snowflake-semanticview

github/awesome-copilot · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview
0 commentsdiscussion
summary

Build and validate Snowflake semantic views using Snowflake CLI with guided DDL creation and testing.

  • Handles the complete semantic view lifecycle: drafting DDL, populating synonyms and comments from Snowflake table metadata, validating against Snowflake via CLI, and executing final CREATE or ALTER statements
  • Requires one-time Snowflake CLI installation and connection setup; confirms prerequisites before proceeding with validation
  • Validates all DDL against Snowflake using temporary v
skill.md

Snowflake Semantic Views

One-Time Setup

Workflow For Each Semantic View Request

  1. Confirm the target database, schema, role, warehouse, and final semantic view name.
  2. Confirm the model follows a star schema (facts with conformed dimensions).
  3. Draft the semantic view DDL using the official syntax:
  4. Populate synonyms and comments for each dimension, fact, and metric:
    • Read Snowflake table/view/column comments first (preferred source):
    • If comments or synonyms are missing, ask whether you can create them, whether the user wants to provide text, or whether you should draft suggestions for approval.
  5. Use SELECT statements with DISTINCT and LIMIT (maximum 1000 rows) to discover relationships between fact and dimension tables, identify column data types, and create more meaningful comments and synonyms for columns.
  6. Create a temporary validation name (for example, append __tmp_validate) while keeping the same database and schema.
  7. Always validate by sending the DDL to Snowflake via Snowflake CLI before finalizing:
    • Use snow sql to execute the statement with the configured connection.
    • If flags differ by version, check snow sql --help and use the connection option shown there.
  8. If validation fails, iterate on the DDL and re-run the validation step until it succeeds.
  9. Apply the final DDL (create or alter) using the real semantic view name.
  10. Run a sample query against the final semantic view to confirm it works as expected. It has a different SQL syntax as can be seen here: https://docs.snowflake.com/en/user-guide/views-semantic/querying#querying-a-semantic-view Example:
SELECT * FROM SEMANTIC_VIEW(
    my_semview_name
    DIMENSIONS customer.customer_market_segment
    METRICS orders.order_average_value
)
ORDER BY customer_market_segment;
  1. Clean up any temporary semantic view created during validation.

Synonyms And Comments (Required)

  • Use the semantic view syntax for synonyms and comments:
WITH SYNONYMS [ = ] ( 'synonym' [ , ... ] )
COMMENT = 'comment_about_dim_fact_or_metric'
  • Treat synonyms as informational only; do not use them to reference dimensions, facts, or metrics elsewhere.
  • Use Snowflake comments as the preferred and first source for synonyms and comments:
  • If Snowflake comments are missing, ask whether you can create them, whether the user wants to provide text, or whether you should draft suggestions for approval.
  • Do not invent synonyms or comments without user approval.

Validation Pattern (Required)

  • Never skip validation. Always execute the DDL against Snowflake with Snowflake CLI before presenting it as final.
  • Prefer a temporary name for validation to avoid clobbering the real view.

Example CLI Validation (Template)

# Replace placeholders with real values.
snow sql -q "<CREATE OR ALTER SEMANTIC VIEW ...>" --connection <connection_name>

If the CLI uses a different connection flag in your version, run:

snow sql --help

Notes

  • Treat installation and connection setup as one-time steps, but confirm they are done before the first validation.
  • Keep the final semantic view definition identical to the validated temporary definition except for the name.
  • Do not omit synonyms or comments; consider them required for completeness even if optional in syntax.
how to use snowflake-semanticview

How to use snowflake-semanticview on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add snowflake-semanticview
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview

The skills CLI fetches snowflake-semanticview from GitHub repository github/awesome-copilot and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/snowflake-semanticview

Reload or restart Cursor to activate snowflake-semanticview. Access the skill through slash commands (e.g., /snowflake-semanticview) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.636 reviews
  • Arjun Mensah· Dec 20, 2024

    I recommend snowflake-semanticview for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ganesh Mohane· Dec 12, 2024

    Solid pick for teams standardizing on skills: snowflake-semanticview is focused, and the summary matches what you get after install.

  • Anaya Sharma· Dec 8, 2024

    Solid pick for teams standardizing on skills: snowflake-semanticview is focused, and the summary matches what you get after install.

  • Hana Li· Dec 4, 2024

    Registry listing for snowflake-semanticview matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ama Menon· Nov 27, 2024

    We added snowflake-semanticview from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Hana Verma· Nov 23, 2024

    Useful defaults in snowflake-semanticview — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yash Thakker· Nov 11, 2024

    snowflake-semanticview is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Hana Smith· Nov 11, 2024

    snowflake-semanticview reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sakshi Patil· Nov 3, 2024

    We added snowflake-semanticview from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chaitanya Patil· Oct 22, 2024

    snowflake-semanticview fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

showing 1-10 of 36

1 / 4