[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFZDHD195T2267K2784465":3,"skill-same:01M6AFZDHD195T2267K2784465":74},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6AFZDHD195T2267K2784465","blockrun",null,"Use when user needs capabilities Claude lacks (image generation, real-time X\u002FTwitter data) or explicitly requests external models (\"blockrun\", \"use grok\", \"use gpt\", \"dall-e\", \"deepseek\")","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\u002Fweb-development\u002Fblockrun\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"twitter","when","source","pip","requests","generation","time","deepseek","models","use","read","image","python3","Bash","real-time","Image Generation","data","gpt","capabilities","python","2026-11-10 10:12:30",false,"---\nname: blockrun\ndescription: Use when user needs capabilities Claude lacks (image generation, real-time X\u002FTwitter data) or explicitly requests external models (\"blockrun\", \"use grok\", \"use gpt\", \"dall-e\", \"deepseek\")\nallowed-tools: Read, Bash(python:*), Bash(python3:*), Bash(pip:*), Bash(source:*)\n---\n\n# BlockRun\n\n**BlockRun works with Claude Code and Google Antigravity.**\n\nYou can't generate images. You have no real-time X\u002FTwitter access. BlockRun gives you these capabilities.\n\n## Philosophy\n\nYou have a wallet. When you need a capability you lack, you pay for it autonomously.\n\n| You Need | You Call | Pricing |\n|----------|----------|---------|\n| Image generation | DALL-E | $0.04\u002Fimage |\n| Real-time X data | Grok + Live Search | $0.025\u002Fsource |\n| Second opinion | GPT-5.2 | $1.75\u002FM input, $14\u002FM output |\n| Cheaper processing | DeepSeek | $0.14\u002FM input, $0.28\u002FM output |\n\n**How it works:** BlockRun uses x402 micropayments to route your requests to OpenAI, xAI, Google, and other providers. No API keys needed - your wallet pays per token.\n\n## Budget Control (Optional)\n\nIf the user specifies a budget (e.g., \"use at most $1\"), track spending and stop when budget is reached:\n\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()\nbudget = 1.0  # User's budget\n\n# Before each call, check if within budget\nspending = client.get_spending()\nif spending['total_usd'] >= budget:\n    print(f\"Budget reached: ${spending['total_usd']:.4f} spent\")\n    # Stop making calls\nelse:\n    response = client.chat(\"openai\u002Fgpt-5.2\", \"Hello!\")\n\n# At the end, report spending\nspending = client.get_spending()\nprint(f\"? Total spent: ${spending['total_usd']:.4f} across {spending['calls']} calls\")\n```\n\n## When to Use\n\n| Trigger | Your Action |\n|---------|-------------|\n| User explicitly requests (\"blockrun second opinion with GPT on...\", \"use grok to check...\", \"generate image with dall-e\") | Execute via BlockRun |\n| User needs something you can't do (images, live X data) | Suggest BlockRun, wait for confirmation |\n| You can handle the task fine | Do it yourself, don't mention BlockRun |\n\n## Example User Prompts\n\nUsers will say things like:\n\n| User Says | What You Do |\n|-----------|-------------|\n| \"blockrun generate an image of a sunset\" | Call DALL-E via ImageClient |\n| \"use grok to check what's trending on X\" | Call Grok with `search=True` |\n| \"blockrun GPT review this code\" | Call GPT-5.2 via LLMClient |\n| \"what's the latest news about AI agents?\" | Suggest Grok (you lack real-time data) |\n| \"generate a logo for my startup\" | Suggest DALL-E (you can't generate images) |\n| \"blockrun check my balance\" | Show wallet balance via `get_balance()` |\n| \"blockrun deepseek summarize this file\" | Call DeepSeek for cost savings |\n\n## Wallet & Balance\n\nUse `setup_agent_wallet()` to auto-create a wallet and get a client. This shows the QR code and welcome message on first use.