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Use when writing Python in n8n, using _input\u002F_json\u002F_node syntax, working with standard library, or need to understand Python limitations in n8n Code nodes.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"python","code","n8n code python","n8n","python code","writing","with","understand","input","writing python","Nodes","use","write","syntax","node","json","library","2026-11-10 10:12:30",false,"---\nname: n8n-code-python\ndescription: Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input\u002F_json\u002F_node syntax, working with standard library, or need to understand Python limitations in n8n Code nodes.\n---\n\n# Python Code Node (Beta)\n\nExpert guidance for writing Python code in n8n Code nodes.\n\n---\n\n## ⚠️ Important: JavaScript First\n\n**Recommendation**: Use **JavaScript for 95% of use cases**. Only use Python when:\n- You need specific Python standard library functions\n- You're significantly more comfortable with Python syntax\n- You're doing data transformations better suited to Python\n\n**Why JavaScript is preferred:**\n- Full n8n helper functions ($helpers.httpRequest, etc.)\n- Luxon DateTime library for advanced date\u002Ftime operations\n- No external library limitations\n- Better n8n documentation and community support\n\n---\n\n## Quick Start\n\n```python\n# Basic template for Python Code nodes\nitems = _input.all()\n\n# Process data\nprocessed = []\nfor item in items:\n    processed.append({\n        \"json\": {\n            **item[\"json\"],\n            \"processed\": True,\n            \"timestamp\": datetime.now().isoformat()\n        }\n    })\n\nreturn processed\n```\n\n### Essential Rules\n\n1. **Consider JavaScript first** - Use Python only when necessary\n2. **Access data**: `_input.all()`, `_input.first()`, or `_input.item`\n3. **CRITICAL**: Must return `[{\"json\": {...}}]` format\n4. **CRITICAL**: Webhook data is under `_json[\"body\"]` (not `_json` directly)\n5. **CRITICAL LIMITATION**: **No external libraries** (no requests, pandas, numpy)\n6. **Standard library only**: json, datetime, re, base64, hashlib, urllib.parse, math, random, statistics\n\n---\n\n## Mode Selection Guide\n\nSame as JavaScript - choose based on your use case:\n\n### Run Once for All Items (Recommended - Default)\n\n**Use this mode for:** 95% of use cases\n\n- **How it works**: Code executes **once** regardless of input count\n- **Data access**: `_input.all()` or `_items` array (Native mode)\n- **Best for**: Aggregation, filtering, batch processing, transformations\n- **Performance**: Faster for multiple items (single execution)\n\n```python\n# Example: Calculate total from all items\nall_items = _input.all()\ntotal = sum(item[\"json\"].get(\"amount\", 0) for item in all_items)\n\nreturn [{\n    \"json\": {\n        \"total\": total,\n        \"count\": len(all_items),\n        \"average\": total \u002F len(all_items) if all_items else 0\n    }\n}]\n```\n\n### Run Once for Each Item\n\n**Use this mode for:** Specialized cases only\n\n- **How it works**: Code executes **separately** for each input item\n- **Data access**: `_input.item` or `_item` (Native mode)\n- **Best for**: Item-specific logic, independent operations, per-item validation\n- **Performance**: Slower for large datasets (multiple executions)\n\n```python\n# Example: Add processing timestamp to each item\nitem = _input.item\n\nreturn [{\n    \"json\": {\n        **item[\"json\"],\n        \"processed\": True,\n        \"processed_at\": datetime.now().isoformat()\n    }\n}]\n```\n\n---\n\n## Python Modes: Beta vs Native\n\nn8n offers two Python execution modes:\n\n### Python (Beta) - Recommended\n- **Use**: `_input`, `_json`, `_node` helper syntax\n- **Best for**: Most Python use cases\n- **Helpers available**: `_now`, `_today`, `_jmespath()`\n- **Import**: `from datetime import datetime`\n\n```python\n# Python (Beta) example\nitems = _input.all()\nnow = _now  # Built-in datetime object\n\nreturn [{\n    \"json\": {\n        \"count\": len(items),\n        \"timestamp\": now.isoformat()\n    }\n}]\n```\n\n### Python (Native) (Beta)\n- **Use**: `_items`, `_item` variables only\n- **No helpers**: No `_input`, `_now`, etc.\n- **More limited**: Standard Python only\n- **Use when**: Need pure Python without n8n helpers\n\n```python\n# Python (Native) example\nprocessed = []\n\nfor item in _items:\n    processed.append({\n        \"json\": {\n            \"id\": item[\"json\"].get(\"id\"),\n            \"processed\": True\n        }\n    })\n\nreturn processed\n```\n\n**Recommendation**: Use **Python (Beta)** for better n8n integration.