[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MACDZ628DM2BHH81YZDMKE":3,"skill-same:01M6MACDZ628DM2BHH81YZDMKE":89},{"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":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6MACDZ628DM2BHH81YZDMKE","llama-cpp",null,"Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1559,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fllama-cpp\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"cpp","Llama","llama cpp","CPU","when","for","Intel GPUs","AMD GPUs","Non-NVIDIA","Edge Deployment","Consumer GPUs","CPU Inference","llama.cpp","GGUF","memory","silicon","bit","apple","Inference Serving","NVIDIA","pytorch","2026-11-10 10:12:30",false,"---\nname: llama-cpp\ndescription: Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1\u002FM2\u002FM3 Macs, AMD\u002FIntel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: ml-inference-and-ops\n  maintainer: Kalaris Labs\n  tags: Inference Serving, Llama.cpp, CPU Inference, Apple Silicon, Edge Deployment, GGUF, Quantization, Non-NVIDIA, AMD GPUs, Intel GPUs, Embedded\n  dependencies: llama-cpp-python\n---\n\n# llama.cpp\n\nPure C\u002FC++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.\n\n## When to use llama.cpp\n\n**Use llama.cpp when:**\n- Running on CPU-only machines\n- Deploying on Apple Silicon (M1\u002FM2\u002FM3\u002FM4)\n- Using AMD or Intel GPUs (no CUDA)\n- Edge deployment (Raspberry Pi, embedded systems)\n- Need simple deployment without Docker\u002FPython\n\n**Use TensorRT-LLM instead when:**\n- Have NVIDIA GPUs (A100\u002FH100)\n- Need maximum throughput (100K+ tok\u002Fs)\n- Running in datacenter with CUDA\n\n**Use vLLM instead when:**\n- Have NVIDIA GPUs\n- Need Python-first API\n- Want PagedAttention\n\n## Quick start\n\n### Installation\n\n```bash\n# macOS\u002FLinux\nbrew install llama.cpp\n\n# Or build from source\ngit clone https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\ncd llama.cpp\nmake\n\n# With Metal (Apple Silicon)\nmake LLAMA_METAL=1\n\n# With CUDA (NVIDIA)\nmake LLAMA_CUDA=1\n\n# With ROCm (AMD)\nmake LLAMA_HIP=1\n```\n\n### Download model\n\n```bash\n# Download from HuggingFace (GGUF format)\nhuggingface-cli download \\\n    TheBloke\u002FLlama-2-7B-Chat-GGUF \\\n    llama-2-7b-chat.Q4_K_M.gguf \\\n    --local-dir models\u002F\n\n# Or convert from HuggingFace\npython convert_hf_to_gguf.py models\u002Fllama-2-7b-chat\u002F\n```\n\n### Run inference\n\n```bash\n# Simple chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    -p \"Explain quantum computing\" \\\n    -n 256  # Max tokens\n\n# Interactive chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --interactive\n```\n\n### Server mode\n\n```bash\n# Start OpenAI-compatible server\n.\u002Fllama-server \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 32  # Offload 32 layers to GPU\n\n# Client request\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"llama-2-7b-chat\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Quantization formats\n\n### GGUF format overview\n\n| Format | Bits | Size (7B) | Speed | Quality | Use Case |\n|--------|------|-----------|-------|---------|----------|\n| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |\n| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |\n| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |\n| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |\n| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |\n| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |\n\n### Choosing quantization\n\n```bash\n# General use (balanced)\nQ4_K_M  # 4-bit, medium quality\n\n# Maximum speed (more degradation)\nQ2_K or Q3_K_M\n\n# Maximum quality (slower)\nQ6_K or Q8_0\n\n# Very large models (70B, 405B)\nQ3_K_M or Q4_K_S  # Lower bits to fit in memory\n```\n\n## Hardware acceleration\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake LLAMA_METAL=1\n\n# Run with GPU acceleration (automatic)\n.\u002Fllama-cli -m model.gguf -ngl 999  # Offload all layers\n\n# Performance: M3 Max 40-60 tokens\u002Fsec (Llama 2-7B Q4_K_M)\n```\n\n### NVIDIA GPUs (CUDA)\n\n```bash\n# Build with CUDA\nmake LLAMA_CUDA=1\n\n# Offload layers to GPU\n.\u002Fllama-cli -m model.gguf -ngl 35  # Offload 35\u002F40 layers\n\n# Hybrid CPU+GPU for large models\n.