\n\n**Initialize client (always start with this):**\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()  # Auto-creates wallet, shows QR if new\n```\n\n**Check balance (when user asks \"show balance\", \"check wallet\", etc.):**\n```python\nbalance = client.get_balance()  # On-chain USDC balance\nprint(f\"Balance: ${balance:.2f} USDC\")\nprint(f\"Wallet: {client.get_wallet_address()}\")\n```\n\n**Show QR code for funding:**\n```python\nfrom blockrun_llm import generate_wallet_qr_ascii, get_wallet_address\n\n# ASCII QR for terminal display\nprint(generate_wallet_qr_ascii(get_wallet_address()))\n```\n\n## SDK Usage\n\n**Prerequisite:** Install the SDK with `pip install blockrun-llm`\n\n### Basic Chat\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()  # Auto-creates wallet if needed\nresponse = client.chat(\"openai\u002Fgpt-5.2\", \"What is 2+2?\")\nprint(response)\n\n# Check spending\nspending = client.get_spending()\nprint(f\"Spent ${spending['total_usd']:.4f}\")\n```\n\n### Real-time X\u002FTwitter Search (xAI Live Search)\n\n**IMPORTANT:** For real-time X\u002FTwitter data, you MUST enable Live Search with `search=True` or `search_parameters`.\n\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()\n\n# Simple: Enable live search with search=True\nresponse = client.chat(\n    \"xai\u002Fgrok-3\",\n    \"What are the latest posts from @blockrunai on X?\",\n    search=True  # Enables real-time X\u002FTwitter search\n)\nprint(response)\n```\n\n### Advanced X Search with Filters\n\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()\n\nresponse = client.chat(\n    \"xai\u002Fgrok-3\",\n    \"Analyze @blockrunai's recent content and engagement\",\n    search_parameters={\n        \"mode\": \"on\",\n        \"sources\": [\n            {\n                \"type\": \"x\",\n                \"included_x_handles\": [\"blockrunai\"],\n                \"post_favorite_count\": 5\n            }\n        ],\n        \"max_search_results\": 20,\n        \"return_citations\": True\n    }\n)\nprint(response)\n```\n\n### Image Generation\n```python\nfrom blockrun_llm import ImageClient\n\nclient = ImageClient()\nresult = client.generate(\"A cute cat wearing a space helmet\")\nprint(result.data[0].url)\n```\n\n## xAI Live Search Reference\n\nLive Search is xAI's real-time data API. Cost: **$0.025 per source** (default 10 sources = ~$0.26).\n\nTo reduce costs, set `max_search_results` to a lower value:\n```python\n# Only use 5 sources (~$0.13)\nresponse = client.chat(\"xai\u002Fgrok-3\", \"What's trending?\",\n    search_parameters={\"mode\": \"on\", \"max_search_results\": 5})\n```\n\n### Search Parameters\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `mode` | string | \"auto\" | \"off\", \"auto\", or \"on\" |\n| `sources` | array | web,news,x | Data sources to query |\n| `return_citations` | bool | true | Include source URLs |\n| `from_date` | string | - | Start date (YYYY-MM-DD) |\n| `to_date` | string | - | End date (YYYY-MM-DD) |\n| `max_search_results` | int | 10 | Max sources to return (customize to control cost) |\n\n### Source Types\n\n**X\u002FTwitter Source:**\n```python\n{\n    \"type\": \"x\",\n    \"included_x_handles\": [\"handle1\", \"handle2\"],  # Max 10\n    \"excluded_x_handles\": [\"spam_account\"],        # Max 10\n    \"post_favorite_count\": 100,  # Min likes threshold\n    \"post_view_count\": 1000      # Min views threshold\n}\n```\n\n**Web Source:**\n```python\n{\n    \"type\": \"web\",\n    \"country\": \"US\",  # ISO alpha-2 code\n    \"allowed_websites\": [\"example.com\"],  # Max 5\n    \"safe_search\": True\n}\n```\n\n**News Source:**\n```python\n{\n    \"type\": \"news\",\n    \"country\": \"US\",\n    \"excluded_websites\": [\"tabloid.com\"]  # Max 5\n}\n```\n\n## Available