\n\n---\n\n## Data Access Patterns\n\n### Pattern 1: _input.all() - Most Common\n\n**Use when**: Processing arrays, batch operations, aggregations\n\n```python\n# Get all items from previous node\nall_items = _input.all()\n\n# Filter, transform as needed\nvalid = [item for item in all_items if item[\"json\"].get(\"status\") == \"active\"]\n\nprocessed = []\nfor item in valid:\n    processed.append({\n        \"json\": {\n            \"id\": item[\"json\"][\"id\"],\n            \"name\": item[\"json\"][\"name\"]\n        }\n    })\n\nreturn processed\n```\n\n### Pattern 2: _input.first() - Very Common\n\n**Use when**: Working with single objects, API responses\n\n```python\n# Get first item only\nfirst_item = _input.first()\ndata = first_item[\"json\"]\n\nreturn [{\n    \"json\": {\n        \"result\": process_data(data),\n        \"processed_at\": datetime.now().isoformat()\n    }\n}]\n```\n\n### Pattern 3: _input.item - Each Item Mode Only\n\n**Use when**: In \"Run Once for Each Item\" mode\n\n```python\n# Current item in loop (Each Item mode only)\ncurrent_item = _input.item\n\nreturn [{\n    \"json\": {\n        **current_item[\"json\"],\n        \"item_processed\": True\n    }\n}]\n```\n\n### Pattern 4: _node - Reference Other Nodes\n\n**Use when**: Need data from specific nodes in workflow\n\n```python\n# Get output from specific node\nwebhook_data = _node[\"Webhook\"][\"json\"]\nhttp_data = _node[\"HTTP Request\"][\"json\"]\n\nreturn [{\n    \"json\": {\n        \"combined\": {\n            \"webhook\": webhook_data,\n            \"api\": http_data\n        }\n    }\n}]\n```\n\n**See**: [DATA_ACCESS.md](DATA_ACCESS.md) for comprehensive guide\n\n---\n\n## Critical: Webhook Data Structure\n\n**MOST COMMON MISTAKE**: Webhook data is nested under `[\"body\"]`\n\n```python\n# ❌ WRONG - Will raise KeyError\nname = _json[\"name\"]\nemail = _json[\"email\"]\n\n# ✅ CORRECT - Webhook data is under [\"body\"]\nname = _json[\"body\"][\"name\"]\nemail = _json[\"body\"][\"email\"]\n\n# ✅ SAFER - Use .get() for safe access\nwebhook_data = _json.get(\"body\", {})\nname = webhook_data.get(\"name\")\n```\n\n**Why**: Webhook node wraps all request data under `body` property. This includes POST data, query parameters, and JSON payloads.\n\n**See**: [DATA_ACCESS.md](DATA_ACCESS.md) for full webhook structure details\n\n---\n\n## Return Format Requirements\n\n**CRITICAL RULE**: Always return list of dictionaries with `\"json\"` key\n\n### Correct Return Formats\n\n```python\n# ✅ Single result\nreturn [{\n    \"json\": {\n        \"field1\": value1,\n        \"field2\": value2\n    }\n}]\n\n# ✅ Multiple results\nreturn [\n    {\"json\": {\"id\": 1, \"data\": \"first\"}},\n    {\"json\": {\"id\": 2, \"data\": \"second\"}}\n]\n\n# ✅ List comprehension\ntransformed = [\n    {\"json\": {\"id\": item[\"json\"][\"id\"], \"processed\": True}}\n    for item in _input.all()\n    if item[\"json\"].get(\"valid\")\n]\nreturn transformed\n\n# ✅ Empty result (when no data to return)\nreturn []\n\n# ✅ Conditional return\nif should_process:\n    return [{\"json\": processed_data}]\nelse:\n    return []\n```\n\n### Incorrect Return Formats\n\n```python\n# ❌ WRONG: Dictionary without list wrapper\nreturn {\n    \"json\": {\"field\": value}\n}\n\n# ❌ WRONG: List without json wrapper\nreturn [{\"field\": value}]\n\n# ❌ WRONG: Plain string\nreturn \"processed\"\n\n# ❌ WRONG: Incomplete structure\nreturn [{\"data\": value}]  # Should be {\"json\": value}\n```\n\n**Why it matters**: Next nodes expect list format. Incorrect format causes workflow execution to fail.\n\n**See**: [ERROR_PATTERNS.md](ERROR_PATTERNS.md) #2 for detailed error solutions\n\n---\n\n## Critical Limitation: No External Libraries\n\n**MOST IMPORTANT PYTHON LIMITATION**: Cannot import external packages\n\n### What's NOT Available\n\n```python\n# ❌ NOT AVAILABLE - Will raise ModuleNotFoundError\nimport requests  # ❌ No\nimport pandas  # ❌ No\nimport numpy  # ❌ No\nimport scipy  # ❌ No\nfrom bs4 import BeautifulSoup  # ❌ No\nimport lxml  # ❌ No\n```\n\n### What IS Available (Standard Library)\n\n```python\n# ✅ AVAILABLE - Standard library only\nimport json  # ✅ JSON parsing\nimport datetime  # ✅ Date\u002Ftime operations\nimport re  # ✅ Regular expressions\nimport base64  # ✅ Base64 encoding\u002Fdecoding\nimport hashlib  # ✅ Hashing functions\nimport urllib.parse  # ✅ URL parsing\nimport math  # ✅ Math functions\nimport random  # ✅ Random numbers\nimport statistics  # ✅ Statistical functions\n```\n\n### Workarounds\n\n**Need HTTP requests?**\n- ✅ Use **HTTP Request node** before Code node\n- ✅ Or switch to **JavaScript** and use `$helpers.httpRequest()`\n\n**Need data analysis (pandas\u002Fnumpy)?**\n- ✅ Use Python **statistics** module for basic stats\n- ✅ Or switch to **JavaScript** for most operations\n- ✅ Manual calculations with lists and dictionaries\n\n**Need web scraping (BeautifulSoup)?