflows directly inside your Snowflake environment. It understands your data catalog, executes SQL as your identity, and integrates natively with dbt, Git, Python, and Streamlit.\u003C/p\u003E\n","\u003Cp\u003EThis guide walks you through installing the CoCo CLI, configuring your Snowflake connection, and completing a hands-on workshop covering three real-world scenarios: building a Dynamic Table pipeline, maintaining and evolving it, and creating a Cortex Agent with a semantic view.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with Snowflake CoCo enabled\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to access Snowflake CoCo via Snowsight (UI) and CLI\u003C/li\u003E\u003Cli\u003EHow to install and configure the CoCo CLI\u003C/li\u003E\u003Cli\u003EHow to authenticate your Snowflake connection\u003C/li\u003E\u003Cli\u003EHow to build a Dynamic Table pipeline with CoCo\u003C/li\u003E\u003Cli\u003EHow to maintain a pipeline\u003C/li\u003E\u003Cli\u003EHow to create a Cortex Agent with a semantic view and evaluate its performance\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA configured Snowflake CoCo CLI connected to your Snowflake environment\u003C/li\u003E\u003Cli\u003EA multi-source AP invoice pipeline using Dynamic Tables\u003C/li\u003E\u003Cli\u003EA Cortex Agent grounded on a semantic view, with an evaluation framework\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAccess Snowflake CoCo in Snowsight\u003C/h2\u003E\n","\u003Cp\u003EBefore setting up the CLI, you can access Snowflake CoCo directly from the Snowflake UI.\u003C/p\u003E\n","\u003Cp\u003EIn Snowsight, click the \u003Cstrong\u003Eblue star icon\u003C/strong\u003E in the bottom right corner to open the Snowflake CoCo panel.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/snowsight_home_coco_button.png?v=bff6daba\" alt=\"Snowsight Home with CoCo Button\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis gives you immediate access to CoCo without any local installation and is useful for quick queries, schema exploration, and testing.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInstall the CoCo CLI on Mac\u003C/h2\u003E\n","\u003Ch3\u003ESupported Architectures\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Ex64\u003C/strong\u003E — Fully supported\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EARM64\u003C/strong\u003E — Fully supported\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EInstall via Terminal\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen prompted to add \u003Ccode\u003Ecortex\u003C/code\u003E to your PATH, respond with \u003Cstrong\u003Ey\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf the \u003Ccode\u003Ecortex\u003C/code\u003E command is not recognized after installation, close and reopen your terminal, or restart your machine.\u003C/p\u003E\n","\u003Ch3\u003EVerify Installation\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure Your Snowflake Connection\u003C/h3\u003E\n","\u003Cp\u003ESnowflake CoCo uses the same connection files as SnowCLI, located at:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E~/.snowflake/config.toml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E(or \u003Ccode\u003Econnections.toml\u003C/code\u003E if you already use that)\u003C/p\u003E\n","\u003Ch3\u003ECreate/open the Config File\u003C/h3\u003E\n","\u003Cp\u003ERun the following in your terminal to create and open the config file:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Emkdir -p ~/.snowflake\ntouch ~/.snowflake/config.toml\nchmod 600 ~/.snowflake/config.toml\nopen -e ~/.snowflake/config.toml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy and adapt the following to your accounts and roles:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003Edefault_connection_name = "DEMO"\n\n[connections.DEMO]\naccount   = "<YOUR_DEMO_ACCOUNT>"    # e.g. SFSENORTHAMERICA-XXXXX\nuser      = "<YOUR_DEMO_USERNAME>"\npassword  = "<YOUR_DEMO_PAT>"\nrole      = "<YOUR_DEMO_ROLE>"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/snowsight_login_settings.png?v=bff6daba\" alt=\"Log In and Find Settings\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/account_details_connection.png?v=bff6daba\" alt=\"Account Details Connection\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce updated, make sure to save the file.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInstall the CoCo CLI on Windows\u003C/h2\u003E\n","\u003Ch3\u003ESupported Architectures\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Ex64\u003C/strong\u003E — Fully supported\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EARM64\u003C/strong\u003E — Support is in progress; not yet recommended for production use\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EInstall via PowerShell\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-powershell\"\u003Eirm https://ai.snowflake.com/static/cc-scripts/install.ps1 | iex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen prompted to add \u003Ccode\u003Ecortex\u003C/code\u003E to your PATH, respond with \u003Cstrong\u003Ey\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf the \u003Ccode\u003Ecortex\u003C/code\u003E command is not recognized after installation, close and reopen your terminal or restart your machine.\u003C/p\u003E\n","\u003Ch3\u003EVerify Installation\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-powershell\"\u003Ecortex --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure Your Snowflake Connection\u003C/h3\u003E\n","\u003Cp\u003EOn Windows, the config file is located at:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E%USERPROFILE%\\.snowflake\\config.toml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate/open the Config File\u003C/h3\u003E\n","\u003Cp\u003ERun the following in PowerShell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-powershell\"\u003Emkdir $env:USERPROFILE\\.snowflake -Force\nNew-Item -ItemType File -Path $env:USERPROFILE\\.snowflake\\config.toml -Force\nnotepad $env:USERPROFILE\\.snowflake\\config.toml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy and adapt the following to your accounts and roles:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003Edefault_connection_name = "DEMO"    # Name for default Snowflake connection\n\n[connections.DEMO]                  # Name for Snowflake connection (same as above if default)\naccount   = "<YOUR_DEMO_ACCOUNT>"  # e.g. Account identifier\nuser      = "<YOUR_DEMO_USERNAME>" # e.g. Authentication variable\npassword  = "<YOUR_DEMO_PAT>"\nrole      = "<YOUR_DEMO_ROLE>"     # e.g. Need a role for connection\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/snowsight_login_settings.png?v=bff6daba\" alt=\"Log In and Find Settings\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/account_details_connection.png?v=bff6daba\" alt=\"Account Details Connection\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce updated, make sure to save the file.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFind Your Connection Details\u003C/h2\u003E\n","\u003Cp\u003ETo fill out your config, you'll need your Snowflake account identifier, role, and warehouse.