[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADR29HTR5RGY443BNPAZ22":3,"skill-same:01M6ADR29HTR5RGY443BNPAZ22":117},{"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":18,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":66,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6ADR29HTR5RGY443BNPAZ22","infrastructure-modal",null,"Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.","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\u002Fai-research\u002Finfrastructure-modal\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"infrastructure","infrastructure modal","modal","use","On-Demand GPU","running","when","automatic","for","orchestra","you","infrastructure management","as","platform","scaling","research","access","GPU Cloud","cloud","batch","gpu","deployment","ml","2026-11-10 10:12:30",false,"---\nname: infrastructure-modal\ndescription: Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Infrastructure, Serverless, GPU, Cloud, Deployment, Modal]\n  dependencies: [modal>=0.64.0]\n---\n\n# Modal Serverless GPU\n\nComprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.\n\n## When to use Modal\n\n**Use Modal when:**\n- Running GPU-intensive ML workloads without managing infrastructure\n- Deploying ML models as auto-scaling APIs\n- Running batch processing jobs (training, inference, data processing)\n- Need pay-per-second GPU pricing without idle costs\n- Prototyping ML applications quickly\n- Running scheduled jobs (cron-like workloads)\n\n**Key features:**\n- **Serverless GPUs**: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand\n- **Python-native**: Define infrastructure in Python code, no YAML\n- **Auto-scaling**: Scale to zero, scale to 100+ GPUs instantly\n- **Sub-second cold starts**: Rust-based infrastructure for fast container launches\n- **Container caching**: Image layers cached for rapid iteration\n- **Web endpoints**: Deploy functions as REST APIs with zero-downtime updates\n\n**Use alternatives instead:**\n- **RunPod**: For longer-running pods with persistent state\n- **Lambda Labs**: For reserved GPU instances\n- **SkyPilot**: For multi-cloud orchestration and cost optimization\n- **Kubernetes**: For complex multi-service architectures\n\n## Quick start\n\n### Installation\n\n```bash\npip install modal\nmodal setup  # Opens browser for authentication\n```\n\n### Hello World with GPU\n\n```python\nimport modal\n\napp = modal.App(\"hello-gpu\")\n\n@app.function(gpu=\"T4\")\ndef gpu_info():\n    import subprocess\n    return subprocess.run([\"nvidia-smi\"], capture_output=True, text=True).stdout\n\n@app.local_entrypoint()\ndef main():\n    print(gpu_info.remote())\n```\n\nRun: `modal run hello_gpu.py`\n\n### Basic inference endpoint\n\n```python\nimport modal\n\napp = modal.App(\"text-generation\")\nimage = modal.Image.debian_slim().pip_install(\"transformers\", \"torch\", \"accelerate\")\n\n@app.cls(gpu=\"A10G\", image=image)\nclass TextGenerator:\n    @modal.enter()\n    def load_model(self):\n        from transformers import pipeline\n        self.pipe = pipeline(\"text-generation\", model=\"gpt2\", device=0)\n\n    @modal.method()\n    def generate(self, prompt: str) -> str:\n        return self.pipe(prompt, max_length=100)[0][\"generated_text\"]\n\n@app.local_entrypoint()\ndef main():\n    print(TextGenerator().generate.remote(\"Hello, world\"))\n```\n\n## Core concepts\n\n### Key components\n\n| Component | Purpose |\n|-----------|---------|\n| `App` | Container for functions and resources |\n| `Function` | Serverless function with compute specs |\n| `Cls` | Class-based functions with lifecycle hooks |\n| `Image` | Container image definition |\n| `Volume` | Persistent storage for models\u002Fdata |\n| `Secret` | Secure credential storage |\n\n### Execution modes\n\n| Command | Description |\n|---------|-------------|\n| `modal run script.py` | Execute and exit |\n| `modal serve script.py` | Development with live reload |\n| `modal deploy script.py` | Persistent cloud deployment |\n\n## GPU configuration\n\n### Available GPUs\n\n| GPU | VRAM | Best For |\n|-----|------|----------|\n| `T4` | 16GB | Budget inference, small