**\n- ✅ Use **HTTP Request node** + **HTML Extract node**\n- ✅ Or switch to **JavaScript** with regex\u002Fstring methods\n\n**See**: [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) for complete reference\n\n---\n\n## Common Patterns Overview\n\nBased on production workflows, here are the most useful Python patterns:\n\n### 1. Data Transformation\nTransform all items with list comprehensions\n\n```python\nitems = _input.all()\n\nreturn [\n    {\n        \"json\": {\n            \"id\": item[\"json\"].get(\"id\"),\n            \"name\": item[\"json\"].get(\"name\", \"Unknown\").upper(),\n            \"processed\": True\n        }\n    }\n    for item in items\n]\n```\n\n### 2. Filtering & Aggregation\nSum, filter, count with built-in functions\n\n```python\nitems = _input.all()\ntotal = sum(item[\"json\"].get(\"amount\", 0) for item in items)\nvalid_items = [item for item in items if item[\"json\"].get(\"amount\", 0) > 0]\n\nreturn [{\n    \"json\": {\n        \"total\": total,\n        \"count\": len(valid_items)\n    }\n}]\n```\n\n### 3. String Processing with Regex\nExtract patterns from text\n\n```python\nimport re\n\nitems = _input.all()\nemail_pattern = r'\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b'\n\nall_emails = []\nfor item in items:\n    text = item[\"json\"].get(\"text\", \"\")\n    emails = re.findall(email_pattern, text)\n    all_emails.extend(emails)\n\n# Remove duplicates\nunique_emails = list(set(all_emails))\n\nreturn [{\n    \"json\": {\n        \"emails\": unique_emails,\n        \"count\": len(unique_emails)\n    }\n}]\n```\n\n### 4. Data Validation\nValidate and clean data\n\n```python\nitems = _input.all()\nvalidated = []\n\nfor item in items:\n    data = item[\"json\"]\n    errors = []\n\n    # Validate fields\n    if not data.get(\"email\"):\n        errors.append(\"Email required\")\n    if not data.get(\"name\"):\n        errors.append(\"Name required\")\n\n    validated.append({\n        \"json\": {\n            **data,\n            \"valid\": len(errors) == 0,\n            \"errors\": errors if errors else None\n        }\n    })\n\nreturn validated\n```\n\n### 5. Statistical Analysis\nCalculate statistics with statistics module\n\n```python\nfrom statistics import mean, median, stdev\n\nitems = _input.all()\nvalues = [item[\"json\"].get(\"value\", 0) for item in items if \"value\" in item[\"json\"]]\n\nif values:\n    return [{\n        \"json\": {\n            \"mean\": mean(values),\n            \"median\": median(values),\n            \"stdev\": stdev(values) if len(values) > 1 else 0,\n            \"min\": min(values),\n            \"max\": max(values),\n            \"count\": len(values)\n        }\n    }]\nelse:\n    return [{\"json\": {\"error\": \"No values found\"}}]\n```\n\n**See**: [COMMON_PATTERNS.md](COMMON_PATTERNS.md) for 10 detailed Python patterns\n\n---\n\n## Error Prevention - Top 5 Mistakes\n\n### #1: Importing External Libraries (Python-Specific!)\n\n```python\n# ❌ WRONG: Trying to import external library\nimport requests  # ModuleNotFoundError!\n\n# ✅ CORRECT: Use HTTP Request node or JavaScript\n# Add HTTP Request node before Code node\n# OR switch to JavaScript and use $helpers.httpRequest()\n```\n\n### #2: Empty Code or Missing Return\n\n```python\n# ❌ WRONG: No return statement\nitems = _input.all()\n# Processing...\n# Forgot to return!\n\n# ✅ CORRECT: Always return data\nitems = _input.all()\n# Processing...\nreturn [{\"json\": item[\"json\"]} for item in items]\n```\n\n### #3: Incorrect Return Format\n\n```python\n# ❌ WRONG: Returning dict instead of list\nreturn {\"json\": {\"result\": \"success\"}}\n\n# ✅ CORRECT: List wrapper required\nreturn [{\"json\": {\"result\": \"success\"}}]\n```\n\n### #4: KeyError on Dictionary Access\n\n```python\n# ❌ WRONG: Direct access crashes if missing\nname = _json[\"user\"][\"name\"]  # KeyError!\n\n# ✅ CORRECT: Use .get() for safe access\nname = _json.get(\"user\", {}).get(\"name\", \"Unknown\")\n```\n\n### #5: Webhook Body Nesting\n\n```python\n# ❌ WRONG: Direct access to webhook data\nemail = _json[\"email\"]  # KeyError!\n\n# ✅ CORRECT: Webhook data under [\"body\"]\nemail = _json[\"body\"][\"email\"]\n\n# ✅ BETTER: Safe access with .get()\nemail = _json.get(\"body\", {}).get(\"email\", \"no-email\")\n```\n\n**See**: [ERROR_PATTERNS.md](ERROR_PATTERNS.md) for comprehensive error guide\n\n---\n\n## Standard Library Reference\n\n### Most Useful Modules\n\n```python\n# JSON operations\nimport json\ndata = json.loads(json_string)\njson_output = json.dumps({\"key\": \"value\"})\n\n# Date\u002Ftime\nfrom datetime import datetime, timedelta\nnow = datetime.now()\ntomorrow = now + timedelta(days=1)\nformatted = now.strftime(\"%Y-%m-%d\")\n\n# Regular expressions\nimport re\nmatches = re.findall(r'\\d+', text)\ncleaned = re.sub(r'[^\\w\\s]', '', text)\n\n# Base64 encoding\nimport base64\nencoded = base64.b64encode(data).decode()\ndecoded = base64.b64decode(encoded)\n\n# Hashing\nimport hashlib\nhash_value = hashlib.sha256(text.encode()).hexdigest()\n\n# URL parsing\nimport urllib.parse\nparams = urllib.parse.urlencode({\"key\": \"value\"})\nparsed = urllib.parse.urlparse(url)\n\n# Statistics\nfrom statistics import mean, median, stdev\naverage = mean([1, 2, 