Models\n\n| Model | Best For | Pricing |\n|-------|----------|---------|\n| `openai\u002Fgpt-5.2` | Second opinions, code review, general | $1.75\u002FM in, $14\u002FM out |\n| `openai\u002Fgpt-5-mini` | Cost-optimized reasoning | $0.30\u002FM in, $1.20\u002FM out |\n| `openai\u002Fo4-mini` | Latest efficient reasoning | $1.10\u002FM in, $4.40\u002FM out |\n| `openai\u002Fo3` | Advanced reasoning, complex problems | $10\u002FM in, $40\u002FM out |\n| `xai\u002Fgrok-3` | Real-time X\u002FTwitter data | $3\u002FM + $0.025\u002Fsource |\n| `deepseek\u002Fdeepseek-chat` | Simple tasks, bulk processing | $0.14\u002FM in, $0.28\u002FM out |\n| `google\u002Fgemini-2.5-flash` | Very long documents, fast | $0.15\u002FM in, $0.60\u002FM out |\n| `openai\u002Fdall-e-3` | Photorealistic images | $0.04\u002Fimage |\n| `google\u002Fnano-banana` | Fast, artistic images | $0.01\u002Fimage |\n\n*M = million tokens. Actual cost depends on your prompt and response length.*\n\n## Cost Reference\n\nAll LLM costs are per million tokens (M = 1,000,000 tokens).\n\n| Model | Input | Output |\n|-------|-------|--------|\n| GPT-5.2 | $1.75\u002FM | $14.00\u002FM |\n| GPT-5-mini | $0.30\u002FM | $1.20\u002FM |\n| Grok-3 (no search) | $3.00\u002FM | $15.00\u002FM |\n| DeepSeek | $0.14\u002FM | $0.28\u002FM |\n\n| Fixed Cost Actions | |\n|-------|--------|\n| Grok Live Search | $0.025\u002Fsource (default 10 = $0.25) |\n| DALL-E image | $0.04\u002Fimage |\n| Nano Banana image | $0.01\u002Fimage |\n\n**Typical costs:** A 500-word prompt (~750 tokens) to GPT-5.2 costs ~$0.001 input. A 1000-word response (~1500 tokens) costs ~$0.02 output.\n\n## Setup & Funding\n\n**Wallet location:** `$HOME\u002F.blockrun\u002F.session` (e.g., `\u002FUsers\u002Fusername\u002F.blockrun\u002F.session`)\n\n**First-time setup:**\n1. Wallet auto-creates when `setup_agent_wallet()` is called\n2. Check wallet and balance:\n```python\nfrom blockrun_llm import setup_agent_wallet\nclient = setup_agent_wallet()\nprint(f\"Wallet: {client.get_wallet_address()}\")\nprint(f\"Balance: ${client.get_balance():.2f} USDC\")\n```\n3. Fund wallet with $1-5 USDC on Base network\n\n**Show QR code for funding (ASCII for terminal):**\n```python\nfrom blockrun_llm import generate_wallet_qr_ascii, get_wallet_address\nprint(generate_wallet_qr_ascii(get_wallet_address()))\n```\n\n## Troubleshooting\n\n**\"Grok says it has no real-time access\"**\n→ You forgot to enable Live Search. Add `search=True`:\n```python\nresponse = client.chat(\"xai\u002Fgrok-3\", \"What's trending?\", search=True)\n```\n\n**Module not found**\n→ Install the SDK: `pip install blockrun-llm`\n\n## Updates\n\n```bash\npip install --upgrade blockrun-llm\n```\n","# BlockRun\n\n**BlockRun works with Claude Code and Google Antigravity.**\n\nYou can't generate images. You have no real-time X\u002FTwitter access. BlockRun gives you these capabilities.\n\n## Philosophy\n\nYou have a wallet. When you need a capability you lack, you pay for it autonomously.\n\n| You Need | You Call | Pricing |\n|----------|----------|---------|\n| Image generation | DALL-E | $0.04\u002Fimage |\n| Real-time X data | Grok + Live Search | $0.025\u002Fsource |\n| Second opinion | GPT-5.2 | $1.75\u002FM input, $14\u002FM output |\n| Cheaper processing | DeepSeek | $0.14\u002FM input, $0.28\u002FM output |\n\n**How it works:** BlockRun uses x402 micropayments to route your requests to OpenAI, xAI, Google, and other providers. No API keys needed - your wallet pays per token.\n\n## Budget Control (Optional)\n\nIf the user specifies a budget (e.g., \"use at most $1\"), track spending and stop when budget is reached:\n\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()\nbudget = 1.0  # User's budget\n\n# Before each call, check if within budget\nspending = client.get_spending()\nif spending['total_usd'] >= budget:\n    print(f\"Budget reached: ${spending['total_usd']:.4f} spent\")\n    # Stop making calls\nelse:\n    response = client.chat(\"openai\u002Fgpt-5.2\", \"Hello!