models |\n| `L4` | 24GB | Inference, Ada Lovelace arch |\n| `A10G` | 24GB | Training\u002Finference, 3.3x faster than T4 |\n| `L40S` | 48GB | Recommended for inference (best cost\u002Fperf) |\n| `A100-40GB` | 40GB | Large model training |\n| `A100-80GB` | 80GB | Very large models |\n| `H100` | 80GB | Fastest, FP8 + Transformer Engine |\n| `H200` | 141GB | Auto-upgrade from H100, 4.8TB\u002Fs bandwidth |\n| `B200` | Latest | Blackwell architecture |\n\n### GPU specification patterns\n\n```python\n# Single GPU\n@app.function(gpu=\"A100\")\n\n# Specific memory variant\n@app.function(gpu=\"A100-80GB\")\n\n# Multiple GPUs (up to 8)\n@app.function(gpu=\"H100:4\")\n\n# GPU with fallbacks\n@app.function(gpu=[\"H100\", \"A100\", \"L40S\"])\n\n# Any available GPU\n@app.function(gpu=\"any\")\n```\n\n## Container images\n\n```python\n# Basic image with pip\nimage = modal.Image.debian_slim(python_version=\"3.11\").pip_install(\n    \"torch==2.1.0\", \"transformers==4.36.0\", \"accelerate\"\n)\n\n# From CUDA base\nimage = modal.Image.from_registry(\n    \"nvidia\u002Fcuda:12.1.0-cudnn8-devel-ubuntu22.04\",\n    add_python=\"3.11\"\n).pip_install(\"torch\", \"transformers\")\n\n# With system packages\nimage = modal.Image.debian_slim().apt_install(\"git\", \"ffmpeg\").pip_install(\"whisper\")\n```\n\n## Persistent storage\n\n```python\nvolume = modal.Volume.from_name(\"model-cache\", create_if_missing=True)\n\n@app.function(gpu=\"A10G\", volumes={\"\u002Fmodels\": volume})\ndef load_model():\n    import os\n    model_path = \"\u002Fmodels\u002Fllama-7b\"\n    if not os.path.exists(model_path):\n        model = download_model()\n        model.save_pretrained(model_path)\n        volume.commit()  # Persist changes\n    return load_from_path(model_path)\n```\n\n## Web endpoints\n\n### FastAPI endpoint decorator\n\n```python\n@app.function()\n@modal.fastapi_endpoint(method=\"POST\")\ndef predict(text: str) -> dict:\n    return {\"result\": model.predict(text)}\n```\n\n### Full ASGI app\n\n```python\nfrom fastapi import FastAPI\nweb_app = FastAPI()\n\n@web_app.post(\"\u002Fpredict\")\nasync def predict(text: str):\n    return {\"result\": await model.predict.remote.aio(text)}\n\n@app.function()\n@modal.asgi_app()\ndef fastapi_app():\n    return web_app\n```\n\n### Web endpoint types\n\n| Decorator | Use Case |\n|-----------|----------|\n| `@modal.fastapi_endpoint()` | Simple function → API |\n| `@modal.asgi_app()` | Full FastAPI\u002FStarlette apps |\n| `@modal.wsgi_app()` | Django\u002FFlask apps |\n| `@modal.web_server(port)` | Arbitrary HTTP servers |\n\n## Dynamic batching\n\n```python\n@app.function()\n@modal.batched(max_batch_size=32, wait_ms=100)\nasync def batch_predict(inputs: list[str]) -> list[dict]:\n    # Inputs automatically batched\n    return model.batch_predict(inputs)\n```\n\n## Secrets management\n\n```bash\n# Create secret\nmodal secret create huggingface HF_TOKEN=hf_xxx\n```\n\n```python\n@app.function(secrets=[modal.Secret.from_name(\"huggingface\")])\ndef download_model():\n    import os\n    token = os.environ[\"HF_TOKEN\"]\n```\n\n## Scheduling\n\n```python\n@app.function(schedule=modal.Cron(\"0 0 * * *\"))  # Daily midnight\ndef daily_job():\n    pass\n\n@app.function(schedule=modal.Period(hours=1))\ndef hourly_job():\n    pass\n```\n\n## Performance optimization\n\n### Cold start mitigation\n\n```python\n@app.function(\n    container_idle_timeout=300,  # Keep warm 5 min\n    allow_concurrent_inputs=10,  # Handle concurrent requests\n)\ndef inference():\n    pass\n```\n\n### Model loading best practices\n\n```python\n@app.cls(gpu=\"A100\")\nclass Model:\n    @modal.enter()  # Run once at container start\n    def load(self):\n        self.model = load_model()  # Load during warm-up\n\n    @modal.method()\n    def predict(self, x):\n        return self.model(x)\n```\n\n## Parallel processing\n\n```python\n@app.function()\ndef process_item(item):\n    return expensive_computation(item)\n\n@app.function()\ndef run_parallel():\n    items = list(range(1000))\n    # Fan out to parallel containers\n    results = list(process_item.map(items))\n    return results\n```\n\n## Common