[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6M9T4NDB8RCR7FD7STSN15F":3,"skill-same:01M6M9T4NDB8RCR7FD7STSN15F":93},{"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":55,"commitSha":56,"skillDirectory":57,"forksCount":58,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6M9T4NDB8RCR7FD7STSN15F","blip-2-vision-language",null,"Explains how to use Salesforce BLIP-2 (Q-Former bridging a frozen image encoder and an LLM such as OPT or FlanT5) through HuggingFace Transformers and LAVIS for image captioning, visual question answering, image-text matching, and feature extraction. Use when generating captions for images, building a VQA system, doing zero-shot image-text understanding without task-specific training, matching or retrieving images against text, or fitting a BLIP-2 model into limited GPU memory with INT8\u002FINT4 quantization. Prefer LLaVA or InstructBLIP for instruction-following multimodal chat, and CLIP for plain image-text similarity.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1566,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fblip-2-vision-language\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"blip 2","blip 2 vision language","vision","language","for","image","pillow","Visual Question Answering","Vision-Language","INT8","as","salesforce","clip","question","text","system","how","Captions","multimodal","training","2026-11-10 10:12:30",false,"---\nname: blip-2-vision-language\ndescription: Explains how to use Salesforce BLIP-2 (Q-Former bridging a frozen image encoder and an LLM such as OPT or FlanT5) through HuggingFace Transformers and LAVIS for image captioning, visual question answering, image-text matching, and feature extraction. Use when generating captions for images, building a VQA system, doing zero-shot image-text understanding without task-specific training, matching or retrieving images against text, or fitting a BLIP-2 model into limited GPU memory with INT8\u002FINT4 quantization. Prefer LLaVA or InstructBLIP for instruction-following multimodal chat, and CLIP for plain image-text similarity.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: multimodal-and-emerging\n  maintainer: Kalaris Labs\n  tags: Multimodal, Vision-Language, Image Captioning, VQA, Zero-Shot\n  dependencies: transformers>=4.30.0, torch>=1.10.0, Pillow\n---\n\n# BLIP-2: Vision-Language Pre-training\n\nComprehensive guide to using Salesforce's BLIP-2 for vision-language tasks with frozen image encoders and large language models.\n\n## When to use BLIP-2\n\n**Use BLIP-2 when:**\n- Need high-quality image captioning with natural descriptions\n- Building visual question answering (VQA) systems\n- Require zero-shot image-text understanding without task-specific training\n- Want to leverage LLM reasoning for visual tasks\n- Building multimodal conversational AI\n- Need image-text retrieval or matching\n\n**Key features:**\n- **Q-Former architecture**: Lightweight query transformer bridges vision and language\n- **Frozen backbone efficiency**: No need to fine-tune large vision\u002Flanguage models\n- **Multiple LLM backends**: OPT (2.7B, 6.7B) and FlanT5 (XL, XXL)\n- **Zero-shot capabilities**: Strong performance without task-specific training\n- **Efficient training**: Only trains Q-Former (~188M parameters)\n- **State-of-the-art results**: Beats larger models on VQA benchmarks\n\n**Use alternatives instead:**\n- **LLaVA**: For instruction-following multimodal chat\n- **InstructBLIP**: For improved instruction-following (BLIP-2 successor)\n- **GPT-4V\u002FClaude 3**: For production multimodal chat (proprietary)\n- **CLIP**: For simple image-text similarity without generation\n- **Flamingo**: For few-shot visual learning\n\n## Quick start\n\n### Installation\n\n```bash\n# HuggingFace Transformers (recommended)\npip install transformers accelerate torch Pillow\n\n# Or LAVIS library (Salesforce official)\npip install salesforce-lavis\n```\n\n### Basic image captioning\n\n```python\nimport torch\nfrom PIL import Image\nfrom transformers import Blip2Processor, Blip2ForConditionalGeneration\n\n# Load model and processor\nprocessor = Blip2Processor.from_pretrained(\"Salesforce\u002Fblip2-opt-2.7b\")\nmodel = Blip2ForConditionalGeneration.from_pretrained(\n    \"Salesforce\u002Fblip2-opt-2.7b\",\n    torch_dtype=torch.float16,\n    device_map=\"auto\"\n)\n\n# Load image\nimage = Image.open(\"photo.jpg\").convert(\"RGB\")\n\n# Generate caption\ninputs = processor(images=image, return_tensors=\"pt\").to(\"cuda\", torch.float16)\ngenerated_ids = model.generate(**inputs, max_new_tokens=50)\ncaption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\nprint(caption)\n```\n\n### Visual question answering\n\n```python\n# Ask a question about the image\nquestion = \"What color is the car in this image?\"\n\ninputs = processor(images=image, text=question, return_tensors=\"pt\").to(\"cuda\", torch.float16)\ngenerated_ids = model.generate(**inputs, max_new_tokens=50)\nanswer = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\nprint(answer)\n```\n\n### Using LAVIS library\n\n```python\nimport torch\nfrom lavis.models import load_model_and_preprocess\nfrom PIL import Image\n\n# Load model\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel, vis_processors, txt_processors = load_model_and_preprocess(\n    name=\"blip2_opt\",\n    model_type=\"pretrain_opt2.7b\",\n    is_eval=True,\n    device=device\n)\n\n# Process image\nimage = Image.open(\"photo.jpg\").convert(\"RGB\")\nimage = vis_processors[\"eval\"](image).unsqueeze(0).to(device)\n\n# Caption\ncaption = model.generate({\"image\": image})\nprint(caption)\n\n# VQA\nquestion = txt_processors[\"eval\"](\"What is in this image?\")\nanswer = model.generate({\"image\": image, \"prompt\": question})\nprint(answer)\n```\n\n## Core concepts\n\n### Architecture overview\n\n```\nBLIP-2 Architecture:\n┌─────────────────────────────────────────────────────────────┐\n│                        Q-Former                              │\n│  ┌─────────────────────────────────────────────────────┐    │\n│  │     Learned Queries (32 queries × 768 dim)          │    │\n│  └────────────────────────┬────────────────────────────┘    │\n│                           │                                  │\n│  ┌────────────────────────▼────────────────────────────┐    │\n│  │    Cross-Attention with Image Features               │    │\n│  └────────────────────────┬────────────────────────────┘    │\n│                           │                                  │\n│  ┌────────────────────────▼────────────────────────────┐    │\n│  │    Self-Attention Layers (Transformer)               │    │\n│  └────────────────────────┬────────────────────────────┘    │\n└───────────────────────────┼─────────────────────────────────┘\n                            │\n┌───────────────────────────▼─────────────────────────────────┐\n│  Frozen Vision Encoder    │      Frozen LLM                  │\n│  (ViT-G\u002F14 from EVA-CLIP) │      (OPT or FlanT5)            │\n└─────────────────────────────────────────────────────────────┘\n```\n\n### Model variants\n\n| Model | LLM Backend | Size | Use Case |\n|-------|-------------|------|----------|\n| `blip2-opt-2.7b` | OPT-2.7B | ~4GB | General captioning, VQA |\n| `blip2-opt-6.7b` | OPT-6.7B | ~8GB | Better reasoning |\n| `blip2-flan-t5-xl` | FlanT5-XL | ~5GB | Instruction following |\n| `blip2-flan-t5-xxl` | FlanT5-XXL | ~13GB | Best quality |\n\n### Q-Former components\n\n| Component | Description | Parameters |\n|-----------|-------------|------------|\n| Learned queries | Fixed set of learnable embeddings | 32 × 768 |\n| Image transformer | Cross-attention to vision features | ~108M |\n| Text transformer | Self-attention for text | ~108M |\n| Linear projection | Maps to LLM dimension | Varies |\n\n## Advanced usage\n\nDetails, code examples and parameter tables: [references\u002Fadvanced-usage-2.md](references\u002Fadvanced-usage-2.md). Read it when this step applies.