\u002Fllama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest\n```\n\n### AMD GPUs (ROCm)\n\n```bash\n# Build with ROCm\nmake LLAMA_HIP=1\n\n# Run with AMD GPU\n.\u002Fllama-cli -m model.gguf -ngl 999\n```\n\n## Common patterns\n\n### Batch processing\n\n```bash\n# Process multiple prompts from file\ncat prompts.txt | .\u002Fllama-cli \\\n    -m model.gguf \\\n    --batch-size 512 \\\n    -n 100\n```\n\n### Constrained generation\n\n```bash\n# JSON output with grammar\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -p \"Generate a person: \" \\\n    --grammar-file grammars\u002Fjson.gbnf\n\n# Outputs valid JSON only\n```\n\n### Context size\n\n```bash\n# Increase context (default 512)\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -c 4096  # 4K context window\n\n# Very long context (if model supports)\n.\u002Fllama-cli -m model.gguf -c 32768  # 32K context\n```\n\n## Performance benchmarks\n\n### CPU performance (Llama 2-7B Q4_K_M)\n\n| CPU | Threads | Speed | Cost |\n|-----|---------|-------|------|\n| Apple M3 Max | 16 | 50 tok\u002Fs | $0 (local) |\n| AMD Ryzen 9 7950X | 32 | 35 tok\u002Fs | $0.50\u002Fhour |\n| Intel i9-13900K | 32 | 30 tok\u002Fs | $0.40\u002Fhour |\n| AWS c7i.16xlarge | 64 | 40 tok\u002Fs | $2.88\u002Fhour |\n\n### GPU acceleration (Llama 2-7B Q4_K_M)\n\n| GPU | Speed | vs CPU | Cost |\n|-----|-------|--------|------|\n| NVIDIA RTX 4090 | 120 tok\u002Fs | 3-4× | $0 (local) |\n| NVIDIA A10 | 80 tok\u002Fs | 2-3× | $1.00\u002Fhour |\n| AMD MI250 | 70 tok\u002Fs | 2× | $2.00\u002Fhour |\n| Apple M3 Max (Metal) | 50 tok\u002Fs | ~Same | $0 (local) |\n\n## Supported models\n\n**LLaMA family**:\n- Llama 2 (7B, 13B, 70B)\n- Llama 3 (8B, 70B, 405B)\n- Code Llama\n\n**Mistral family**:\n- Mistral 7B\n- Mixtral 8x7B, 8x22B\n\n**Other**:\n- Falcon, BLOOM, GPT-J\n- Phi-3, Gemma, Qwen\n- LLaVA (vision), Whisper (audio)\n\n**Find models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n\n## References\n\n- **[Quantization Guide](references\u002Fquantization.md)** - GGUF formats, conversion, quality comparison\n- **[Server Deployment](references\u002Fserver.md)** - API endpoints, Docker, monitoring\n- **[Optimization](references\u002Foptimization.md)** - Performance tuning, hybrid CPU+GPU\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fllama-cpp\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm hardware, framework and server versions, model format, and expected load.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Serve or log a single request or run end to end before scaling.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure latency, throughput and output correctness against a reference; check resource usage and costs.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| The server fails to start or OOMs | Check model size versus memory, quantization and parallelism settings. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not expose services or credentials publicly; confirm cloud costs before provisioning.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `gguf-quantization`: GGUF format and llama.cpp quantization for efficient CPU\u002FGPU inference.\n- `tensorrt-llm`: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency.\n- `gptq`: Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers.\n","# llama.cpp\n\nPure C\u002FC++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.\n\n## When to use llama.cpp\n\n**Use llama.cpp when:**\n- Running on CPU-only machines\n- Deploying on Apple Silicon (M1\u002FM2\u002FM3\u002FM4)\n- Using AMD or Intel GPUs (no CUDA)\n- Edge deployment (Raspberry Pi, embedded systems)\n- Need simple deployment without Docker\u002FPython\n\n**Use TensorRT-LLM instead when:**\n- Have NVIDIA GPUs (A100\u002FH100)\n- Need maximum throughput (100K+ tok\u002Fs)\n- Running in datacenter with CUDA\n\n**Use vLLM instead when:**\n- Have NVIDIA GPUs\n- Need Python-first API\n- Want PagedAttention\n\n## Quick start\n\n### Installation\n\n```bash\n# macOS\u002FLinux\nbrew install llama.cpp\n\n# Or build from source\ngit clone https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\ncd llama.cpp\nmake\n\n# With Metal (Apple Silicon)\nmake LLAMA_METAL=1\n\n# With CUDA (NVIDIA)\nmake LLAMA_CUDA=1\n\n# With ROCm (AMD)\nmake LLAMA_HIP=1\n```\n\n### Download model\n\n```bash\n# Download from HuggingFace (GGUF format)\nhuggingface-cli download \\\n    TheBloke\u002FLlama-2-7B-Chat-GGUF \\\n    llama-2-7b-chat.Q4_K_M.gguf \\\n    --local-dir models\u002F\n\n# Or convert from HuggingFace\npython convert_hf_to_gguf.py models\u002Fllama-2-7b-chat\u002F\n```\n\n### Run inference\n\n```bash\n# Simple chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    -p \"Explain quantum computing\" \\\n    -n 256  # Max tokens\n\n# Interactive chat\n.