configuration\n\n```python\n@app.function(\n    gpu=\"A100\",\n    memory=32768,              # 32GB RAM\n    cpu=4,                     # 4 CPU cores\n    timeout=3600,              # 1 hour max\n    container_idle_timeout=120,# Keep warm 2 min\n    retries=3,                 # Retry on failure\n    concurrency_limit=10,      # Max concurrent containers\n)\ndef my_function():\n    pass\n```\n\n## Debugging\n\n```python\n# Test locally\nif __name__ == \"__main__\":\n    result = my_function.local()\n\n# View logs\n# modal app logs my-app\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Cold start latency | Increase `container_idle_timeout`, use `@modal.enter()` |\n| GPU OOM | Use larger GPU (`A100-80GB`), enable gradient checkpointing |\n| Image build fails | Pin dependency versions, check CUDA compatibility |\n| Timeout errors | Increase `timeout`, add checkpointing |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-GPU, distributed training, cost optimization\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmodal.com\u002Fdocs\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fmodal-labs\u002Fmodal-examples\n- **Pricing**: https:\u002F\u002Fmodal.com\u002Fpricing\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fmodal\n","# Modal Serverless GPU\n\nComprehensive guide to running ML workloads on Modal's serverless GPU cloud platform.\n\n## When to use Modal\n\n**Use Modal when:**\n- Running GPU-intensive ML workloads without managing infrastructure\n- Deploying ML models as auto-scaling APIs\n- Running batch processing jobs (training, inference, data processing)\n- Need pay-per-second GPU pricing without idle costs\n- Prototyping ML applications quickly\n- Running scheduled jobs (cron-like workloads)\n\n**Key features:**\n- **Serverless GPUs**: T4, L4, A10G, L40S, A100, H100, H200, B200 on-demand\n- **Python-native**: Define infrastructure in Python code, no YAML\n- **Auto-scaling**: Scale to zero, scale to 100+ GPUs instantly\n- **Sub-second cold starts**: Rust-based infrastructure for fast container launches\n- **Container caching**: Image layers cached for rapid iteration\n- **Web endpoints**: Deploy functions as REST APIs with zero-downtime updates\n\n**Use alternatives instead:**\n- **RunPod**: For longer-running pods with persistent state\n- **Lambda Labs**: For reserved GPU instances\n- **SkyPilot**: For multi-cloud orchestration and cost optimization\n- **Kubernetes**: For complex multi-service architectures\n\n## Quick start\n\n### Installation\n\n```bash\npip install modal\nmodal setup  # Opens browser for authentication\n```\n\n### Hello World with GPU\n\n```python\nimport modal\n\napp = modal.App(\"hello-gpu\")\n\n@app.function(gpu=\"T4\")\ndef gpu_info():\n    import subprocess\n    return subprocess.run([\"nvidia-smi\"], capture_output=True, text=True).stdout\n\n@app.local_entrypoint()\ndef main():\n    print(gpu_info.remote())\n```\n\nRun: `modal run hello_gpu.py`\n\n### Basic inference endpoint\n\n```python\nimport modal\n\napp = modal.App(\"text-generation\")\nimage = modal.Image.debian_slim().pip_install(\"transformers\", \"torch\", \"accelerate\")\n\n@app.cls(gpu=\"A10G\", image=image)\nclass TextGenerator:\n    @modal.enter()\n    def load_model(self):\n        from transformers import pipeline\n        self.pipe = pipeline(\"text-generation\", model=\"gpt2\", device=0)\n\n    @modal.method()\n    def generate(self, prompt: str) -> str:\n        return self.pipe(prompt, max_length=100)[0][\"generated_text\"]\n\n@app.local_entrypoint()\ndef main():\n    print(TextGenerator().generate.remote(\"Hello, world\"))\n```\n\n## Core concepts\n\n### Key components\n\n| Component | Purpose |\n|-----------|---------|\n| `App` | Container for functions and resources |\n| `Function` | Serverless function with compute specs |\n| `Cls` | Class-based functions with lifecycle hooks |\n| `Image` | Container image definition |\n| `Volume` | Persistent storage for models\u002Fdata |\n| `Secret` | Secure credential storage |\n\n### Execution modes\n\n| Command | Description |\n|---------|-------------|\n| `modal run script.py` | Execute and exit |\n| `modal