PatrickJS/awesome-cursorrules is a collection of Markdown rule files that give Cursor AI editor project-specific instructions about code, frameworks, workflows, and standards. Developers use it to find reusable guidance for shaping Cursor’s behavior in different kinds of software projects.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/patrickjs/awesome-cursorrules/snowflake-cortex-ai-cursorrules-prompt-filegit clone --depth 1 https://github.com/PatrickJS/awesome-cursorrulesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/patrickjs/awesome-cursorrules/snowflake-cortex-ai-cursorrules-prompt-file)<a href="https://agentmods.dev/rules/patrickjs/awesome-cursorrules/snowflake-cortex-ai-cursorrules-prompt-file"><img src="https://agentmods.dev/badge/rules/patrickjs/awesome-cursorrules/snowflake-cortex-ai-cursorrules-prompt-file.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.01667 |
| Opus 5 | $0.00000 | $0.00834 |
| Sonnet 5 | $0.00000 | $0.00333 |
| Haiku 4.5 | $0.00000 | $0.00167 |
Grade A, and why
snowflake-cortex-ai-cursorrules-prompt-file scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
// Snowflake Cortex AI // Expert guidance for Cortex AI Functions and Cortex Search (hybrid vector+keyword search)
You are an expert in Snowflake Cortex — the AI layer of Snowflake including Cortex AI Functions (SQL-callable LLM/ML functions) and Cortex Search (managed hybrid search for RAG applications). All processing runs inside Snowflake with no data leaving the platform.
// ═══════════════════════════════════════════ // CORTEX AI FUNCTIONS // ═══════════════════════════════════════════
// Available Functions (use these names — they are the current versions): // AI_COMPLETE — General-purpose LLM completion (text, images, documents). // AI_CLASSIFY — Classify text/images into user-defined categories (multi-label supported). // AI_FILTER — Returns TRUE/FALSE for text/image input. Use in WHERE clauses. // AI_AGG — Aggregate insights across rows of text (no context window limit). // AI_EMBED — Generate embedding vectors (similarity search, clustering). // AI_EXTRACT — Extract structured info from text, images, or documents. // AI_SENTIMENT — Sentiment score from text (-1 to 1). // AI_SUMMARIZE_AGG — Summarize across rows (no context window limit). // AI_SIMILARITY — Embedding similarity between two inputs. // AI_TRANSCRIBE — Transcribe audio/video from stages. // AI_PARSE_DOCUMENT — OCR or text+layout extraction from documents in stages. // AI_REDACT — Redact PII from text. // AI_TRANSLATE — Translate between supported languages.
// Helper Functions: // TO_FILE('@stage', 'filename') — File reference for document processing. // AI_COUNT_TOKENS(model, text) — Check token count before calling a model. // PROMPT('template {0}', arg) — Build prompt objects for AI_COMPLETE. // TRY_COMPLETE — Returns NULL on failure instead of error.
// AI_COMPLETE — The Primary Function // Models: claude-4-opus, claude-4-sonnet, claude-sonnet-4-5, claude-opus-4-5, claude-haiku-4-5, // gemini-3-pro, llama3.1-70b, llama3.1-8b, llama3.3-70b, mistral-large2, mistral-small2, deepseek-r1
// Text completion: SELECT AI_COMPLETE(MODEL => 'claude-4-sonnet', PROMPT => 'Summarize: ' || review_text) FROM reviews;
// Document processing: SELECT AI_COMPLETE( MODEL => 'claude-4-sonnet', PROMPT => PROMPT('Extract the invoice total from {0}', TO_FILE('@docs', 'invoice.pdf')) );
// Structured JSON output: SELECT AI_COMPLETE(MODEL => 'claude-4-sonnet', PROMPT => 'Extract name, email, company as JSON: ' || raw_text)::VARIANT AS extracted FROM contacts;
// AI_CLASSIFY: SELECT AI_CLASSIFY(ticket_text, ['billing', 'technical', 'account', 'other']) AS category FROM tickets; // Multi-label: AI_CLASSIFY(input, categories, {'output_mode': 'multi'})
// AI_FILTER (natural-language WHERE): SELECT * FROM reviews WHERE AI_FILTER(review_text, 'mentions product quality issues');
// AI_AGG (cross-row aggregation): SELECT AI_AGG(feedback_text, 'What are the top 3 themes?') FROM customer_feedback;
// AI_EXTRACT (entity extraction): SELECT AI_EXTRACT(email_body, 'meeting date', 'attendees', 'action items') FROM emails;
// AI_SENTIMENT: SELECT review_text, AI_SENTIMENT(review_text) AS sentiment FROM product_reviews; // AI_EMBED: SELECT AI_EMBED(description) AS embedding FROM products; // AI_PARSE_DOCUMENT: SELECT AI_PARSE_DOCUMENT(TO_FILE('@docs', 'contract.pdf'), MODE => 'LAYOUT'); // AI_TRANSCRIBE: SELECT AI_TRANSCRIBE(TO_FILE('@media', 'recording.mp3')) AS transcript; // AI_REDACT: SELECT AI_REDACT(customer_notes) AS redacted FROM support_cases;
// Privileges: USE AI FUNCTIONS account privilege + SNOWFLAKE.CORTEX_USER database role (both granted to PUBLIC by default).
// ═══════════════════════════════════════════ // CORTEX SEARCH — Hybrid Vector + Keyword Search // ═══════════════════════════════════════════
// Fully managed search combining vector (semantic) and keyword (lexical) search. // Use cases: RAG for LLM chatbots, enterprise search, AI-powered Q&A.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 140 lines · 1,667 tokens per session scan A 1e267d60cabf
snowflake-cortex-ai-cursorrules-prompt-file is a cursor rule published in the GitHub repository PatrickJS/awesome-cursorrules (40,722 stars, last pushed 3mo ago), licensed CC0-1.0. It costs nothing until one of its globs matches a file; then it loads 1,667 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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