[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6R4EWCM5ST7BC0SCG6CCS22":3,"skill-same:01M6R4EWCM5ST7BC0SCG6CCS22":70},{"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":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6R4EWCM5ST7BC0SCG6CCS22","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\")","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\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],"read","when","source","pip","requests","generation","time","deepseek","models","use","image","twitter","python3","Bash","real-time","Image Generation","data","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\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\n",{"name":5,"description":7,"allowed-tools":48},"Read, Bash(python:*), Bash(python3:*), Bash(pip:*), Bash(source:*)","main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fweb-development\u002Fblockrun",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-11 17:58:24",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill blockrun",{"tool":62,"command":63},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill blockrun",{"tool":65,"command":66},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill blockrun","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-development\u002Fblockrun\u002FSKILL.md",true,[],[71,102,128,155,181,205],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":75,"licenseFileUrl":76,"sourceHtmlUrl":77,"categories":78,"keywords":82,"publishedAt":43},"01M6R4ARN4W2VZRDERDTMN1KP0","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[79],{"categoryNo":80,"categoryName":81,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[83,84,85,86,87,88,89,90,91,92,93,94,95,96,33,97,98,99,100,101],"web artifacts builder","web","artifacts","artifacts builder","builder","frontend","css","components","react","routing","state management","state","file","Tailwind","single-file","ui components","multi","for","single",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":75,"licenseFileUrl":106,"sourceHtmlUrl":107,"categories":108,"keywords":113,"publishedAt":43},"01M6R45GXREM5DP8T08MAC788H","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\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[109,112],{"categoryNo":110,"categoryName":111,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":80,"categoryName":81,"taxonomyType":21,"taxonomyTypeDesc":22},[114,115,116,88,117,118,84,119,120,121,122,123,124,125,126,127,100],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","ui","logs","applications","behavior","local","with",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":132,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":43},"01M6R2GYJ470X8EB7R58V0B68K","think-tank","Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural\u002Fdesign\u002Fstrategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,25],"think tank","think","design","run","feeds","analysis","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":75,"licenseFileUrl":159,"sourceHtmlUrl":160,"categories":161,"keywords":165,"publishedAt":43},"01M6R2FSR55AVRFA9YZ8SC2GZG","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[162],{"categoryNo":163,"categoryName":164,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[166,167,168,169,170,41,171,172,173,174,175,176,177,178,127,100,179,25,180],"skill creator","creator","workflows","create","update","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":187,"publishedAt":43},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[188,189,190,191,192,193,24,194,33,195,196,197,198,199,200,201,202,203,204,25],"learn","ship learn next","ship","next","framework","learning","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":75,"licenseFileUrl":209,"sourceHtmlUrl":210,"categories":211,"keywords":216,"publishedAt":43},"01M6R2088G1RHDA5MDX16HASRB","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[212,215],{"categoryNo":213,"categoryName":214,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[217,218,219,167,220,221,222,223,171,33,151,178,153,224,225,100,226,25],"slack gif creator","slack","gif","validation","Tools","constraints","Animation","Utilities","concepts","request"]