serve script.py` | Development with live reload |\n| `modal deploy script.py` | Persistent cloud deployment |\n\n## GPU configuration\n\n### Available GPUs\n\n| GPU | VRAM | Best For |\n|-----|------|----------|\n| `T4` | 16GB | Budget inference, small models |\n| `L4` | 24GB | Inference, Ada Lovelace arch |\n| `A10G` | 24GB | Training\u002Finference, 3.3x faster than T4 |\n| `L40S` | 48GB | Recommended for inference (best cost\u002Fperf) |\n| `A100-40GB` | 40GB | Large model training |\n| `A100-80GB` | 80GB | Very large models |\n| `H100` | 80GB | Fastest, FP8 + Transformer Engine |\n| `H200` | 141GB | Auto-upgrade from H100, 4.8TB\u002Fs bandwidth |\n| `B200` | Latest | Blackwell architecture |\n\n### GPU specification patterns\n\n```python\n# Single GPU\n@app.function(gpu=\"A100\")\n\n# Specific memory variant\n@app.function(gpu=\"A100-80GB\")\n\n# Multiple GPUs (up to 8)\n@app.function(gpu=\"H100:4\")\n\n# GPU with fallbacks\n@app.function(gpu=[\"H100\", \"A100\", \"L40S\"])\n\n# Any available GPU\n@app.function(gpu=\"any\")\n```\n\n## Container images\n\n```python\n# Basic image with pip\nimage = modal.Image.debian_slim(python_version=\"3.11\").pip_install(\n    \"torch==2.1.0\", \"transformers==4.36.0\", \"accelerate\"\n)\n\n# From CUDA base\nimage = modal.Image.from_registry(\n    \"nvidia\u002Fcuda:12.1.0-cudnn8-devel-ubuntu22.04\",\n    add_python=\"3.11\"\n).pip_install(\"torch\", \"transformers\")\n\n# With system packages\nimage = modal.Image.debian_slim().apt_install(\"git\", \"ffmpeg\").pip_install(\"whisper\")\n```\n\n## Persistent storage\n\n```python\nvolume = modal.Volume.from_name(\"model-cache\", create_if_missing=True)\n\n@app.function(gpu=\"A10G\", volumes={\"\u002Fmodels\": volume})\ndef load_model():\n    import os\n    model_path = \"\u002Fmodels\u002Fllama-7b\"\n    if not os.path.exists(model_path):\n        model = download_model()\n        model.save_pretrained(model_path)\n        volume.commit()  # Persist changes\n    return load_from_path(model_path)\n```\n\n## Web endpoints\n\n### FastAPI endpoint decorator\n\n```python\n@app.function()\n@modal.fastapi_endpoint(method=\"POST\")\ndef predict(text: str) -> dict:\n    return {\"result\": model.predict(text)}\n```\n\n### Full ASGI app\n\n```python\nfrom fastapi import FastAPI\nweb_app = FastAPI()\n\n@web_app.post(\"\u002Fpredict\")\nasync def predict(text: str):\n    return {\"result\": await model.predict.remote.aio(text)}\n\n@app.function()\n@modal.asgi_app()\ndef fastapi_app():\n    return web_app\n```\n\n### Web endpoint types\n\n| Decorator | Use Case |\n|-----------|----------|\n| `@modal.fastapi_endpoint()` | Simple function → API |\n| `@modal.asgi_app()` | Full FastAPI\u002FStarlette apps |\n| `@modal.wsgi_app()` | Django\u002FFlask apps |\n| `@modal.web_server(port)` | Arbitrary HTTP servers |\n\n## Dynamic batching\n\n```python\n@app.function()\n@modal.batched(max_batch_size=32, wait_ms=100)\nasync def batch_predict(inputs: list[str]) -> list[dict]:\n    # Inputs automatically batched\n    return model.batch_predict(inputs)\n```\n\n## Secrets management\n\n```bash\n# Create secret\nmodal secret create huggingface HF_TOKEN=hf_xxx\n```\n\n```python\n@app.function(secrets=[modal.Secret.from_name(\"huggingface\")])\ndef download_model():\n    import os\n    token = os.environ[\"HF_TOKEN\"]\n```\n\n## Scheduling\n\n```python\n@app.function(schedule=modal.Cron(\"0 0 * * *\"))  # Daily midnight\ndef daily_job():\n    pass\n\n@app.function(schedule=modal.Period(hours=1))\ndef hourly_job():\n    pass\n```\n\n## Performance optimization\n\n### Cold start mitigation\n\n```python\n@app.function(\n    container_idle_timeout=300,  # Keep warm 5 min\n    allow_concurrent_inputs=10,  # Handle concurrent requests\n)\ndef inference():\n    pass\n```\n\n### Model loading best practices\n\n```python\n@app.cls(gpu=\"A100\")\nclass Model:\n    @modal.enter()  # Run once at container start\n    def load(self):\n        self.model = load_model()  # Load during warm-up\n\n    @modal.method()\n    def predict(self, x):\n        return self.model(x)\n```\n\n## Parallel processing\n\n```python\n@app.function()\ndef