\n\n## Common workflows\n\n### Workflow 1: Image captioning pipeline\n\n```python\nimport torch\nfrom PIL import Image\nfrom transformers import Blip2Processor, Blip2ForConditionalGeneration\nfrom pathlib import Path\n\nclass ImageCaptioner:\n    def __init__(self, model_name=\"Salesforce\u002Fblip2-opt-2.7b\"):\n        self.processor = Blip2Processor.from_pretrained(model_name)\n        self.model = Blip2ForConditionalGeneration.from_pretrained(\n            model_name,\n            torch_dtype=torch.float16,\n            device_map=\"auto\"\n        )\n\n    def caption(self, image_path: str, prompt: str = None) -> str:\n        image = Image.open(image_path).convert(\"RGB\")\n\n        if prompt:\n            inputs = self.processor(images=image, text=prompt, return_tensors=\"pt\")\n        else:\n            inputs = self.processor(images=image, return_tensors=\"pt\")\n\n        inputs = inputs.to(\"cuda\", torch.float16)\n\n        generated_ids = self.model.generate(\n            **inputs,\n            max_new_tokens=50,\n            num_beams=5\n        )\n\n        return self.processor.decode(generated_ids[0], skip_special_tokens=True)\n\n    def caption_batch(self, image_paths: list, prompt: str = None) -> list:\n        images = [Image.open(p).convert(\"RGB\") for p in image_paths]\n\n        if prompt:\n            inputs = self.processor(\n                images=images,\n                text=[prompt] * len(images),\n                return_tensors=\"pt\",\n                padding=True\n            )\n        else:\n            inputs = self.processor(images=images, return_tensors=\"pt\", padding=True)\n\n        inputs = inputs.to(\"cuda\", torch.float16)\n\n        generated_ids = self.model.generate(**inputs, max_new_tokens=50)\n        return self.processor.batch_decode(generated_ids, skip_special_tokens=True)\n\n# Usage\ncaptioner = ImageCaptioner()\n\n# Single image\ncaption = captioner.caption(\"photo.jpg\")\nprint(f\"Caption: {caption}\")\n\n# With prompt for style\ncaption = captioner.caption(\"photo.jpg\", \"a detailed description of\")\nprint(f\"Detailed: {caption}\")\n\n# Batch processing\ncaptions = captioner.caption_batch([\"img1.jpg\", \"img2.jpg\", \"img3.jpg\"])\nfor i, cap in enumerate(captions):\n    print(f\"Image {i+1}: {cap}\")\n```\n\n### Workflow 2: Visual Q&A system\n\n```python\nclass VisualQA:\n    def __init__(self, model_name=\"Salesforce\u002Fblip2-flan-t5-xl\"):\n        self.processor = Blip2Processor.from_pretrained(model_name)\n        self.model = Blip2ForConditionalGeneration.from_pretrained(\n            model_name,\n            torch_dtype=torch.float16,\n            device_map=\"auto\"\n        )\n        self.current_image = None\n        self.current_inputs = None\n\n    def set_image(self, image_path: str):\n        \"\"\"Load image for multiple questions.\"\"\"\n        self.current_image = Image.open(image_path).convert(\"RGB\")\n\n    def ask(self, question: str) -> str:\n        \"\"\"Ask a question about the current image.\"\"\"\n        if self.current_image is None:\n            raise ValueError(\"No image set. Call set_image() first.\")\n\n        # Format question for FlanT5\n        prompt = f\"Question: {question} Answer:\"\n\n        inputs = self.processor(\n            images=self.current_image,\n            text=prompt,\n            return_tensors=\"pt\"\n        ).to(\"cuda\", torch.float16)\n\n        generated_ids = self.model.generate(\n            **inputs,\n            max_new_tokens=50,\n            num_beams=5\n        )\n\n        return self.processor.decode(generated_ids[0], skip_special_tokens=True)\n\n    def ask_multiple(self, questions: list) -> dict:\n        \"\"\"Ask multiple questions about current image.\"\"\"\n        return {q: self.ask(q) for q in questions}\n\n# Usage\nvqa = VisualQA()\nvqa.set_image(\"scene.jpg\")\n\n# Ask questions\nprint(vqa.ask(\"What objects are in this image?\"))\nprint(vqa.ask(\"What is the weather like?\"))\nprint(vqa.ask(\"How many people are there?\"))\n\n# Batch questions\nresults = vqa.ask_multiple([\n    \"What is the main subject?\",\n    \"What colors are dominant?\",\n    \"Is this indoors or outdoors?