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog in to Snowsight\u003C/li\u003E\u003Cli\u003EClick your username in the bottom left → \u003Cstrong\u003EAccount\u003C/strong\u003E → \u003Cstrong\u003EView account details\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ENote your \u003Cstrong\u003EAccount Identifier\u003C/strong\u003E (e.g. \u003Ccode\u003ESFSENORTHAMERICA-XXXXX\u003C/code\u003E) and \u003Cstrong\u003ERole\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EAuthentication Options\u003C/h3\u003E\n","\u003Cp\u003EThere are several ways to authenticate your connection. Common environment variables include:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EVariable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE_ACCOUNT\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour account identifier\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE_USER\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EYour username\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE_PASSWORD\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPassword authentication\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE_TOKEN\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOAuth token\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE_TOKEN_FILE_PATH\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPath to token file\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESNOWFLAKE_OAUTH_CLIENT_ID\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOAuth client ID\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EFor a full list, see the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-connect\"\u003EManage Snowflake connections guide\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELaunch Snowflake CoCo\u003C/h2\u003E\n","\u003Cp\u003EOnce your connection is configured, launch the CLI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex -c DEMO\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter connecting, you'll see the Snowflake CoCo CLI interface. Test your connection by asking a simple question — for example:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat databases do I have access to?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/cortex_code_cli_interface.png?v=bff6daba\" alt=\"Snowflake CoCo CLI Interface\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELab Setup\u003C/h2\u003E\n","\u003Cp\u003EBefore starting the demos, run the setup scripts to create the workshop environment and load sample data. These files are in the \u003Ccode\u003Eassets/\u003C/code\u003E folder of \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/cortex-code-foundations/assets\"\u003Ethis repo\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Create the Workshop Environment\u003C/h3\u003E\n","\u003Cp\u003ERun \u003Ccode\u003E00_snowday_setup.sql\u003C/code\u003E as \u003Ccode\u003ESYSADMIN\u003C/code\u003E. This creates:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ECOCO_WORKSHOP\u003C/code\u003E database\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EPIPELINE_LAB\u003C/code\u003E and \u003Ccode\u003ESOURCE_DATA\u003C/code\u003E schemas\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ECOCO_WORKSHOP_WH\u003C/code\u003E warehouse (X-Small, auto-suspend 120s)\u003C/li\u003E\u003Cli\u003ETags for optional governance exercises\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2: Load Sample Data\u003C/h3\u003E\n","\u003Cp\u003ERun \u003Ccode\u003E00_sample_data.sql\u003C/code\u003E. This creates and populates:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EBRONZE_SAP_AP_INVOICES\u003C/code\u003E — 15 SAP invoices (USD, EUR, GBP)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EBRONZE_ORACLE_AP_INVOICES\u003C/code\u003E — 15 Oracle invoices (USD, EUR, GBP)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EBRONZE_BAAN_AP_INVOICES\u003C/code\u003E — 10 Baan invoices (EUR, GBP) — used in Demo 2\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EBRONZE_WORKDAY_AP_INVOICES\u003C/code\u003E — 10 Workday invoices (USD, GBP, EUR) — used in Demo 2\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EAGENT_EVAL_SET\u003C/code\u003E — 15 golden evaluation questions — used in Demo 3\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResetting Between Runs\u003C/h3\u003E\n","\u003Cp\u003EIf you need to start fresh, run \u003Ccode\u003E01_demo_reset.sql\u003C/code\u003E to drop all demo-created objects, then re-run the setup scripts above.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERunning this in a customer account with Snowflake CoCo enabled?\u003C/strong\u003E You will need the \u003Ccode\u003ESNOWFLAKE.CORTEX_USER\u003C/code\u003E database role on your user (directly or via a parent role) so Snowflake CoCo CLI can use Snowflake AI features. Use the following files in the \u003Ccode\u003E/assets\u003C/code\u003E subfolder in the repo: \u003Ccode\u003E00_admin_lab_setup.sql\u003C/code\u003E, \u003Ccode\u003E01_admin_lab_reset.sql\u003C/code\u003E, \u003Ccode\u003E02_admin_lab_teardown.sql\u003C/code\u003E, \u003Ccode\u003E03_participant_connection_template.toml\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWorkshop Overview\u003C/h2\u003E\n","\u003Cp\u003EThis workshop follows a single AP invoices storyline across three demos — from data discovery through operationalization and optional agent design.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDemo 1: Pipeline Builder\u003C/h2\u003E\n","\u003Cp\u003EFinance needs a trusted AP invoices layer. SAP and Oracle invoice data already exist in Snowflake, but there is no standardized Silver object yet. The goal is a single, well-modeled table that can be explained, monitored, and evolved without repeating discovery work each time a requirement changes.\u003C/p\u003E\n","\u003Ch3\u003EStep 1.1 – Discover the Source\u003C/h3\u003E\n","\u003Cp\u003EBegin with data discovery, a natural first move when you enter a new schema.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat tables are in the `COCO_WORKSHOP.SOURCE_DATA` schema?\nFor each table, give me a one-line description of what it appears to contain and identify\nwhich ones are most relevant to an AP invoices Silver layer.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A short list of tables relevant to the AP workflow, with enough description to orient yourself without manually opening each object.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1.2 – Compare SAP and Oracle Invoice Schemas\u003C/h3\u003E\n","\u003Cp\u003EOnce you know which tables matter, compare them to get the shortest path to a clean normalization plan.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECompare the columns between\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_SAP_AP_INVOICES and\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_ORACLE_AP_INVOICES.\nReturn:\n- Equivalent fields with different names\n- Fields that require type normalization or default values\n- Differences that should remain open questions instead of becoming hidden assumptions\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E Equivalent business fields with different names, normalization work you should expect before building Silver, and open questions that should stay explicit for later review.