\u002Fllama-cli \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --interactive\n```\n\n### Server mode\n\n```bash\n# Start OpenAI-compatible server\n.\u002Fllama-server \\\n    -m models\u002Fllama-2-7b-chat.Q4_K_M.gguf \\\n    --host 0.0.0.0 \\\n    --port 8080 \\\n    -ngl 32  # Offload 32 layers to GPU\n\n# Client request\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"model\": \"llama-2-7b-chat\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"temperature\": 0.7,\n    \"max_tokens\": 100\n  }'\n```\n\n## Quantization formats\n\n### GGUF format overview\n\n| Format | Bits | Size (7B) | Speed | Quality | Use Case |\n|--------|------|-----------|-------|---------|----------|\n| **Q4_K_M** | 4.5 | 4.1 GB | Fast | Good | **Recommended default** |\n| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |\n| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |\n| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |\n| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |\n| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |\n\n### Choosing quantization\n\n```bash\n# General use (balanced)\nQ4_K_M  # 4-bit, medium quality\n\n# Maximum speed (more degradation)\nQ2_K or Q3_K_M\n\n# Maximum quality (slower)\nQ6_K or Q8_0\n\n# Very large models (70B, 405B)\nQ3_K_M or Q4_K_S  # Lower bits to fit in memory\n```\n\n## Hardware acceleration\n\n### Apple Silicon (Metal)\n\n```bash\n# Build with Metal\nmake LLAMA_METAL=1\n\n# Run with GPU acceleration (automatic)\n.\u002Fllama-cli -m model.gguf -ngl 999  # Offload all layers\n\n# Performance: M3 Max 40-60 tokens\u002Fsec (Llama 2-7B Q4_K_M)\n```\n\n### NVIDIA GPUs (CUDA)\n\n```bash\n# Build with CUDA\nmake LLAMA_CUDA=1\n\n# Offload layers to GPU\n.\u002Fllama-cli -m model.gguf -ngl 35  # Offload 35\u002F40 layers\n\n# Hybrid CPU+GPU for large models\n.\u002Fllama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest\n```\n\n### AMD GPUs (ROCm)\n\n```bash\n# Build with ROCm\nmake LLAMA_HIP=1\n\n# Run with AMD GPU\n.\u002Fllama-cli -m model.gguf -ngl 999\n```\n\n## Common patterns\n\n### Batch processing\n\n```bash\n# Process multiple prompts from file\ncat prompts.txt | .\u002Fllama-cli \\\n    -m model.gguf \\\n    --batch-size 512 \\\n    -n 100\n```\n\n### Constrained generation\n\n```bash\n# JSON output with grammar\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -p \"Generate a person: \" \\\n    --grammar-file grammars\u002Fjson.gbnf\n\n# Outputs valid JSON only\n```\n\n### Context size\n\n```bash\n# Increase context (default 512)\n.\u002Fllama-cli \\\n    -m model.gguf \\\n    -c 4096  # 4K context window\n\n# Very long context (if model supports)\n.\u002Fllama-cli -m model.gguf -c 32768  # 32K context\n```\n\n## Performance benchmarks\n\n### CPU performance (Llama 2-7B Q4_K_M)\n\n| CPU | Threads | Speed | Cost |\n|-----|---------|-------|------|\n| Apple M3 Max | 16 | 50 tok\u002Fs | $0 (local) |\n| AMD Ryzen 9 7950X | 32 | 35 tok\u002Fs | $0.50\u002Fhour |\n| Intel i9-13900K | 32 | 30 tok\u002Fs | $0.40\u002Fhour |\n| AWS c7i.16xlarge | 64 | 40 tok\u002Fs | $2.88\u002Fhour |\n\n### GPU acceleration (Llama 2-7B Q4_K_M)\n\n| GPU | Speed | vs CPU | Cost |\n|-----|-------|--------|------|\n| NVIDIA RTX 4090 | 120 tok\u002Fs | 3-4× | $0 (local) |\n| NVIDIA A10 | 80 tok\u002Fs | 2-3× | $1.00\u002Fhour |\n| AMD MI250 | 70 tok\u002Fs | 2× | $2.00\u002Fhour |\n| Apple M3 Max (Metal) | 50 tok\u002Fs | ~Same | $0 (local) |\n\n## Supported models\n\n**LLaMA family**:\n- Llama 2 (7B, 13B, 70B)\n- Llama 3 (8B, 70B, 405B)\n- Code Llama\n\n**Mistral family**:\n- Mistral 7B\n- Mixtral 8x7B, 8x22B\n\n**Other**:\n- Falcon, BLOOM, GPT-J\n- Phi-3, Gemma, Qwen\n- LLaVA (vision), Whisper (audio)\n\n**Find models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n\n## References\n\n- **[Quantization Guide](references\u002Fquantization.md)** - GGUF formats, conversion, quality comparison\n- **[Server Deployment](references\u002Fserver.md)** - API endpoints, Docker, monitoring\n- **[Optimization](references\u002Foptimization.md)** - Performance tuning, hybrid CPU+GPU\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp\n- **Models**: https:\u002F\u002Fhuggingface.co\u002Fmodels?library=gguf\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fllama-cpp\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm hardware, framework and server versions, model format, and expected load.