\"\n])\n```\n\n### Workflow 3: Image search\u002Fretrieval\n\n```python\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom lavis.models import load_model_and_preprocess\n\nclass ImageSearchEngine:\n    def __init__(self):\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        self.model, self.vis_processors, self.txt_processors = load_model_and_preprocess(\n            name=\"blip2_feature_extractor\",\n            model_type=\"pretrain\",\n            is_eval=True,\n            device=self.device\n        )\n        self.image_features = []\n        self.image_paths = []\n\n    def index_images(self, image_paths: list):\n        \"\"\"Build index from images.\"\"\"\n        self.image_paths = image_paths\n\n        for path in image_paths:\n            image = Image.open(path).convert(\"RGB\")\n            image = self.vis_processors[\"eval\"](image).unsqueeze(0).to(self.device)\n\n            with torch.no_grad():\n                features = self.model.extract_features({\"image\": image}, mode=\"image\")\n                # Use projected features for matching\n                self.image_features.append(\n                    features.image_embeds_proj.mean(dim=1).cpu().numpy()\n                )\n\n        self.image_features = np.vstack(self.image_features)\n\n    def search(self, query: str, top_k: int = 5) -> list:\n        \"\"\"Search images by text query.\"\"\"\n        # Get text features\n        text = self.txt_processors[\"eval\"](query)\n        text_input = {\"text_input\": [text]}\n\n        with torch.no_grad():\n            text_features = self.model.extract_features(text_input, mode=\"text\")\n            text_embeds = text_features.text_embeds_proj[:, 0].cpu().numpy()\n\n        # Compute similarities\n        similarities = np.dot(self.image_features, text_embeds.T).squeeze()\n        top_indices = np.argsort(similarities)[::-1][:top_k]\n\n        return [(self.image_paths[i], similarities[i]) for i in top_indices]\n\n# Usage\nengine = ImageSearchEngine()\nengine.index_images([\"img1.jpg\", \"img2.jpg\", \"img3.jpg\", ...])\n\n# Search\nresults = engine.search(\"a sunset over the ocean\", top_k=5)\nfor path, score in results:\n    print(f\"{path}: {score:.3f}\")\n```\n\n## Output format\n\n### Generation output\n\n```python\n# Direct generation returns token IDs\ngenerated_ids = model.generate(**inputs, max_new_tokens=50)\n# Shape: [batch_size, sequence_length]\n\n# Decode to text\ntext = processor.batch_decode(generated_ids, skip_special_tokens=True)\n# Returns: list of strings\n```\n\n### Feature extraction output\n\n```python\n# Q-Former outputs\nfeatures = model.extract_features({\"image\": image}, mode=\"image\")\n\nfeatures.image_embeds          # [B, 32, 768] - Q-Former outputs\nfeatures.image_embeds_proj     # [B, 32, 256] - Projected for matching\nfeatures.text_embeds          # [B, seq_len, 768] - Text features\nfeatures.text_embeds_proj     # [B, 256] - Projected text (CLS)\n```\n\n## Performance optimization\n\n### GPU memory requirements\n\n| Model | FP16 VRAM | INT8 VRAM | INT4 VRAM |\n|-------|-----------|-----------|-----------|\n| blip2-opt-2.7b | ~8GB | ~5GB | ~3GB |\n| blip2-opt-6.7b | ~16GB | ~9GB | ~5GB |\n| blip2-flan-t5-xl | ~10GB | ~6GB | ~4GB |\n| blip2-flan-t5-xxl | ~26GB | ~14GB | ~8GB |\n\n### Speed optimization\n\n```python\n# Use Flash Attention if available\nmodel = Blip2ForConditionalGeneration.from_pretrained(\n    \"Salesforce\u002Fblip2-opt-2.7b\",\n    torch_dtype=torch.float16,\n    attn_implementation=\"flash_attention_2\",  # Requires flash-attn\n    device_map=\"auto\"\n)\n\n# Compile model (PyTorch 2.0+)\nmodel = torch.compile(model)\n\n# Use smaller images (if quality allows)\nprocessor = Blip2Processor.from_pretrained(\"Salesforce\u002Fblip2-opt-2.7b\")\n# Default is 224x224, which is optimal\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| CUDA OOM | Use INT8\u002FINT4 quantization, smaller model |\n| Slow generation | Use greedy decoding, reduce max_new_tokens |\n| Poor captions | Try FlanT5 variant, use prompts |\n| Hallucinations | Lower temperature, use beam search |\n| Wrong answers | Rephrase question, provide context |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Fine-tuning, integration, deployment\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12597\n- **GitHub (LAVIS)**: https:\u002F\u002Fgithub.com\u002Fsalesforce\u002FLAVIS\n- **HuggingFace**: https:\u002F\u002Fhuggingface.co\u002FSalesforce\u002Fblip2-opt-2.7b\n- **Demo**: https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FSalesforce\u002FBLIP2\n- **InstructBLIP**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06500 (successor)\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm GPU memory, model checkpoint and licence, framework versions and input formats.