\")\n\n# At the end, report spending\nspending = client.get_spending()\nprint(f\"? Total spent: ${spending['total_usd']:.4f} across {spending['calls']} calls\")\n```\n\n## When to Use\n\n| Trigger | Your Action |\n|---------|-------------|\n| User explicitly requests (\"blockrun second opinion with GPT on...\", \"use grok to check...\", \"generate image with dall-e\") | Execute via BlockRun |\n| User needs something you can't do (images, live X data) | Suggest BlockRun, wait for confirmation |\n| You can handle the task fine | Do it yourself, don't mention BlockRun |\n\n## Example User Prompts\n\nUsers will say things like:\n\n| User Says | What You Do |\n|-----------|-------------|\n| \"blockrun generate an image of a sunset\" | Call DALL-E via ImageClient |\n| \"use grok to check what's trending on X\" | Call Grok with `search=True` |\n| \"blockrun GPT review this code\" | Call GPT-5.2 via LLMClient |\n| \"what's the latest news about AI agents?\" | Suggest Grok (you lack real-time data) |\n| \"generate a logo for my startup\" | Suggest DALL-E (you can't generate images) |\n| \"blockrun check my balance\" | Show wallet balance via `get_balance()` |\n| \"blockrun deepseek summarize this file\" | Call DeepSeek for cost savings |\n\n## Wallet & Balance\n\nUse `setup_agent_wallet()` to auto-create a wallet and get a client. This shows the QR code and welcome message on first use.\n\n**Initialize client (always start with this):**\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()  # Auto-creates wallet, shows QR if new\n```\n\n**Check balance (when user asks \"show balance\", \"check wallet\", etc.):**\n```python\nbalance = client.get_balance()  # On-chain USDC balance\nprint(f\"Balance: ${balance:.2f} USDC\")\nprint(f\"Wallet: {client.get_wallet_address()}\")\n```\n\n**Show QR code for funding:**\n```python\nfrom blockrun_llm import generate_wallet_qr_ascii, get_wallet_address\n\n# ASCII QR for terminal display\nprint(generate_wallet_qr_ascii(get_wallet_address()))\n```\n\n## SDK Usage\n\n**Prerequisite:** Install the SDK with `pip install blockrun-llm`\n\n### Basic Chat\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()  # Auto-creates wallet if needed\nresponse = client.chat(\"openai\u002Fgpt-5.2\", \"What is 2+2?\")\nprint(response)\n\n# Check spending\nspending = client.get_spending()\nprint(f\"Spent ${spending['total_usd']:.4f}\")\n```\n\n### Real-time X\u002FTwitter Search (xAI Live Search)\n\n**IMPORTANT:** For real-time X\u002FTwitter data, you MUST enable Live Search with `search=True` or `search_parameters`.\n\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()\n\n# Simple: Enable live search with search=True\nresponse = client.chat(\n    \"xai\u002Fgrok-3\",\n    \"What are the latest posts from @blockrunai on X?\",\n    search=True  # Enables real-time X\u002FTwitter search\n)\nprint(response)\n```\n\n### Advanced X Search with Filters\n\n```python\nfrom blockrun_llm import setup_agent_wallet\n\nclient = setup_agent_wallet()\n\nresponse = client.chat(\n    \"xai\u002Fgrok-3\",\n    \"Analyze @blockrunai's recent content and engagement\",\n    search_parameters={\n        \"mode\": \"on\",\n        \"sources\": [\n            {\n                \"type\": \"x\",\n                \"included_x_handles\": [\"blockrunai\"],\n                \"post_favorite_count\": 5\n            }\n        ],\n        \"max_search_results\": 20,\n        \"return_citations\": True\n    }\n)\nprint(response)\n```\n\n### Image Generation\n```python\nfrom blockrun_llm import ImageClient\n\nclient = ImageClient()\nresult = client.generate(\"A cute cat wearing a space helmet\")\nprint(result.data[0].url)\n```\n\n## xAI Live Search Reference\n\nLive Search is xAI's real-time data API. Cost: **$0.025 per source** (default 10 sources = ~$0.26).