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1.3 – Generate the Silver Dynamic Table\u003C/h3\u003E\n","\u003Cp\u003EFor this step, turn on \u003Cstrong\u003EPlan Mode\u003C/strong\u003E (\u003Ccode\u003ECtrl+P\u003C/code\u003E) to see how Snowflake CoCo thinks through a multi-step task before executing.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPlan Mode\u003C/strong\u003E stays read-only while it thinks, then returns a structured multi-step plan and waits for your approval before executing. This is useful for higher-risk tasks like creating core tables.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECtrl+P\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen run the following prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUse database COCO_WORKSHOP and schema PIPELINE_LAB for outputs.\nCreate a Dynamic Table called SILVER_AP_INVOICES in my current schema by combining\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_SAP_AP_INVOICES and\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_ORACLE_AP_INVOICES.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A clean first-pass Dynamic Table definition with explainable design choices.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWhen finished, exit Plan Mode — it should turn off automatically when you approve execution.\u003C/p\u003E\n","\u003Ch3\u003EStep 1.4 – Use the Bundled $dynamic-tables Skill\u003C/h3\u003E\n","\u003Cp\u003ESkills are reusable workflows that tell Snowflake CoCo how to handle a specific Snowflake task. Instead of responding in a completely open-ended way, a skill provides domain context, expected inputs, a defined process, and structured outputs.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBundled skills\u003C/strong\u003E ship with Snowflake CoCo — prebuilt, Snowflake-native workflows designed to start from proven patterns.\u003C/p\u003E\n","\u003Cp\u003EFirst, see what skills are available:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E/skill list\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen inspect the Dynamic Tables skill before applying it:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EWhat does the $dynamic-tables skill do? Summarize when I should use it,\nwhat inputs it expects, and what kinds of output it returns.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen apply it to \u003Ccode\u003ESILVER_AP_INVOICES\u003C/code\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E$dynamic-tables\nAnalyze the Dynamic Table SILVER_AP_INVOICES in my current schema.\nReturn:\n1. The recommended TARGET_LAG choice for this workflow and why\n2. SQL to inspect current state, lag, and refresh history\n3. The main failure or staleness patterns to watch for\n4. A short best-practices checklist for operating this table well\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A runbook you would actually keep — a couple of monitoring queries and a concise operating checklist, not generic advice.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1.5 – Save a Proof Query \u003Cem\u003E(optional)\u003C/em\u003E\u003C/h3\u003E\n","\u003Cp\u003EEnd Demo 1 with a lightweight proof query you can rerun after every change.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EGive me one concise proof query for SILVER_AP_INVOICES that shows record counts\nby SOURCE_SYSTEM and is easy to rerun after future changes.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A concise query that can be reused after every change.\nSave this output: \u003Ccode\u003Esql/02_silver_ap_invoices_proof.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EBy the end of Demo 1 you have a Silver-grade AP invoices Dynamic Table, an operating runbook generated by a bundled skill, and a proof query you can rerun after each change.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDemo 2: Add Local Context and Productionize\u003C/h2\u003E\n","\u003Cp\u003EA month later, the business sends a PRD that expands the AP invoices pipeline. New source systems must be added, field requirements have changed, and some definitions need clarification. Rather than solving this once with a long prompt every time, you can standardize the workflow as a custom skill.\u003C/p\u003E\n","\u003Ch3\u003EAbout the Sample PRD\u003C/h3\u003E\n","\u003Cp\u003EDownload the three CSV files prepared by the Finance Transformation PMO in the \u003Ccode\u003Eassets/\u003C/code\u003E folder of \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/cortex-code-foundations/assets\"\u003Ethis repo\u003C/a\u003E. Create a folder on your local device and add the files:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Esample_business_requirements_source_onboarding.csv\u003C/code\u003E — new source systems (Baan, Workday), owners, priorities, and go-live targets\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esample_business_requirements_column_mapping.csv\u003C/code\u003E — field-level mappings for both new sources into the Silver schema\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Esample_business_requirements_business_rules.csv\u003C/code\u003E — BR-001 through BR-010 covering status normalization, dedup logic, currency handling, and open questions\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDownload all three before starting Demo 2.\u003C/p\u003E\n","\u003Ch3\u003EStep 2.1 – Read the Local PRD\u003C/h3\u003E\n","\u003Cp\u003EStart by understanding the business request. Save the CSV files from \u003Ccode\u003Eassets/\u003C/code\u003E to your working directory, then run:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ERead the local files sample_business_requirements_source_onboarding.csv and\nsample_business_requirements_business_rules.csv.\nSummarize the changes that affect the AP invoices pipeline.\nReturn:\n- New source systems being introduced\n- New fields or business rules that affect the Silver layer\n- Ambiguities or open questions that should be resolved before implementation\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A clear distinction between requirements and assumptions, and the shape of information your custom skill should standardize.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhy Create a Custom Skill?\u003C/h3\u003E\n","\u003Cp\u003EIf you stop here and simply ask Snowflake CoCo to update \u003Ccode\u003ESILVER_AP_INVOICES\u003C/code\u003E, you get a reasonable one-off result. But the repeatable pattern is the real value:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERead the PRD\u003C/li\u003E\u003Cli\u003EExtract requested changes\u003C/li\u003E\u003Cli\u003ETranslate those changes into a Dynamic Table plan\u003C/li\u003E\u003Cli\u003ESurface assumptions and open questions\u003C/li\u003E\u003Cli\u003EPropose validation queries\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThat is exactly the kind of workflow custom skills are meant to standardize.