\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.** Run inference on a single sample at low resolution or short length first.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Inspect outputs qualitatively and with task metrics; check licence restrictions for generated content.\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| Out-of-memory on large inputs | Reduce resolution or sequence length, use half precision, or tile the input. |\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- Check model and dataset licences before commercial or published use.\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- `clip`: OpenAI's model connecting vision and language.\n- `llava`: Large Language and Vision Assistant.\n- `quantizing-models-bitsandbytes`: Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.\n","# BLIP-2: Vision-Language Pre-training\n\nComprehensive guide to using Salesforce's BLIP-2 for vision-language tasks with frozen image encoders and large language models.\n\n## When to use BLIP-2\n\n**Use BLIP-2 when:**\n- Need high-quality image captioning with natural descriptions\n- Building visual question answering (VQA) systems\n- Require zero-shot image-text understanding without task-specific training\n- Want to leverage LLM reasoning for visual tasks\n- Building multimodal conversational AI\n- Need image-text retrieval or matching\n\n**Key features:**\n- **Q-Former architecture**: Lightweight query transformer bridges vision and language\n- **Frozen backbone efficiency**: No need to fine-tune large vision\u002Flanguage models\n- **Multiple LLM backends**: OPT (2.7B, 6.7B) and FlanT5 (XL, XXL)\n- **Zero-shot capabilities**: Strong performance without task-specific training\n- **Efficient training**: Only trains Q-Former (~188M parameters)\n- **State-of-the-art results**: Beats larger models on VQA benchmarks\n\n**Use alternatives instead:**\n- **LLaVA**: For instruction-following multimodal chat\n- **InstructBLIP**: For improved instruction-following (BLIP-2 successor)\n- **GPT-4V\u002FClaude 3**: For production multimodal chat (proprietary)\n- **CLIP**: For simple image-text similarity without generation\n- **Flamingo**: For few-shot visual learning\n\n## Quick start\n\n### Installation\n\n```bash\n# HuggingFace Transformers (recommended)\npip install transformers accelerate torch Pillow\n\n# Or LAVIS library (Salesforce official)\npip install salesforce-lavis\n```\n\n### Basic image captioning\n\n```python\nimport torch\nfrom PIL import Image\nfrom transformers import Blip2Processor, Blip2ForConditionalGeneration\n\n# Load model and processor\nprocessor = Blip2Processor.from_pretrained(\"Salesforce\u002Fblip2-opt-2.7b\")\nmodel = Blip2ForConditionalGeneration.from_pretrained(\n    \"Salesforce\u002Fblip2-opt-2.7b\",\n    torch_dtype=torch.float16,\n    device_map=\"auto\"\n)\n\n# Load image\nimage = Image.open(\"photo.jpg\").convert(\"RGB\")\n\n# Generate caption\ninputs = processor(images=image, return_tensors=\"pt\").to(\"cuda\", torch.float16)\ngenerated_ids = model.generate(**inputs, max_new_tokens=50)\ncaption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\nprint(caption)\n```\n\n### Visual question answering\n\n```python\n# Ask a question about the image\nquestion = \"What color is the car in this image?\"\n\ninputs = processor(images=image, text=question, return_tensors=\"pt\").to(\"cuda\", torch.float16)\ngenerated_ids = model.generate(**inputs, max_new_tokens=50)\nanswer = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\nprint(answer)\n```\n\n### Using LAVIS library\n\n```python\nimport torch\nfrom lavis.models import load_model_and_preprocess\nfrom PIL import Image\n\n# Load model\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel, vis_processors, txt_processors = load_model_and_preprocess(\n    name=\"blip2_opt\",\n    model_type=\"pretrain_opt2.7b\",\n    is_eval=True,\n    device=device\n)\n\n# Process image\nimage = Image.open(\"photo.jpg\").convert(\"RGB\")\nimage = vis_processors[\"eval\"](image).unsqueeze(0).to(device)\n\n# Caption\ncaption = model.generate({\"image\": image})\nprint(caption)\n\n# VQA\nquestion = txt_processors[\"eval\"](\"What is in this image?