[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AG2SRFVT9KRGEK86GQZWFX":3,"skill-same:01M6AG2SRFVT9KRGEK86GQZWFX":94},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6AG2SRFVT9KRGEK86GQZWFX","n8n-code-python",null,"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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-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",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"python","code","n8n code python","n8n","when","writing","standard","python code","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 - 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",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add davila7\u002Fclaude-code-templates --skill n8n-code-python",{"tool":64,"command":65},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill n8n-code-python",{"tool":67,"command":68},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill n8n-code-python","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python\u002FSKILL.md",true,[72,78,82,86,90],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"COMMON_PATTERNS.md","REFERENCE","参考文档",19329,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python\u002FCOMMON_PATTERNS.md",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"DATA_ACCESS.md",15026,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python\u002FDATA_ACCESS.md",{"path":83,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":84,"sourceHtmlUrl":85},"ERROR_PATTERNS.md",13913,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python\u002FERROR_PATTERNS.md",{"path":87,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":88,"sourceHtmlUrl":89},"README.md",9524,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python\u002FREADME.md",{"path":91,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":92,"sourceHtmlUrl":93},"STANDARD_LIBRARY.md",18115,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-code-python\u002FSTANDARD_LIBRARY.md",[95,124,150,181,216,240],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":106,"publishedAt":43},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[101],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[107,108,109,110,111,112,113,114,37,28,115,32,116,117,118,119,120,121,122,123],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","record","track","verdict","evm","solana","how","public","look",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":131,"publishedAt":43},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[130],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},[132,133,134,135,136,137,138,139,140,141,142,28,25,143,144,145,146,147,148,149],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":163,"publishedAt":43},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[156,159,160],{"categoryNo":157,"categoryName":158,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},{"categoryNo":161,"categoryName":162,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师",[164,165,166,167,168,169,170,112,171,172,173,174,175,176,177,37,178,179,28,180],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":185,"licenseFileUrl":186,"sourceHtmlUrl":187,"categories":188,"keywords":192,"publishedAt":43},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[189],{"categoryNo":190,"categoryName":191,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000009","前端开发",[193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,112,215],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":185,"licenseFileUrl":220,"sourceHtmlUrl":221,"categories":222,"keywords":227,"publishedAt":43},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[223,226],{"categoryNo":224,"categoryName":225,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":190,"categoryName":191,"taxonomyType":104,"taxonomyTypeDesc":105},[228,229,230,231,232,194,233,203,204,234,235,236,237,112,238,32,239],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":241,"name":242,"nameZh":6,"description":243,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":185,"licenseFileUrl":244,"sourceHtmlUrl":245,"categories":246,"keywords":254,"publishedAt":43},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[247,250,251],{"categoryNo":248,"categoryName":249,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},{"categoryNo":252,"categoryName":253,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者",[255,256,257,258,259,260,261,29,196,262,206,25,263,264,265,266,267,176,268,28,269,270,271,272,273,121,274],"discernment nudge","nudge","interpretation","help","run","reasoning","data","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]