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhere custom skills live:\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELocation\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EScope\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBundled\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBuilt into Snowflake CoCo\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAvailable by default\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUser-level\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E~/.snowflake/cortex/skills/\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAvailable across projects\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProject-level\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E.cortex/skills/\u003C/code\u003E in your repo\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAvailable only in that project\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EFor this quickstart, use a \u003Cstrong\u003Eproject skill\u003C/strong\u003E so anyone who clones the repo gets the same behavior.\u003C/p\u003E\n","\u003Ch3\u003EStep 2.2 – Scaffold a Custom Skill\u003C/h3\u003E\n","\u003Cp\u003EConfirm the \u003Ccode\u003E$skill-development\u003C/code\u003E workflow is available with the following prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E/skill list\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen ask it to help you define the new custom skill:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EThis seems like a repeatable workflow I will have for many PRDs. Walk me through\n[$skill-development] for building a project skill that will help me take PRD-style\nfiles like this and turn them into a plan for putting them into a target Dynamic Table\n(for this demo, SILVER_AP_INVOICES).\n\nDefine:\n- When to use the skill\n- What inputs it expects (for example, prd_path and target_dynamic_table)\n- The exact outputs it should always return\n- Best practices for surfacing assumptions and open questions instead of guessing\n- An example usage for an AP invoices pipeline update\n\nRequirements:\n- Make it a project skill\n- Put it under .cortex/skills/ in this demo repo\n- Start by supporting XLSX files\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A skill that returns the same categories of output every time: requested changes, source-to-Silver mapping, open questions and assumptions, DDL delta plan, and validation queries.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2.3 – Run the PRD Evaluator Skill\u003C/h3\u003E\n","\u003Cp\u003EWith the custom skill in place, invoke the workflow instead of rebuilding the logic from scratch:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ERun the project skill we just made prd-to-silver\nContext:\n- prd_path: sample_business_requirements_column_mapping.csv\n- target_dynamic_table: SILVER_AP_INVOICES\nReturn:\n1. Summary of requested changes\n2. Source-to-Silver mapping summary\n3. Open questions and assumptions\n4. DDL delta plan for SILVER_AP_INVOICES\n5. Validation queries to run after implementation\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A consistent shape you could compare across future PRDs, and a delta plan another engineer could review before deployment.\nSave this output: \u003Ccode\u003Enotes/02_prd_change_plan.md\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2.4 – Apply the Update\u003C/h3\u003E\n","\u003Cp\u003EUse the structured output from the skill to update the Dynamic Table with the following prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUpdate SILVER_AP_INVOICES in my current schema using the change plan from\nsample_business_requirements_source_onboarding.csv,\nsample_business_requirements_column_mapping.csv, and\nsample_business_requirements_business_rules.csv.\nAssume the PRD introduces Baan and Workday as additional AP invoice sources.\nReturn:\n- The updated Dynamic Table SQL\n- A short explanation of how the new sources map into the common schema\n- Any assumptions that require engineering review\n- Validation queries that prove the update worked\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E Updated SQL, explainable source mapping, explicit assumptions, and validation queries.\nSave this output: \u003Ccode\u003Esql/03_silver_ap_invoices_prd_update.sql\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2.5 – Save Handoff Artifacts \u003Cem\u003E(optional)\u003C/em\u003E\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EList the artifacts in a local markdown file I should save from this PRD-driven update\nso another engineer can review the change, rerun the checks, and reuse the\nPRD Evaluator skill.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A concise handoff package another engineer can review and rerun.\nSave this output: \u003Ccode\u003Enotes/03_prd_workflow_handoff.md\u003C/code\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAt this point the quickstart is complete for most teams: you have a curated AP invoices object and a repeatable workflow for evolving it with new PRDs.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDemo 3: Build an Agent on the Data Product \u003Cem\u003E(Optional)\u003C/em\u003E\u003C/h2\u003E\n","\u003Cp\u003EBy this point you have a curated AP invoices Silver object and a repeatable way to evolve it based on business requirements. That is the right moment to introduce agents — because you can keep the AI experience grounded in trusted, well-modeled data.\u003C/p\u003E\n","\u003Ch3\u003EStep 3.1 – Define the Agent Use Case\u003C/h3\u003E\n","\u003Cp\u003EKeep the first pass narrow and grounded in the data product you built with this prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHelp me define a Cortex data agent on top of SILVER_AP_INVOICES in my current schema.\nSuggest:\n- The primary audience\n- The top five business questions the agent should answer\n- Guardrails that keep the agent grounded in the curated data\n- Any semantic descriptions that would improve answer quality\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3.2 – Create a Semantic View over the Silver Data\u003C/h3\u003E\n","\u003Cp\u003ECreate a semantic view that exposes business-friendly dimensions and measures. This is the object the agent will rely on for most of its answers.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ELet's start by building the semantic model using the $semantic-view.\nCreate a semantic view called SV_AP_ANALYTICS over <YOUR_SCHEMA>.SILVER_AP_INVOICES.\nIt should support natural language questions like:\n- "Total AP spend by vendor over the last 12 months"\n- "Invoice count by month and business unit"\n- "Top 10 vendors by unpaid invoice amount"\nReturn:\n- A complete semantic view definition that clearly names business measures and dimensions.\n- Any assumptions you are making about grain, time dimensions, and vendor identifiers.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E A semantic view definition with clear business measures, dimensions, and assumptions.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3.3 – Create a Cortex Agent\u003C/h3\u003E\n","\u003Cp\u003ENow create a Cortex data agent that uses the semantic view to answer natural-language questions with the following prompt:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E$cortex-agent\nCreate an agent named AP_ANALYTICS_ASSISTANT.