\n\nTo reduce costs, set `max_search_results` to a lower value:\n```python\n# Only use 5 sources (~$0.13)\nresponse = client.chat(\"xai\u002Fgrok-3\", \"What's trending?\",\n    search_parameters={\"mode\": \"on\", \"max_search_results\": 5})\n```\n\n### Search Parameters\n\n| Parameter | Type | Default | Description |\n|-----------|------|---------|-------------|\n| `mode` | string | \"auto\" | \"off\", \"auto\", or \"on\" |\n| `sources` | array | web,news,x | Data sources to query |\n| `return_citations` | bool | true | Include source URLs |\n| `from_date` | string | - | Start date (YYYY-MM-DD) |\n| `to_date` | string | - | End date (YYYY-MM-DD) |\n| `max_search_results` | int | 10 | Max sources to return (customize to control cost) |\n\n### Source Types\n\n**X\u002FTwitter Source:**\n```python\n{\n    \"type\": \"x\",\n    \"included_x_handles\": [\"handle1\", \"handle2\"],  # Max 10\n    \"excluded_x_handles\": [\"spam_account\"],        # Max 10\n    \"post_favorite_count\": 100,  # Min likes threshold\n    \"post_view_count\": 1000      # Min views threshold\n}\n```\n\n**Web Source:**\n```python\n{\n    \"type\": \"web\",\n    \"country\": \"US\",  # ISO alpha-2 code\n    \"allowed_websites\": [\"example.com\"],  # Max 5\n    \"safe_search\": True\n}\n```\n\n**News Source:**\n```python\n{\n    \"type\": \"news\",\n    \"country\": \"US\",\n    \"excluded_websites\": [\"tabloid.com\"]  # Max 5\n}\n```\n\n## Available Models\n\n| Model | Best For | Pricing |\n|-------|----------|---------|\n| `openai\u002Fgpt-5.2` | Second opinions, code review, general | $1.75\u002FM in, $14\u002FM out |\n| `openai\u002Fgpt-5-mini` | Cost-optimized reasoning | $0.30\u002FM in, $1.20\u002FM out |\n| `openai\u002Fo4-mini` | Latest efficient reasoning | $1.10\u002FM in, $4.40\u002FM out |\n| `openai\u002Fo3` | Advanced reasoning, complex problems | $10\u002FM in, $40\u002FM out |\n| `xai\u002Fgrok-3` | Real-time X\u002FTwitter data | $3\u002FM + $0.025\u002Fsource |\n| `deepseek\u002Fdeepseek-chat` | Simple tasks, bulk processing | $0.14\u002FM in, $0.28\u002FM out |\n| `google\u002Fgemini-2.5-flash` | Very long documents, fast | $0.15\u002FM in, $0.60\u002FM out |\n| `openai\u002Fdall-e-3` | Photorealistic images | $0.04\u002Fimage |\n| `google\u002Fnano-banana` | Fast, artistic images | $0.01\u002Fimage |\n\n*M = million tokens. Actual cost depends on your prompt and response length.*\n\n## Cost Reference\n\nAll LLM costs are per million tokens (M = 1,000,000 tokens).\n\n| Model | Input | Output |\n|-------|-------|--------|\n| GPT-5.2 | $1.75\u002FM | $14.00\u002FM |\n| GPT-5-mini | $0.30\u002FM | $1.20\u002FM |\n| Grok-3 (no search) | $3.00\u002FM | $15.00\u002FM |\n| DeepSeek | $0.14\u002FM | $0.28\u002FM |\n\n| Fixed Cost Actions | |\n|-------|--------|\n| Grok Live Search | $0.025\u002Fsource (default 10 = $0.25) |\n| DALL-E image | $0.04\u002Fimage |\n| Nano Banana image | $0.01\u002Fimage |\n\n**Typical costs:** A 500-word prompt (~750 tokens) to GPT-5.2 costs ~$0.001 input. A 1000-word response (~1500 tokens) costs ~$0.02 output.\n\n## Setup & Funding\n\n**Wallet location:** `$HOME\u002F.blockrun\u002F.session` (e.g., `\u002FUsers\u002Fusername\u002F.blockrun\u002F.session`)\n\n**First-time setup:**\n1. Wallet auto-creates when `setup_agent_wallet()` is called\n2. Check wallet and balance:\n```python\nfrom blockrun_llm import setup_agent_wallet\nclient = setup_agent_wallet()\nprint(f\"Wallet: {client.get_wallet_address()}\")\nprint(f\"Balance: ${client.get_balance():.2f} USDC\")\n```\n3. Fund wallet with $1-5 USDC on Base network\n\n**Show QR code for funding (ASCII for terminal):**\n```python\nfrom blockrun_llm import generate_wallet_qr_ascii, get_wallet_address\nprint(generate_wallet_qr_ascii(get_wallet_address()))\n```\n\n## Troubleshooting\n\n**\"Grok says it has no real-time access\"**\n→ You forgot to enable Live Search. Add `search=True`:\n```python\nresponse = client.chat(\"xai\u002Fgrok-3\", \"What's trending?