\")\nanswer = model.generate({\"image\": image, \"prompt\": question})\nprint(answer)\n```\n\n## Core concepts\n\n### Architecture overview\n\n```\nBLIP-2 Architecture:\n┌─────────────────────────────────────────────────────────────┐\n│                        Q-Former                              │\n│  ┌─────────────────────────────────────────────────────┐    │\n│  │     Learned Queries (32 queries × 768 dim)          │    │\n│  └────────────────────────┬────────────────────────────┘    │\n│                           │                                  │\n│  ┌────────────────────────▼────────────────────────────┐    │\n│  │    Cross-Attention with Image Features               │    │\n│  └────────────────────────┬────────────────────────────┘    │\n│                           │                                  │\n│  ┌────────────────────────▼────────────────────────────┐    │\n│  │    Self-Attention Layers (Transformer)               │    │\n│  └────────────────────────┬────────────────────────────┘    │\n└───────────────────────────┼─────────────────────────────────┘\n                            │\n┌───────────────────────────▼─────────────────────────────────┐\n│  Frozen Vision Encoder    │      Frozen LLM                  │\n│  (ViT-G\u002F14 from EVA-CLIP) │      (OPT or FlanT5)            │\n└─────────────────────────────────────────────────────────────┘\n```\n\n### Model variants\n\n| Model | LLM Backend | Size | Use Case |\n|-------|-------------|------|----------|\n| `blip2-opt-2.7b` | OPT-2.7B | ~4GB | General captioning, VQA |\n| `blip2-opt-6.7b` | OPT-6.7B | ~8GB | Better reasoning |\n| `blip2-flan-t5-xl` | FlanT5-XL | ~5GB | Instruction following |\n| `blip2-flan-t5-xxl` | FlanT5-XXL | ~13GB | Best quality |\n\n### Q-Former components\n\n| Component | Description | Parameters |\n|-----------|-------------|------------|\n| Learned queries | Fixed set of learnable embeddings | 32 × 768 |\n| Image transformer | Cross-attention to vision features | ~108M |\n| Text transformer | Self-attention for text | ~108M |\n| Linear projection | Maps to LLM dimension | Varies |\n\n## Advanced usage\n\nDetails, code examples and parameter tables: [references\u002Fadvanced-usage-2.md](references\u002Fadvanced-usage-2.md). Read it when this step applies.\n\n## Common workflows\n\n### Workflow 1: Image captioning pipeline\n\n```python\nimport torch\nfrom PIL import Image\nfrom transformers import Blip2Processor, Blip2ForConditionalGeneration\nfrom pathlib import Path\n\nclass ImageCaptioner:\n    def __init__(self, model_name=\"Salesforce\u002Fblip2-opt-2.7b\"):\n        self.processor = Blip2Processor.from_pretrained(model_name)\n        self.model = Blip2ForConditionalGeneration.from_pretrained(\n            model_name,\n            torch_dtype=torch.float16,\n            device_map=\"auto\"\n        )\n\n    def caption(self, image_path: str, prompt: str = None) -> str:\n        image = Image.open(image_path).convert(\"RGB\")\n\n        if prompt:\n            inputs = self.processor(images=image, text=prompt, return_tensors=\"pt\")\n        else:\n            inputs = self.processor(images=image, return_tensors=\"pt\")\n\n        inputs = inputs.to(\"cuda\", torch.float16)\n\n        generated_ids = self.model.generate(\n            **inputs,\n            max_new_tokens=50,\n            num_beams=5\n        )\n\n        return self.processor.decode(generated_ids[0], skip_special_tokens=True)\n\n    def caption_batch(self, image_paths: list, prompt: str = None) -> list:\n        images = [Image.open(p).convert(\"RGB\") for p in image_paths]\n\n        if prompt:\n            inputs = self.processor(\n                images=images,\n                