\nThe agent should:\n- Prefer SV_AP_ANALYTICS as its primary data source.\n- Always respond with three parts: (1) the final answer, (2) the SQL used,\n  and (3) any assumptions about grain or filters.\n- Ask a clarifying question if the requested metric or time grain is ambiguous.\nReturn a configuration I can save alongside my project files.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3.4 – Audit the Semantic View\u003C/h3\u003E\n","\u003Cp\u003EWith the agent created, validate the semantic view and get improvement suggestions:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPrompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHelp me audit my Semantic View for best practices and provide suggestions.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat to look for:\u003C/strong\u003E An evaluation of the semantic view against best practices, with concrete suggestions for improving answer quality.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 3.5 – Skills and Workflows from Here\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E$agent-optimize\u003C/code\u003E and \u003Ccode\u003E$semantic-view\u003C/code\u003E skills provide structured workflows for iterating on your agent:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECapabilities\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E$semantic-view\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate, Audit (VQR testing, best practices), Debug (root cause analysis for failing queries)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E$agent-optimize\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreate, Ad-Hoc Test, Debug Query, Optimize (improve accuracy from evaluation results)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ECommon next steps teams take from here: running curated evaluation datasets, testing verified queries after schema changes, auditing semantic models for best practices, and iterating on agent accuracy based on user feedback.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EThe core pattern is simple: pick one concrete object, ask Snowflake CoCo for one concrete artifact, and keep the result in the project.\u003C/p\u003E\n","\u003Cp\u003EIn Demo 1, that means a Dynamic Table, a small runbook from a bundled skill, and a single proof query you can rerun after every change. In Demo 2, it means treating the PRD and its evaluator as part of the same data product — with a custom skill that turns a loosely written requirements file into a structured, reviewable change plan. By the time you reach the optional agent design in Demo 3, you can see how bundled skills and custom skills together create a path from disciplined data engineering to a credible AI experience.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECLI installation on Mac and Windows and Snowflake connection configuration\u003C/li\u003E\u003Cli\u003EData discovery, schema comparison, and Silver Dynamic Table creation with CoCo\u003C/li\u003E\u003Cli\u003EUsing Plan Mode (\u003Ccode\u003ECtrl+P\u003C/code\u003E) for multi-step, higher-risk operations\u003C/li\u003E\u003Cli\u003EApplying bundled skills (\u003Ccode\u003E$dynamic-tables\u003C/code\u003E) for operating runbooks\u003C/li\u003E\u003Cli\u003EBuilding and using custom skills to standardize repeatable PRD workflows\u003C/li\u003E\u003Cli\u003ECreating a Cortex Agent grounded on a semantic view with verifiable responses\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code\"\u003ESnowflake CoCo Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-connect\"\u003EManage Snowflake Connections\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-intro\"\u003EDynamic Tables Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agent\"\u003ECortex Agents Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides/get-started-with-guides\"\u003EGet Started with Snowflake Guides\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguides/issues\"\u003Esfguides GitHub Issues\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nSnowflake CoCo (CoCo) is Snowflake's AI coding agent built to accelerate data and AI workflows directly inside your Snowflake environment. It understands your data catalog, executes SQL as your identity, and integrates natively with dbt, Git, Python, and Streamlit.\n\nThis guide walks you through installing the CoCo CLI, configuring your Snowflake connection, and completing a hands-on workshop covering three real-world scenarios: building a Dynamic Table pipeline, maintaining and evolving it, and creating a Cortex Agent with a semantic view.\n\n### Prerequisites\n- A Snowflake account with Snowflake CoCo enabled\n\n### What You'll Learn\n- How to access Snowflake CoCo via Snowsight (UI) and CLI\n- How to install and configure the CoCo CLI \n- How to authenticate your Snowflake connection\n- How to build a Dynamic Table pipeline with CoCo\n- How to maintain a pipeline \n- How to create a Cortex Agent with a semantic view and evaluate its performance\n\n### What You'll Build\n- A configured Snowflake CoCo CLI connected to your Snowflake environment\n- A multi-source AP invoice pipeline using Dynamic Tables \n- A Cortex Agent grounded on a semantic view, with an evaluation framework\n\n\u003C!-- ------------------------ --\u003E\n## Access Snowflake CoCo in Snowsight\n\nBefore setting up the CLI, you can access Snowflake CoCo directly from the Snowflake UI.\n\nIn Snowsight, click the **blue star icon** in the bottom right corner to open the Snowflake CoCo panel.\n\n![Snowsight Home with CoCo Button](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/snowsight_home_coco_button.png?v=bff6daba)\n\nThis gives you immediate access to CoCo without any local installation and is useful for quick queries, schema exploration, and testing.\n\n\u003C!-- ------------------------ --\u003E\n## Install the CoCo CLI on Mac\n\n### Supported Architectures\n- **x64** — Fully supported\n- **ARM64** — Fully supported\n\n### Install via Terminal\n\n```bash\ncurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\n```\n\nWhen prompted to add `cortex` to your PATH, respond with **y**.\n\nIf the `cortex` command is not recognized after installation, close and reopen your terminal, or restart your machine.\n\n### Verify Installation\n\n```bash\ncortex --version\n```\n\n### Configure Your Snowflake Connection\n\nSnowflake CoCo uses the same connection files as SnowCLI, located at:\n\n```\n~/.snowflake/config.toml\n```\n\n(or `connections.toml` if you already use that)\n\n### Create/open the Config File \n\nRun the following in your terminal to create and open the config file:\n\n```bash\nmkdir -p ~/.snowflake\ntouch ~/.snowflake/config.toml\nchmod 600 ~/.snowflake/config.toml\nopen -e ~/.snowflake/config.toml\n```\n\nCopy and adapt the following to your accounts and roles:\n\n```toml\ndefault_connection_name = \"DEMO\"\n\n[connections.DEMO]\naccount   = \"\u003CYOUR_DEMO_ACCOUNT\u003E\"    # e.g. SFSENORTHAMERICA-XXXXX\nuser      = \"\u003CYOUR_DEMO_USERNAME\u003E\"\npassword  = \"\u003CYOUR_DEMO_PAT\u003E\"\nrole      = \"\u003CYOUR_DEMO_ROLE\u003E\"\n```\n\n![Log In and Find Settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/snowsight_login_settings.png?v=bff6daba)\n\n![Account Details Connection](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/account_details_connection.png?v=bff6daba)\n\nOnce updated, make sure to save the file. \n\n\n\u003C!