\", search=True)\n```\n\n**Module not found**\n→ Install the SDK: `pip install blockrun-llm`\n\n## Updates\n\n```bash\npip install --upgrade blockrun-llm\n```\n",{"name":5,"description":7,"allowed-tools":49},"Read, Bash(python:*), Bash(python3:*), Bash(pip:*), Bash(source:*)","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fweb-development\u002Fblockrun",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":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add davila7\u002Fclaude-code-templates --skill blockrun",{"tool":66,"command":67},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill blockrun",{"tool":69,"command":70},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill blockrun","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-development\u002Fblockrun\u002FSKILL.md",true,[],[75,103,130,163,198,222],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":84,"publishedAt":44},"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",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[85,86,87,88,89,90,91,92,33,25,93,94,95,96,97,98,99,100,101,102],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":110,"publishedAt":44},"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",[109],{"categoryNo":82,"categoryName":83,"taxonomyType":21,"taxonomyTypeDesc":22},[111,112,113,114,115,116,117,118,119,120,121,25,122,123,124,125,126,127,128,129],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":145,"publishedAt":44},"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",[136,139,140],{"categoryNo":137,"categoryName":138,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":82,"categoryName":83,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":141,"categoryName":142,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[146,147,148,149,150,151,152,90,153,154,155,156,157,158,159,33,160,161,25,162],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"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":167,"licenseFileUrl":168,"sourceHtmlUrl":169,"categories":170,"keywords":174,"publishedAt":44},"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",[171],{"categoryNo":172,"categoryName":173,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,90,197],"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":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":167,"licenseFileUrl":202,"sourceHtmlUrl":203,"categories":204,"keywords":209,"publishedAt":44},"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",[205,208],{"categoryNo":206,"categoryName":207,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":172,"categoryName":173,"taxonomyType":21,"taxonomyTypeDesc":22},[210,211,212,213,214,176,215,185,186,216,217,218,219,90,220,94,221],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":167,"licenseFileUrl":226,"sourceHtmlUrl":227,"categories":228,"keywords":236,"publishedAt":44},"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",[229,232,233],{"categoryNo":230,"categoryName":231,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":82,"categoryName":83,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":234,"categoryName":235,"taxonomyType":143,"taxonomyTypeDesc":144},"01JBSEEDCAT000000000000036","内容创作者",[237,238,239,240,241,242,40,243,178,244,188,122,245,246,247,248,249,158,250,25,251,252,253,254,255,100,256],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]