3, 4, 5])\n```\n\n**See**: [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) for complete reference\n\n---\n\n## Best Practices\n\n### 1. Always Use .get() for Dictionary Access\n\n```python\n# ✅ SAFE: Won't crash if field missing\nvalue = item[\"json\"].get(\"field\", \"default\")\n\n# ❌ RISKY: Crashes if field doesn't exist\nvalue = item[\"json\"][\"field\"]\n```\n\n### 2. Handle None\u002FNull Values Explicitly\n\n```python\n# ✅ GOOD: Default to 0 if None\namount = item[\"json\"].get(\"amount\") or 0\n\n# ✅ GOOD: Check for None explicitly\ntext = item[\"json\"].get(\"text\")\nif text is None:\n    text = \"\"\n```\n\n### 3. Use List Comprehensions for Filtering\n\n```python\n# ✅ PYTHONIC: List comprehension\nvalid = [item for item in items if item[\"json\"].get(\"active\")]\n\n# ❌ VERBOSE: Manual loop\nvalid = []\nfor item in items:\n    if item[\"json\"].get(\"active\"):\n        valid.append(item)\n```\n\n### 4. Return Consistent Structure\n\n```python\n# ✅ CONSISTENT: Always list with \"json\" key\nreturn [{\"json\": result}]  # Single result\nreturn results  # Multiple results (already formatted)\nreturn []  # No results\n```\n\n### 5. Debug with print() Statements\n\n```python\n# Debug statements appear in browser console (F12)\nitems = _input.all()\nprint(f\"Processing {len(items)} items\")\nprint(f\"First item: {items[0] if items else 'None'}\")\n```\n\n---\n\n## When to Use Python vs JavaScript\n\n### Use Python When:\n- ✅ You need `statistics` module for statistical operations\n- ✅ You're significantly more comfortable with Python syntax\n- ✅ Your logic maps well to list comprehensions\n- ✅ You need specific standard library functions\n\n### Use JavaScript When:\n- ✅ You need HTTP requests ($helpers.httpRequest())\n- ✅ You need advanced date\u002Ftime (DateTime\u002FLuxon)\n- ✅ You want better n8n integration\n- ✅ **For 95% of use cases** (recommended)\n\n### Consider Other Nodes When:\n- ❌ Simple field mapping → Use **Set** node\n- ❌ Basic filtering → Use **Filter** node\n- ❌ Simple conditionals → Use **IF** or **Switch** node\n- ❌ HTTP requests only → Use **HTTP Request** node\n\n---\n\n## Integration with Other Skills\n\n### Works With:\n\n**n8n Expression Syntax**:\n- Expressions use `{{ }}` syntax in other nodes\n- Code nodes use Python directly (no `{{ }}`)\n- When to use expressions vs code\n\n**n8n MCP Tools Expert**:\n- How to find Code node: `search_nodes({query: \"code\"})`\n- Get configuration help: `get_node_essentials(\"nodes-base.code\")`\n- Validate code: `validate_node_operation()`\n\n**n8n Node Configuration**:\n- Mode selection (All Items vs Each Item)\n- Language selection (Python vs JavaScript)\n- Understanding property dependencies\n\n**n8n Workflow Patterns**:\n- Code nodes in transformation step\n- When to use Python vs JavaScript in patterns\n\n**n8n Validation Expert**:\n- Validate Code node configuration\n- Handle validation errors\n- Auto-fix common issues\n\n**n8n Code JavaScript**:\n- When to use JavaScript instead\n- Comparison of JavaScript vs Python features\n- Migration from Python to JavaScript\n\n---\n\n## Quick Reference Checklist\n\nBefore deploying Python Code nodes, verify:\n\n- [ ] **Considered JavaScript first** - Using Python only when necessary\n- [ ] **Code is not empty** - Must have meaningful logic\n- [ ] **Return statement exists** - Must return list of dictionaries\n- [ ] **Proper return format** - Each item: `{\"json\": {...}}`\n- [ ] **Data access correct** - Using `_input.all()`, `_input.first()`, or `_input.item`\n- [ ] **No external imports** - Only standard library (json, datetime, re, etc.)\n- [ ] **Safe dictionary access** - Using `.get()` to avoid KeyError\n- [ ] **Webhook data** - Access via `[\"body\"]` if from webhook\n- [ ] **Mode selection** - \"All Items\" for most cases\n- [ ] **Output consistent** - All code paths return same structure\n\n---\n\n## Additional Resources\n\n### Related Files\n- [DATA_ACCESS.md](DATA_ACCESS.md) - Comprehensive Python data access patterns\n- [COMMON_PATTERNS.md](COMMON_PATTERNS.md) - 10 Python patterns for n8n\n- [ERROR_PATTERNS.md](ERROR_PATTERNS.md) - Top 5 errors and solutions\n- [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) - Complete standard library reference\n\n### n8n Documentation\n- Code Node Guide: https:\u002F\u002Fdocs.n8n.io\u002Fcode\u002Fcode-node\u002F\n- Python in n8n: https:\u002F\u002Fdocs.n8n.io\u002Fcode\u002Fbuiltin\u002Fpython-modules\u002F\n\n---\n\n**Ready to write Python in n8n Code nodes - but consider JavaScript first!** Use Python for specific needs, reference the error patterns guide to avoid common mistakes, and leverage the standard library effectively.\n","# Python Code Node (Beta)\n\nExpert guidance for writing Python code in n8n Code nodes.