-- ------------------------ --\u003E\n## Install the CoCo CLI on Windows\n\n### Supported Architectures\n- **x64** — Fully supported\n- **ARM64** — Support is in progress; not yet recommended for production use\n\n### Install via PowerShell\n\n```powershell\nirm https://ai.snowflake.com/static/cc-scripts/install.ps1 | iex\n```\n\nWhen prompted to add `cortex` to your PATH, respond with **y**.\n\nIf the `cortex` command is not recognized after installation, close and reopen your terminal or restart your machine.\n\n### Verify Installation\n\n```powershell\ncortex --version\n```\n\n### Configure Your Snowflake Connection\n\nOn Windows, the config file is located at:\n\n```\n%USERPROFILE%\\.snowflake\\config.toml\n```\n\n### Create/open the Config File\n\nRun the following in PowerShell:\n\n```powershell\nmkdir $env:USERPROFILE\\.snowflake -Force\nNew-Item -ItemType File -Path $env:USERPROFILE\\.snowflake\\config.toml -Force\nnotepad $env:USERPROFILE\\.snowflake\\config.toml\n```\n\nCopy and adapt the following to your accounts and roles:\n\n```toml\ndefault_connection_name = \"DEMO\"    # Name for default Snowflake connection\n\n[connections.DEMO]                  # Name for Snowflake connection (same as above if default)\naccount   = \"\u003CYOUR_DEMO_ACCOUNT\u003E\"  # e.g. Account identifier\nuser      = \"\u003CYOUR_DEMO_USERNAME\u003E\" # e.g. Authentication variable\npassword  = \"\u003CYOUR_DEMO_PAT\u003E\"\nrole      = \"\u003CYOUR_DEMO_ROLE\u003E\"     # e.g. Need a role for connection\n```\n\n![Log In and Find Settings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/snowsight_login_settings.png?v=bff6daba)\n\n![Account Details Connection](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/account_details_connection.png?v=bff6daba)\n\nOnce updated, make sure to save the file. \n\n\u003C!-- ------------------------ --\u003E\n## Find Your Connection Details\n\nTo fill out your config, you'll need your Snowflake account identifier, role, and warehouse.\n\n1. Log in to Snowsight\n2. Click your username in the bottom left → **Account** → **View account details**\n3. Note your **Account Identifier** (e.g. `SFSENORTHAMERICA-XXXXX`) and **Role**\n\n### Authentication Options\n\nThere are several ways to authenticate your connection. Common environment variables include:\n\n| Variable | Description |\n|---|---|\n| `SNOWFLAKE_ACCOUNT` | Your account identifier |\n| `SNOWFLAKE_USER` | Your username |\n| `SNOWFLAKE_PASSWORD` | Password authentication |\n| `SNOWFLAKE_TOKEN` | OAuth token |\n| `SNOWFLAKE_TOKEN_FILE_PATH` | Path to token file |\n| `SNOWFLAKE_OAUTH_CLIENT_ID` | OAuth client ID |\n\nFor a full list, see the [Manage Snowflake connections guide](https://docs.snowflake.com/en/user-guide/snowsql-connect).\n\n\u003C!-- ------------------------ --\u003E\n## Launch Snowflake CoCo\n\nOnce your connection is configured, launch the CLI:\n\n```bash\ncortex -c DEMO\n```\n\nAfter connecting, you'll see the Snowflake CoCo CLI interface. Test your connection by asking a simple question — for example:\n\n```\nWhat databases do I have access to?\n```\n\n![Snowflake CoCo CLI Interface](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/cortex-code-foundations/cortex_code_cli_interface.png?v=bff6daba)\n\n\u003C!-- ------------------------ --\u003E\n## Lab Setup\n\nBefore starting the demos, run the setup scripts to create the workshop environment and load sample data. These files are in the `assets/` folder of [this repo](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/cortex-code-foundations/assets).\n\n### Step 1: Create the Workshop Environment\n\nRun `00_snowday_setup.sql` as `SYSADMIN`. This creates:\n- `COCO_WORKSHOP` database\n- `PIPELINE_LAB` and `SOURCE_DATA` schemas\n- `COCO_WORKSHOP_WH` warehouse (X-Small, auto-suspend 120s)\n- Tags for optional governance exercises\n\n### Step 2: Load Sample Data\n\nRun `00_sample_data.sql`. This creates and populates:\n- `BRONZE_SAP_AP_INVOICES` — 15 SAP invoices (USD, EUR, GBP)\n- `BRONZE_ORACLE_AP_INVOICES` — 15 Oracle invoices (USD, EUR, GBP)\n- `BRONZE_BAAN_AP_INVOICES` — 10 Baan invoices (EUR, GBP) — used in Demo 2\n- `BRONZE_WORKDAY_AP_INVOICES` — 10 Workday invoices (USD, GBP, EUR) — used in Demo 2\n- `AGENT_EVAL_SET` — 15 golden evaluation questions — used in Demo 3\n\n### Resetting Between Runs\n\nIf you need to start fresh, run `01_demo_reset.sql` to drop all demo-created objects, then re-run the setup scripts above.\n\n\u003E **Running this in a customer account with Snowflake CoCo enabled?** You will need the `SNOWFLAKE.CORTEX_USER` database role on your user (directly or via a parent role) so Snowflake CoCo CLI can use Snowflake AI features. Use the following files in the `/assets` subfolder in the repo: `00_admin_lab_setup.sql`, `01_admin_lab_reset.sql`, `02_admin_lab_teardown.sql`, `03_participant_connection_template.toml`\n\n\u003C!-- ------------------------ --\u003E\n## Workshop Overview\n\nThis workshop follows a single AP invoices storyline across three demos — from data discovery through operationalization and optional agent design.\n\n\u003C!-- ------------------------ --\u003E\n## Demo 1: Pipeline Builder\n\nFinance needs a trusted AP invoices layer. SAP and Oracle invoice data already exist in Snowflake, but there is no standardized Silver object yet. The goal is a single, well-modeled table that can be explained, monitored, and evolved without repeating discovery work each time a requirement changes.\n\n### Step 1.1 – Discover the Source\n\nBegin with data discovery, a natural first move when you enter a new schema.\n\n**Prompt:**\n```\nWhat tables are in the `COCO_WORKSHOP.SOURCE_DATA` schema?\nFor each table, give me a one-line description of what it appears to contain and identify\nwhich ones are most relevant to an AP invoices Silver layer.\n```\n\n\u003E **What to look for:** A short list of tables relevant to the AP workflow, with enough description to orient yourself without manually opening each object.\n\n### Step 1.2 – Compare SAP and Oracle Invoice Schemas\n\nOnce you know which tables matter, compare them to get the shortest path to a clean normalization plan.\n\n**Prompt:**\n```\nCompare the columns between\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_SAP_AP_INVOICES and\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_ORACLE_AP_INVOICES.\nReturn:\n- Equivalent fields with different names\n- Fields that require type normalization or default values\n- Differences that should remain open questions instead of becoming hidden assumptions\n```\n\n\u003E **What to look for:** Equivalent business fields with different names, normalization work you should expect before building Silver, and open questions that should stay explicit for later review.