process_item(item):\n    return expensive_computation(item)\n\n@app.function()\ndef run_parallel():\n    items = list(range(1000))\n    # Fan out to parallel containers\n    results = list(process_item.map(items))\n    return results\n```\n\n## Common configuration\n\n```python\n@app.function(\n    gpu=\"A100\",\n    memory=32768,              # 32GB RAM\n    cpu=4,                     # 4 CPU cores\n    timeout=3600,              # 1 hour max\n    container_idle_timeout=120,# Keep warm 2 min\n    retries=3,                 # Retry on failure\n    concurrency_limit=10,      # Max concurrent containers\n)\ndef my_function():\n    pass\n```\n\n## Debugging\n\n```python\n# Test locally\nif __name__ == \"__main__\":\n    result = my_function.local()\n\n# View logs\n# modal app logs my-app\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Cold start latency | Increase `container_idle_timeout`, use `@modal.enter()` |\n| GPU OOM | Use larger GPU (`A100-80GB`), enable gradient checkpointing |\n| Image build fails | Pin dependency versions, check CUDA compatibility |\n| Timeout errors | Increase `timeout`, add checkpointing |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-GPU, distributed training, cost optimization\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fmodal.com\u002Fdocs\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fmodal-labs\u002Fmodal-examples\n- **Pricing**: https:\u002F\u002Fmodal.com\u002Fpricing\n- **Discord**: 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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",[124],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[130,131,132,133,134,27,135,136,22,25,137,138,139,140,141,142,143,144,145,146],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":154,"publishedAt":42},"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",[153],{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},[155,156,157,158,159,160,161,162,163,164,165,25,166,167,168,169,170,171,172,173],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":189,"publishedAt":42},"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",[180,183,184],{"categoryNo":181,"categoryName":182,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},{"categoryNo":185,"categoryName":186,"taxonomyType":187,"taxonomyTypeDesc":188},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[190,191,192,193,194,195,196,27,197,198,199,200,201,202,203,22,35,204,25,205],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","database","query",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":211,"sourceHtmlUrl":212,"categories":213,"keywords":217,"publishedAt":42},"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",[214],{"categoryNo":215,"categoryName":216,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000009","前端开发",[218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,27,240],"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":242,"name":243,"nameZh":6,"description":244,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":245,"sourceHtmlUrl":246,"categories":247,"keywords":252,"publishedAt":42},"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",[248,251],{"categoryNo":249,"categoryName":250,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":215,"categoryName":216,"taxonomyType":127,"taxonomyTypeDesc":128},[253,254,255,256,257,219,258,228,229,259,260,261,262,27,263,138,264],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":266,"name":267,"nameZh":6,"description":268,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":210,"licenseFileUrl":269,"sourceHtmlUrl":270,"categories":271,"keywords":279,"publishedAt":42},"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",[272,275,276],{"categoryNo":273,"categoryName":274,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},{"categoryNo":277,"categoryName":278,"taxonomyType":187,"taxonomyTypeDesc":188},"01JBSEEDCAT000000000000036","内容创作者",[280,281,282,283,284,285,286,287,221,288,231,166,289,290,291,292,293,202,294,25,29,295,296,297,298,144,299],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","analysis","claims","argument","assemble","recommendations"]