\n\n---\n\n## ⚠️ Important: JavaScript First\n\n**Recommendation**: Use **JavaScript for 95% of use cases**. Only use Python when:\n- You need specific Python standard library functions\n- You're significantly more comfortable with Python syntax\n- You're doing data transformations better suited to Python\n\n**Why JavaScript is preferred:**\n- Full n8n helper functions ($helpers.httpRequest, etc.)\n- Luxon DateTime library for advanced date\u002Ftime operations\n- No external library limitations\n- Better n8n documentation and community support\n\n---\n\n## Quick Start\n\n```python\n# Basic template for Python Code nodes\nitems = _input.all()\n\n# Process data\nprocessed = []\nfor item in items:\n    processed.append({\n        \"json\": {\n            **item[\"json\"],\n            \"processed\": True,\n            \"timestamp\": datetime.now().isoformat()\n        }\n    })\n\nreturn processed\n```\n\n### Essential Rules\n\n1. **Consider JavaScript first** - Use Python only when necessary\n2. **Access data**: `_input.all()`, `_input.first()`, or `_input.item`\n3. **CRITICAL**: Must return `[{\"json\": {...}}]` format\n4. **CRITICAL**: Webhook data is under `_json[\"body\"]` (not `_json` directly)\n5. **CRITICAL LIMITATION**: **No external libraries** (no requests, pandas, numpy)\n6. **Standard library only**: json, datetime, re, base64, hashlib, urllib.parse, math, random, statistics\n\n---\n\n## Mode Selection Guide\n\nSame as JavaScript - choose based on your use case:\n\n### Run Once for All Items (Recommended - Default)\n\n**Use this mode for:** 95% of use cases\n\n- **How it works**: Code executes **once** regardless of input count\n- **Data access**: `_input.all()` or `_items` array (Native mode)\n- **Best for**: Aggregation, filtering, batch processing, transformations\n- **Performance**: Faster for multiple items (single execution)\n\n```python\n# Example: Calculate total from all items\nall_items = _input.all()\ntotal = sum(item[\"json\"].get(\"amount\", 0) for item in all_items)\n\nreturn [{\n    \"json\": {\n        \"total\": total,\n        \"count\": len(all_items),\n        \"average\": total \u002F len(all_items) if all_items else 0\n    }\n}]\n```\n\n### Run Once for Each Item\n\n**Use this mode for:** Specialized cases only\n\n- **How it works**: Code executes **separately** for each input item\n- **Data access**: `_input.item` or `_item` (Native mode)\n- **Best for**: Item-specific logic, independent operations, per-item validation\n- **Performance**: Slower for large datasets (multiple executions)\n\n```python\n# Example: Add processing timestamp to each item\nitem = _input.item\n\nreturn [{\n    \"json\": {\n        **item[\"json\"],\n        \"processed\": True,\n        \"processed_at\": datetime.now().isoformat()\n    }\n}]\n```\n\n---\n\n## Python Modes: Beta vs Native\n\nn8n offers two Python execution modes:\n\n### Python (Beta) - Recommended\n- **Use**: `_input`, `_json`, `_node` helper syntax\n- **Best for**: Most Python use cases\n- **Helpers available**: `_now`, `_today`, `_jmespath()`\n- **Import**: `from datetime import datetime`\n\n```python\n# Python (Beta) example\nitems = _input.all()\nnow = _now  # Built-in datetime object\n\nreturn [{\n    \"json\": {\n        \"count\": len(items),\n        \"timestamp\": now.isoformat()\n    }\n}]\n```\n\n### Python (Native) (Beta)\n- **Use**: `_items`, `_item` variables only\n- **No helpers**: No `_input`, `_now`, etc.\n- **More limited**: Standard Python only\n- **Use when**: Need pure Python without n8n helpers\n\n```python\n# Python (Native) example\nprocessed = []\n\nfor item in _items:\n    processed.append({\n        \"json\": {\n            \"id\": item[\"json\"].get(\"id\"),\n            \"processed\": True\n        }\n    })\n\nreturn processed\n```\n\n**Recommendation**: Use **Python (Beta)** for better n8n integration.\n\n---\n\n## Data Access Patterns\n\n### Pattern 1: _input.all() - Most Common\n\n**Use when**: Processing arrays, batch operations, aggregations\n\n```python\n# Get all items from previous node\nall_items = _input.all()\n\n# Filter, transform as needed\nvalid = [item for item in all_items if item[\"json\"].get(\"status\") == \"active\"]\n\nprocessed = []\nfor item in valid:\n    processed.append({\n        \"json\": {\n            \"id\": item[\"json\"][\"id\"],\n            \"name\": item[\"json\"][\"name\"]\n        }\n    })\n\nreturn processed\n```\n\n### Pattern 2: _input.first() - Very Common\n\n**Use when**: Working with single objects, API responses\n\n```python\n# Get first item only\nfirst_item = _input.first()\ndata = first_item[\"json\"]\n\nreturn [{\n    \"json\": {\n        \"result\": process_data(data),\n        \"processed_at\": datetime.now().isoformat()\n    }\n}]\n```\n\n### Pattern 3: _input.item - Each Item Mode Only\n\n**Use when**: In \"Run Once for Each Item\" mode\n\n```python\n# Current item in loop (Each Item mode only)\ncurrent_item = _input.item\n\nreturn [{\n    \"json\": {\n        **current_item[\"json\"],\n        \"item_processed\": True\n    }\n}]\n```\n\n### Pattern 4: _node - Reference Other Nodes\n\n**Use when**: Need data from specific nodes in workflow\n\n```python\n# Get output from specific node\nwebhook_data = _node[\"Webhook\"][\"json\"]\nhttp_data = _node[\"HTTP Request\"][\"json\"]\n\nreturn [{\n    \"json\": {\n        \"combined\": {\n            \"webhook\": webhook_data,\n            \"api\": http_data\n        }\n    }\n}]\n```\n\n**See**: [DATA_ACCESS.md](DATA_ACCESS.md) for comprehensive guide\n\n---\n\n## Critical: Webhook Data Structure\n\n**MOST COMMON MISTAKE**: Webhook data is nested under `[\"body\"]`\n\n```python\n# ❌ WRONG - Will raise KeyError\nname = _json[\"name\"]\nemail = _json[\"email\"]\n\n# ✅ CORRECT - Webhook data is under [\"body\"]\nname = _json[\"body\"][\"name\"]\nemail = _json[\"body\"][\"email\"]\n\n# ✅ SAFER - Use .get() for safe access\nwebhook_data = _json.get(\"body\", {})\nname = webhook_data.get(\"name\")\n```\n\n**Why**: Webhook node wraps all request data under `body` property. This includes POST data, query parameters, and JSON payloads.\n\n**See**: [DATA_ACCESS.md](DATA_ACCESS.md) for full webhook structure details\n\n---\n\n## Return Format Requirements\n\n**CRITICAL RULE**: Always return list of dictionaries with `\"json\"` key\n\n### Correct Return Formats\n\n```python\n# ✅ Single result\nreturn [{\n    \"json\": {\n        \"field1\": value1,\n        \"field2\": value2\n    }\n}]\n\n# ✅ Multiple results\nreturn [\n    {\"json\": {\"id\": 1, \"data\": \"first\"}},\n    {\"json\": {\"id\": 2, \"data\": \"second\"}}\n]\n\n# ✅ List comprehension\ntransformed = [\n    {\"json\": {\"id\": item[\"json\"][\"id\"], \"processed\": True}}\n    for item in _input.all()\n    if item[\"json\"].get(\"valid\")\n]\nreturn transformed\n\n# ✅ Empty result (when no data to return)\nreturn []\n\n# ✅ Conditional return\nif should_process:\n    return [{\"json\": processed_data}]\nelse:\n    return []\n```\n\n### Incorrect Return Formats\n\n```python\n# ❌ WRONG: Dictionary without list wrapper\nreturn {\n    \"json\": {\"field\": value}\n}\n\n# ❌ WRONG: List without json wrapper\nreturn [{\"field\": value}]\n\n# ❌ WRONG: Plain string\nreturn \"processed\"\n\n# ❌ WRONG: Incomplete structure\nreturn [{\"data\": value}]  # Should be {\"json\": value}\n```\n\n**Why it matters**: Next nodes expect list format. Incorrect format causes workflow execution to fail.\n\n**See**: [ERROR_PATTERNS.md](ERROR_PATTERNS.md) #2 for detailed error solutions\n\n---\n\n## Critical Limitation: No External Libraries\n\n**MOST IMPORTANT PYTHON LIMITATION**: Cannot import external packages\n\n### What's NOT Available\n\n```python\n# ❌ NOT AVAILABLE - Will raise ModuleNotFoundError\nimport requests  # ❌ No\nimport pandas  # ❌ No\nimport numpy  # ❌ No\nimport scipy  # ❌ No\nfrom bs4 import BeautifulSoup  # ❌ No\nimport lxml  # ❌ No\n```\n\n### What IS Available (Standard Library)\n\n```python\n# ✅ AVAILABLE - Standard library only\nimport json  # ✅ JSON parsing\nimport datetime  # ✅ Date\u002Ftime operations\nimport re  # ✅ Regular expressions\nimport base64  # ✅ Base64 encoding\u002Fdecoding\nimport hashlib  # ✅ Hashing functions\nimport urllib.parse  # ✅ URL parsing\nimport math  # ✅ Math functions\nimport random  # ✅ Random numbers\nimport statistics  # ✅ Statistical functions\n```\n\n### Workarounds\n\n**Need HTTP requests?**\n- ✅ Use **HTTP Request node** before Code node\n- ✅ Or switch to **JavaScript** and use `$helpers.httpRequest()`\n\n**Need data analysis (pandas\u002Fnumpy)?**\n- ✅ Use Python **statistics** module for basic stats\n- ✅ Or switch to **JavaScript** for most operations\n- ✅ Manual calculations with lists and dictionaries\n\n**Need web scraping (BeautifulSoup)?**\n- ✅ Use **HTTP Request node** + **HTML Extract node**\n- ✅ Or switch to **JavaScript** with regex\u002Fstring methods\n\n**See**: [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) for complete reference\n\n---\n\n## Common Patterns Overview\n\nBased on production workflows, here are the most useful Python patterns:\n\n### 1. Data Transformation\nTransform all items with list comprehensions\n\n```python\nitems = _input.all()\n\nreturn [\n    {\n        \"json\": {\n            \"id\": item[\"json\"].get(\"id\"),\n            \"name\": item[\"json\"].get(\"name\", \"Unknown\").upper(),\n            \"processed\": True\n        }\n    }\n    for item in items\n]\n```\n\n### 2. Filtering & Aggregation\nSum, filter, count with built-in functions\n\n```python\nitems = _input.all()\ntotal = sum(item[\"json\"].get(\"amount\", 0) for item in items)\nvalid_items = [item for item in items if item[\"json\"].get(\"amount\", 0) > 0]\n\nreturn [{\n    \"json\": {\n        \"total\": total,\n        \"count\": len(valid_items)\n    }\n}]\n```\n\n### 3. String Processing with Regex\nExtract patterns from text\n\n```python\nimport re\n\nitems = _input.all()\nemail_pattern = r'\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b'\n\nall_emails = []\nfor item in items:\n    text = item[\"json\"].get(\"text\", \"\")\n    emails = re.findall(email_pattern, text)\n    all_emails.extend(emails)\n\n# Remove duplicates\nunique_emails = list(set(all_emails))\n\nreturn [{\n    \"json\": {\n        \"emails\": unique_emails,\n        \"count\": len(unique_emails)\n    }\n}]\n```\n\n### 4. Data Validation\nValidate and clean data\n\n```python\nitems = _input.all()\nvalidated = []\n\nfor item in items:\n    data = item[\"json\"]\n    errors = []\n\n    # Validate fields\n    if not data.get(\"email\"):\n        errors.append(\"Email required\")\n    if not data.get(\"name\"):\n        errors.append(\"Name required\")\n\n    validated.append({\n        \"json\": {\n            **data,\n            \"valid\": len(errors) == 0,\n            \"errors\": errors if errors else None\n        }\n    })\n\nreturn validated\n```\n\n### 5. Statistical Analysis\nCalculate statistics with statistics module\n\n```python\nfrom statistics import mean, median, stdev\n\nitems = _input.all()\nvalues = [item[\"json\"].get(\"value\", 0) for item in items if \"value\" in item[\"json\"]]\n\nif values:\n    return [{\n        \"json\": {\n            \"mean\": mean(values),\n            \"median\": median(values),\n            \"stdev\": stdev(values) if len(values) > 1 else 0,\n            \"min\": min(values),\n            \"max\": max(values),\n            \"count\": len(values)\n        }\n    }]\nelse:\n    return [{\"json\": {\"error\": \"No values found\"}}]\n```\n\n**See**: [COMMON_PATTERNS.md](COMMON_PATTERNS.md) for 10 detailed Python patterns\n\n---\n\n## Error Prevention - Top 5 Mistakes\n\n### #1: Importing External Libraries (Python-Specific!)\n\n```python\n# ❌ WRONG: Trying to import external library\nimport requests  # ModuleNotFoundError!\n\n# ✅ CORRECT: Use HTTP Request node or JavaScript\n# Add HTTP Request node before Code node\n# OR switch to JavaScript and use $helpers.httpRequest()\n```\n\n### #2: Empty Code or Missing Return\n\n```python\n# ❌ WRONG: No return statement\nitems = _input.all()\n# Processing...\n# Forgot to return!\n\n# ✅ CORRECT: Always return data\nitems = _input.all()\n# Processing...\nreturn [{\"json\": item[\"json\"]} for item in items]\n```\n\n### #3: Incorrect Return Format\n\n```python\n# ❌ WRONG: Returning dict instead of list\nreturn {\"json\": {\"result\": \"success\"}}\n\n# ✅ CORRECT: List wrapper required\nreturn [{\"json\": {\"result\": \"success\"}}]\n```\n\n### #4: KeyError on Dictionary Access\n\n```python\n# ❌ WRONG: Direct access crashes if missing\nname = _json[\"user\"][\"name\"]  # KeyError!\n\n# ✅ CORRECT: Use .get() for safe access\nname = _json.get(\"user\", {}).get(\"name\", \"Unknown\")\n```\n\n### #5: Webhook Body Nesting\n\n```python\n# ❌ WRONG: Direct access to webhook data\nemail = _json[\"email\"]  # KeyError!\n\n# ✅ CORRECT: Webhook data under [\"body\"]\nemail = _json[\"body\"][\"email\"]\n\n# ✅ BETTER: Safe access with .get()\nemail = _json.get(\"body\", {}).get(\"email\", \"no-email\")\n```\n\n**See**: [ERROR_PATTERNS.md](ERROR_PATTERNS.md) for comprehensive error guide\n\n---\n\n## Standard Library Reference\n\n### Most Useful Modules\n\n```python\n# JSON operations\nimport json\ndata = json.loads(json_string)\njson_output = json.dumps({\"key\": \"value\"})\n\n# Date\u002Ftime\nfrom datetime import datetime, timedelta\nnow = datetime.now()\ntomorrow = now + timedelta(days=1)\nformatted = now.strftime(\"%Y-%m-%d\")\n\n# Regular expressions\nimport re\nmatches = re.findall(r'\\d+', text)\ncleaned = re.sub(r'[^\\w\\s]', '', text)\n\n# Base64 encoding\nimport base64\nencoded = base64.b64encode(data).decode()\ndecoded = base64.b64decode(encoded)\n\n# Hashing\nimport hashlib\nhash_value = hashlib.sha256(text.encode()).hexdigest()\n\n# URL parsing\nimport urllib.parse\nparams = urllib.parse.urlencode({\"key\": \"value\"})\nparsed = urllib.parse.urlparse(url)\n\n# Statistics\nfrom statistics import mean, median, stdev\naverage = mean([1, 2, 3, 4, 5])\n```\n\n**See**: [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) for complete reference\n\n---\n\n## Best Practices\n\n### 1. Always Use .get() for Dictionary Access\n\n```python\n# ✅ SAFE: Won't crash if field missing\nvalue = item[\"json\"].get(\"field\", \"default\")\n\n# ❌ RISKY: Crashes if field doesn't exist\nvalue = item[\"json\"][\"field\"]\n```\n\n### 2. Handle None\u002FNull Values Explicitly\n\n```python\n# ✅ GOOD: Default to 0 if None\namount = item[\"json\"].get(\"amount\") or 0\n\n# ✅ GOOD: Check for None explicitly\ntext = item[\"json\"].get(\"text\")\nif text is None:\n    text = \"\"\n```\n\n### 3. Use List Comprehensions for Filtering\n\n```python\n# ✅ PYTHONIC: List comprehension\nvalid = [item for item in items if item[\"json\"].get(\"active\")]\n\n# ❌ VERBOSE: Manual loop\nvalid = []\nfor item in items:\n    if item[\"json\"].get(\"active\"):\n        valid.append(item)\n```\n\n### 4. Return