\n\n### Step 1.3 – Generate the Silver Dynamic Table\n\nFor this step, turn on **Plan Mode** (`Ctrl+P`) to see how Snowflake CoCo thinks through a multi-step task before executing.\n\n**Plan Mode** stays read-only while it thinks, then returns a structured multi-step plan and waits for your approval before executing. This is useful for higher-risk tasks like creating core tables.\n\n```\nCtrl+P\n```\n\nThen run the following prompt: \n\n```\nUse database COCO_WORKSHOP and schema PIPELINE_LAB for outputs.\nCreate a Dynamic Table called SILVER_AP_INVOICES in my current schema by combining\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_SAP_AP_INVOICES and\nCOCO_WORKSHOP.SOURCE_DATA.BRONZE_ORACLE_AP_INVOICES.\n```\n\n\u003E **What to look for:** A clean first-pass Dynamic Table definition with explainable design choices.\n\nWhen finished, exit Plan Mode — it should turn off automatically when you approve execution.\n\n### Step 1.4 – Use the Bundled $dynamic-tables Skill\n\nSkills are reusable workflows that tell Snowflake CoCo how to handle a specific Snowflake task. Instead of responding in a completely open-ended way, a skill provides domain context, expected inputs, a defined process, and structured outputs.\n\n**Bundled skills** ship with Snowflake CoCo — prebuilt, Snowflake-native workflows designed to start from proven patterns.\n\nFirst, see what skills are available:\n\n**Prompt:**\n```\n/skill list\n```\n\nThen inspect the Dynamic Tables skill before applying it:\n\n**Prompt:**\n```\nWhat does the $dynamic-tables skill do? Summarize when I should use it,\nwhat inputs it expects, and what kinds of output it returns.\n```\n\nThen apply it to `SILVER_AP_INVOICES`:\n\n**Prompt:**\n```\n$dynamic-tables\nAnalyze the Dynamic Table SILVER_AP_INVOICES in my current schema.\nReturn:\n1. The recommended TARGET_LAG choice for this workflow and why\n2. SQL to inspect current state, lag, and refresh history\n3. The main failure or staleness patterns to watch for\n4. A short best-practices checklist for operating this table well\n```\n\n\u003E **What to look for:** A runbook you would actually keep — a couple of monitoring queries and a concise operating checklist, not generic advice.\n\n### Step 1.5 – Save a Proof Query *(optional)*\n\nEnd Demo 1 with a lightweight proof query you can rerun after every change.\n\n```\nGive me one concise proof query for SILVER_AP_INVOICES that shows record counts\nby SOURCE_SYSTEM and is easy to rerun after future changes.\n```\n\n\u003E **What to look for:** A concise query that can be reused after every change.\n\u003E Save this output: `sql/02_silver_ap_invoices_proof.sql`\n\nBy the end of Demo 1 you have a Silver-grade AP invoices Dynamic Table, an operating runbook generated by a bundled skill, and a proof query you can rerun after each change.\n\n\u003C!-- ------------------------ --\u003E\n## Demo 2: Add Local Context and Productionize\n\nA month later, the business sends a PRD that expands the AP invoices pipeline. New source systems must be added, field requirements have changed, and some definitions need clarification. Rather than solving this once with a long prompt every time, you can standardize the workflow as a custom skill.\n\n### About the Sample PRD\n\nDownload the three CSV files prepared by the Finance Transformation PMO in the `assets/` folder of [this repo](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/cortex-code-foundations/assets). Create a folder on your local device and add the files:\n\n- `sample_business_requirements_source_onboarding.csv` — new source systems (Baan, Workday), owners, priorities, and go-live targets\n- `sample_business_requirements_column_mapping.csv` — field-level mappings for both new sources into the Silver schema\n- `sample_business_requirements_business_rules.csv` — BR-001 through BR-010 covering status normalization, dedup logic, currency handling, and open questions\n\nDownload all three before starting Demo 2.\n\n### Step 2.1 – Read the Local PRD\n\nStart by understanding the business request. Save the CSV files from `assets/` to your working directory, then run:\n\n**Prompt:**\n```\nRead the local files sample_business_requirements_source_onboarding.csv and\nsample_business_requirements_business_rules.csv.\nSummarize the changes that affect the AP invoices pipeline.\nReturn:\n- New source systems being introduced\n- New fields or business rules that affect the Silver layer\n- Ambiguities or open questions that should be resolved before implementation\n```\n\n\u003E **What to look for:** A clear distinction between requirements and assumptions, and the shape of information your custom skill should standardize.\n\n### Why Create a Custom Skill?\n\nIf you stop here and simply ask Snowflake CoCo to update `SILVER_AP_INVOICES`, you get a reasonable one-off result. But the repeatable pattern is the real value:\n\n- Read the PRD\n- Extract requested changes\n- Translate those changes into a Dynamic Table plan\n- Surface assumptions and open questions\n- Propose validation queries\n\nThat is exactly the kind of workflow custom skills are meant to standardize.\n\n**Where custom skills live:**\n\n| Skill Type | Location | Scope |\n|---|---|---|\n| Bundled | Built into Snowflake CoCo | Available by default |\n| User-level | `~/.snowflake/cortex/skills/` | Available across projects |\n| Project-level | `.cortex/skills/` in your repo | Available only in that project |\n\nFor this quickstart, use a **project skill** so anyone who clones the repo gets the same behavior.\n\n### Step 2.2 – Scaffold a Custom Skill\n\nConfirm the `$skill-development` workflow is available with the following prompt:\n\n```\n/skill list\n```\n\nThen ask it to help you define the new custom skill:\n\n**Prompt:**\n```\nThis seems like a repeatable workflow I will have for many PRDs. Walk me through\n[$skill-development] for building a project skill that will help me take PRD-style\nfiles like this and turn them into a plan for putting them into a target Dynamic Table\n(for this demo, SILVER_AP_INVOICES).\n\nDefine:\n- When to use the skill\n- What inputs it expects (for example, prd_path and target_dynamic_table)\n- The exact outputs it should always return\n- Best practices for surfacing assumptions and open questions instead of guessing\n- An example usage for an AP invoices pipeline update\n\nRequirements:\n- Make it a project skill\n- Put it under .cortex/skills/ in this demo repo\n- Start by supporting XLSX files\n```\n\n\u003E **What to look for:** A skill that returns the same categories of output every time: requested changes, source-to-Silver mapping, open questions and assumptions, DDL delta plan, and validation queries.\n\n### Step 2.3 – Run the PRD Evaluator Skill\n\nWith the custom skill in place, invoke the workflow instead of rebuilding the logic from scratch:\n\n**Prompt:**\n```\nRun the project skill we just made prd-to-silver\nContext:\n- prd_path: sample_business_requirements_column_mapping.csv\n- target_dynamic_table: SILVER_AP_INVOICES\nReturn:\n1. Summary of requested changes\n2. Source-to-Silver mapping summary\n3. Open questions and assumptions\n4. DDL delta plan for SILVER_AP_INVOICES\n5. Validation queries to run after implementation\n```\n\n\u003E **What to look for:** A consistent shape you could compare across future PRDs, and a delta plan another engineer could review before deployment.