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Serve or log a single request or run end to end before scaling.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure latency, throughput and output correctness against a reference; check resource usage and costs.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| The server fails to start or OOMs | Check model size versus memory, quantization and parallelism settings. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. 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If something cannot be run or verified, say so plainly.\n- Do not expose services or credentials publicly; confirm cloud costs before provisioning.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `gguf-quantization`: GGUF format and llama.cpp quantization for efficient CPU\u002FGPU inference.\n- `tensorrt-llm`: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency.\n- `gptq`: Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers.\n",{"name":5,"license":15,"metadata":45,"description":7},{"tags":46,"version":47,"category":48,"maintainer":49,"dependencies":50},"Inference Serving, Llama.cpp, CPU Inference, Apple Silicon, Edge Deployment, GGUF, Quantization, Non-NVIDIA, AMD GPUs, Intel GPUs, Embedded","1.0.0","ml-inference-and-ops","Kalaris Labs","llama-cpp-python","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fllama-cpp",474,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 14:40:14",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill llama-cpp",{"tool":67,"command":68},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill llama-cpp",{"tool":70,"command":71},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill llama-cpp","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fllama-cpp\u002FSKILL.md",true,[75,81,85],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Foptimization.md","REFERENCE","参考文档",1659,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fllama-cpp\u002Freferences\u002Foptimization.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fquantization.md",4956,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fllama-cpp\u002Freferences\u002Fquantization.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fserver.md",2259,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fllama-cpp\u002Freferences\u002Fserver.md",[90,99,106,113,138,163],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":98},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":98},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":98},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":40},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[120,121,122,123,124,33,125,126,127,128,129,23,130,131,132,133,134,135,136,137],"search","deja search","never","error","ready","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":94,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":40},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[145,146,147,133,148,149,150,151,152,153,154,155,156,157,33,158,159,160,161,162],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"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":94,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":180,"publishedAt":40},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[169,174,177],{"categoryNo":170,"categoryName":171,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":175,"categoryName":176,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":178,"categoryName":179,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000010","后端与接口",[165,181,182,183,184,185,186,159,187,125,188,189,190,191,23,192,193,194,195,196],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]