Consistent Structure\n\n```python\n# ✅ CONSISTENT: Always list with \"json\" key\nreturn [{\"json\": result}]  # Single result\nreturn results  # Multiple results (already formatted)\nreturn []  # No results\n```\n\n### 5. Debug with print() Statements\n\n```python\n# Debug statements appear in browser console (F12)\nitems = _input.all()\nprint(f\"Processing {len(items)} items\")\nprint(f\"First item: {items[0] if items else 'None'}\")\n```\n\n---\n\n## When to Use Python vs JavaScript\n\n### Use Python When:\n- ✅ You need `statistics` module for statistical operations\n- ✅ You're significantly more comfortable with Python syntax\n- ✅ Your logic maps well to list comprehensions\n- ✅ You need specific standard library functions\n\n### Use JavaScript When:\n- ✅ You need HTTP requests ($helpers.httpRequest())\n- ✅ You need advanced date\u002Ftime (DateTime\u002FLuxon)\n- ✅ You want better n8n integration\n- ✅ **For 95% of use cases** (recommended)\n\n### Consider Other Nodes When:\n- ❌ Simple field mapping → Use **Set** node\n- ❌ Basic filtering → Use **Filter** node\n- ❌ Simple conditionals → Use **IF** or **Switch** node\n- ❌ HTTP requests only → Use **HTTP Request** node\n\n---\n\n## Integration with Other Skills\n\n### Works With:\n\n**n8n Expression Syntax**:\n- Expressions use `{{ }}` syntax in other nodes\n- Code nodes use Python directly (no `{{ }}`)\n- When to use expressions vs code\n\n**n8n MCP Tools Expert**:\n- How to find Code node: `search_nodes({query: \"code\"})`\n- Get configuration help: `get_node_essentials(\"nodes-base.code\")`\n- Validate code: `validate_node_operation()`\n\n**n8n Node Configuration**:\n- Mode selection (All Items vs Each Item)\n- Language selection (Python vs JavaScript)\n- Understanding property dependencies\n\n**n8n Workflow Patterns**:\n- Code nodes in transformation step\n- When to use Python vs JavaScript in patterns\n\n**n8n Validation Expert**:\n- Validate Code node configuration\n- Handle validation errors\n- Auto-fix common issues\n\n**n8n Code JavaScript**:\n- When to use JavaScript instead\n- Comparison of JavaScript vs Python features\n- Migration from Python to JavaScript\n\n---\n\n## Quick Reference Checklist\n\nBefore deploying Python Code nodes, verify:\n\n- [ ] **Considered JavaScript first** - Using Python only when necessary\n- [ ] **Code is not empty** - Must have meaningful logic\n- [ ] **Return statement exists** - Must return list of dictionaries\n- [ ] **Proper return format** - Each item: `{\"json\": {...}}`\n- [ ] **Data access correct** - Using `_input.all()`, `_input.first()`, or `_input.item`\n- [ ] **No external imports** - Only standard library (json, datetime, re, etc.)\n- [ ] **Safe dictionary access** - Using `.get()` to avoid KeyError\n- [ ] **Webhook data** - Access via `[\"body\"]` if from webhook\n- [ ] **Mode selection** - \"All Items\" for most cases\n- [ ] **Output consistent** - All code paths return same structure\n\n---\n\n## Additional Resources\n\n### Related Files\n- [DATA_ACCESS.md](DATA_ACCESS.md) - Comprehensive Python data access patterns\n- [COMMON_PATTERNS.md](COMMON_PATTERNS.md) - 10 Python patterns for n8n\n- [ERROR_PATTERNS.md](ERROR_PATTERNS.md) - Top 5 errors and solutions\n- [STANDARD_LIBRARY.md](STANDARD_LIBRARY.md) - Complete standard library reference\n\n### n8n Documentation\n- Code Node Guide: https:\u002F\u002Fdocs.n8n.io\u002Fcode\u002Fcode-node\u002F\n- Python in n8n: https:\u002F\u002Fdocs.n8n.io\u002Fcode\u002Fbuiltin\u002Fpython-modules\u002F\n\n---\n\n**Ready to write Python in n8n Code nodes - 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Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[147],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[107,108,149,150,151,152,153,154,155,156,157,158,27,159,160,161,162,30,118,119],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","payment","worker","scripts","temporal",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":169,"publishedAt":41},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[170,171,172,173,174,152,175,153,154,176,35,177,178,179,180,181,182,30,118,119],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":187,"categories":188,"keywords":192,"publishedAt":41},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[189],{"categoryNo":190,"categoryName":191,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000021","资料检索与研究",[193,194,195,30,196,107,197,198,199,200,201,202,35,203,204,205,206,207,208,118],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":213,"categories":214,"keywords":219,"publishedAt":41},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[215,218],{"categoryNo":216,"categoryName":217,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[107,220,27,221,104,222,223,152,224,198,194,225,226,227,228,229,230,231,232,233],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","database","api","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]