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Implements comprehensive workflow pattern guidance from 31,917 real workflows. Addresses the most common use case (813 webhook searches). Files created: - 5 evaluations testing pattern selection and implementation - SKILL.md (486 lines) - Pattern overview, selection guide, workflow checklist - webhook_processing.md (554 lines) - Webhook patterns, $json.body gotcha, auth - http_api_integration.md (763 lines) - REST APIs, pagination, rate limiting - database_operations.md (854 lines) - DB ops, batch processing, SQL injection prevention - ai_agent_workflow.md (918 lines) - AI agents, 8 connection types, tool configuration - scheduled_tasks.md (845 lines) - Cron schedules, timezone handling, monitoring - README.md - Skill metadata with pattern statistics Key features: - 5 core patterns: Webhook (35%), HTTP API (892 templates), Database (456), AI (234), Scheduled (28%) - Workflow creation checklist (planning → implementation → validation → deployment) - Pattern selection guide with statistics - Common gotchas documented (webhook $json.body, SQL injection, timezone, etc.) - Error handling strategies for each pattern - Performance optimization techniques - Security best practices - Testing approaches - Real template examples Critical insights: - Webhook data under $json.body (not $json) - #1 gotcha - Always use parameterized queries (SQL injection prevention) - ANY node can be an AI tool (connect to ai_tool port) - Set workflow timezone explicitly (DST handling) - Authentication via credentials (never hardcode) Pattern statistics: - Trigger distribution: Webhook 35%, Schedule 28%, Manual 22%, Service 15% - Transformation: Set 68%, Code 42%, IF 38%, Switch 18% - Output: HTTP 45%, Slack 32%, Database 28%, Email 24% - Complexity: Simple 42%, Medium 38%, Complex 20% Total: ~4,420 lines across 12 files Based on analysis of 31,917 real workflows and usage patterns. 🤖 Conceived by Romuald Członkowski - https://www.aiadvisors.pl/en
15 lines
653 B
JSON
15 lines
653 B
JSON
{
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"id": "pattern-002",
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"skills": ["n8n-workflow-patterns"],
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"query": "I want to fetch data from a REST API, transform it, and store it in my database. How should I structure this workflow?",
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"expected_behavior": [
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"Identifies this as an HTTP API Integration pattern",
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"References the http_api_integration.md pattern file",
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"Explains the structure: Trigger → HTTP Request → Transform → Database → Error Handler",
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"Discusses authentication handling (credentials)",
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"Mentions pagination for large datasets",
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"Suggests retry logic for API failures",
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"Provides guidance on response parsing and data mapping"
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]
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}
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