\n\u003E Save this output: `notes/02_prd_change_plan.md`\n\n### Step 2.4 – Apply the Update\n\nUse the structured output from the skill to update the Dynamic Table with the following prompt:\n\n```\nUpdate SILVER_AP_INVOICES in my current schema using the change plan from\nsample_business_requirements_source_onboarding.csv,\nsample_business_requirements_column_mapping.csv, and\nsample_business_requirements_business_rules.csv.\nAssume the PRD introduces Baan and Workday as additional AP invoice sources.\nReturn:\n- The updated Dynamic Table SQL\n- A short explanation of how the new sources map into the common schema\n- Any assumptions that require engineering review\n- Validation queries that prove the update worked\n```\n\n\u003E **What to look for:** Updated SQL, explainable source mapping, explicit assumptions, and validation queries.\n\u003E Save this output: `sql/03_silver_ap_invoices_prd_update.sql`\n\n### Step 2.5 – Save Handoff Artifacts *(optional)*\n\n**Prompt:**\n```\nList the artifacts in a local markdown file I should save from this PRD-driven update\nso another engineer can review the change, rerun the checks, and reuse the\nPRD Evaluator skill.\n```\n\n\u003E **What to look for:** A concise handoff package another engineer can review and rerun.\n\u003E Save this output: `notes/03_prd_workflow_handoff.md`\n\nAt this point the quickstart is complete for most teams: you have a curated AP invoices object and a repeatable workflow for evolving it with new PRDs.\n\n\u003C!-- ------------------------ --\u003E\n## Demo 3: Build an Agent on the Data Product *(Optional)*\n\nBy this point you have a curated AP invoices Silver object and a repeatable way to evolve it based on business requirements. That is the right moment to introduce agents — because you can keep the AI experience grounded in trusted, well-modeled data.\n\n### Step 3.1 – Define the Agent Use Case\n\nKeep the first pass narrow and grounded in the data product you built with this prompt:\n\n```\nHelp me define a Cortex data agent on top of SILVER_AP_INVOICES in my current schema.\nSuggest:\n- The primary audience\n- The top five business questions the agent should answer\n- Guardrails that keep the agent grounded in the curated data\n- Any semantic descriptions that would improve answer quality\n```\n\n### Step 3.2 – Create a Semantic View over the Silver Data\n\nCreate a semantic view that exposes business-friendly dimensions and measures. This is the object the agent will rely on for most of its answers.\n\n**Prompt:**\n```\nLet's start by building the semantic model using the $semantic-view.\nCreate a semantic view called SV_AP_ANALYTICS over \u003CYOUR_SCHEMA\u003E.SILVER_AP_INVOICES.\nIt should support natural language questions like:\n- \"Total AP spend by vendor over the last 12 months\"\n- \"Invoice count by month and business unit\"\n- \"Top 10 vendors by unpaid invoice amount\"\nReturn:\n- A complete semantic view definition that clearly names business measures and dimensions.\n- Any assumptions you are making about grain, time dimensions, and vendor identifiers.\n```\n\n\u003E **What to look for:** A semantic view definition with clear business measures, dimensions, and assumptions.\n\n### Step 3.3 – Create a Cortex Agent\n\nNow create a Cortex data agent that uses the semantic view to answer natural-language questions with the following prompt:\n\n```\n$cortex-agent\nCreate an agent named AP_ANALYTICS_ASSISTANT.\nThe agent should:\n- Prefer SV_AP_ANALYTICS as its primary data source.\n- Always respond with three parts: (1) the final answer, (2) the SQL used,\n  and (3) any assumptions about grain or filters.\n- Ask a clarifying question if the requested metric or time grain is ambiguous.\nReturn a configuration I can save alongside my project files.\n```\n\n### Step 3.4 – Audit the Semantic View\n\nWith the agent created, validate the semantic view and get improvement suggestions:\n\n**Prompt:**\n```\nHelp me audit my Semantic View for best practices and provide suggestions.\n```\n\n\u003E **What to look for:** An evaluation of the semantic view against best practices, with concrete suggestions for improving answer quality.\n\n### Step 3.5 – Skills and Workflows from Here\n\nThe `$agent-optimize` and `$semantic-view` skills provide structured workflows for iterating on your agent:\n\n| Skill | Capabilities |\n|---|---|\n| `$semantic-view` | Create, Audit (VQR testing, best practices), Debug (root cause analysis for failing queries) |\n| `$agent-optimize` | Create, Ad-Hoc Test, Debug Query, Optimize (improve accuracy from evaluation results) |\n\nCommon next steps teams take from here: running curated evaluation datasets, testing verified queries after schema changes, auditing semantic models for best practices, and iterating on agent accuracy based on user feedback.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nThe core pattern is simple: pick one concrete object, ask Snowflake CoCo for one concrete artifact, and keep the result in the project.\n\nIn Demo 1, that means a Dynamic Table, a small runbook from a bundled skill, and a single proof query you can rerun after every change. In Demo 2, it means treating the PRD and its evaluator as part of the same data product — with a custom skill that turns a loosely written requirements file into a structured, reviewable change plan. By the time you reach the optional agent design in Demo 3, you can see how bundled skills and custom skills together create a path from disciplined data engineering to a credible AI experience.\n\n### What You Learned\n- CLI installation on Mac and Windows and Snowflake connection configuration\n- Data discovery, schema comparison, and Silver Dynamic Table creation with CoCo\n- Using Plan Mode (`Ctrl+P`) for multi-step, higher-risk operations\n- Applying bundled skills (`$dynamic-tables`) for operating runbooks\n- Building and using custom skills to standardize repeatable PRD workflows\n- Creating a Cortex Agent grounded on a semantic view with verifiable responses\n\n### Related Resources\n- [Snowflake CoCo Documentation](https://docs.snowflake.com/en/user-guide/cortex-code)\n- [Manage Snowflake Connections](https://docs.snowflake.com/en/user-guide/snowsql-connect)\n- [Dynamic Tables Overview](https://docs.snowflake.com/en/user-guide/dynamic-tables-intro)\n- [Cortex Agents Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agent)\n- [Get Started with Snowflake Guides](https://www.snowflake.com/en/developers/guides/get-started-with-guides)\n- [sfguides GitHub Issues](https://github.com/Snowflake-Labs/sfguides/issues)\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-65257e54c1","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"id":"container-3e4e095e00","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-f2bf6a9792","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-09",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-36f02d17c5","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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