text=[prompt] * len(images),\n                return_tensors=\"pt\",\n                padding=True\n            )\n        else:\n            inputs = self.processor(images=images, return_tensors=\"pt\", padding=True)\n\n        inputs = inputs.to(\"cuda\", torch.float16)\n\n        generated_ids = self.model.generate(**inputs, max_new_tokens=50)\n        return self.processor.batch_decode(generated_ids, skip_special_tokens=True)\n\n# Usage\ncaptioner = ImageCaptioner()\n\n# Single image\ncaption = captioner.caption(\"photo.jpg\")\nprint(f\"Caption: {caption}\")\n\n# With prompt for style\ncaption = captioner.caption(\"photo.jpg\", \"a detailed description of\")\nprint(f\"Detailed: {caption}\")\n\n# Batch processing\ncaptions = captioner.caption_batch([\"img1.jpg\", \"img2.jpg\", \"img3.jpg\"])\nfor i, cap in enumerate(captions):\n    print(f\"Image {i+1}: {cap}\")\n```\n\n### Workflow 2: Visual Q&A system\n\n```python\nclass VisualQA:\n    def __init__(self, model_name=\"Salesforce\u002Fblip2-flan-t5-xl\"):\n        self.processor = Blip2Processor.from_pretrained(model_name)\n        self.model = Blip2ForConditionalGeneration.from_pretrained(\n            model_name,\n            torch_dtype=torch.float16,\n            device_map=\"auto\"\n        )\n        self.current_image = None\n        self.current_inputs = None\n\n    def set_image(self, image_path: str):\n        \"\"\"Load image for multiple questions.\"\"\"\n        self.current_image = Image.open(image_path).convert(\"RGB\")\n\n    def ask(self, question: str) -> str:\n        \"\"\"Ask a question about the current image.\"\"\"\n        if self.current_image is None:\n            raise ValueError(\"No image set. Call set_image() first.\")\n\n        # Format question for FlanT5\n        prompt = f\"Question: {question} Answer:\"\n\n        inputs = self.processor(\n            images=self.current_image,\n            text=prompt,\n            return_tensors=\"pt\"\n        ).to(\"cuda\", torch.float16)\n\n        generated_ids = self.model.generate(\n            **inputs,\n            max_new_tokens=50,\n            num_beams=5\n        )\n\n        return self.processor.decode(generated_ids[0], skip_special_tokens=True)\n\n    def ask_multiple(self, questions: list) -> dict:\n        \"\"\"Ask multiple questions about current image.\"\"\"\n        return {q: self.ask(q) for q in questions}\n\n# Usage\nvqa = VisualQA()\nvqa.set_image(\"scene.jpg\")\n\n# Ask questions\nprint(vqa.ask(\"What objects are in this image?\"))\nprint(vqa.ask(\"What is the weather like?\"))\nprint(vqa.ask(\"How many people are there?\"))\n\n# Batch questions\nresults = vqa.ask_multiple([\n    \"What is the main subject?\",\n    \"What colors are dominant?\",\n    \"Is this indoors or outdoors?\"\n])\n```\n\n### Workflow 3: Image search\u002Fretrieval\n\n```python\nimport torch\nimport numpy as np\nfrom PIL import Image\nfrom lavis.models import load_model_and_preprocess\n\nclass ImageSearchEngine:\n    def __init__(self):\n        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        self.model, self.vis_processors, self.txt_processors = load_model_and_preprocess(\n            name=\"blip2_feature_extractor\",\n            model_type=\"pretrain\",\n            is_eval=True,\n            device=self.device\n        )\n        self.image_features = []\n        self.image_paths = []\n\n    def index_images(self, image_paths: list):\n        \"\"\"Build index from images.\"\"\"\n        self.image_paths = image_paths\n\n        for path in image_paths:\n            image = Image.open(path).convert(\"RGB\")\n            image = self.vis_processors[\"eval\"](image).unsqueeze(0).to(self.device)\n\n            with torch.no_grad():\n                features = self.model.extract_features({\"image\": image}, mode=\"image\")\n                # Use projected features for matching\n                self.image_features.append(\n                    features.image_embeds_proj.mean(dim=1).cpu().numpy()\n                )\n\n        self.image_features = np.vstack(self.image_features)\n\n    def search(self, query: str, top_k: int = 5) -> list:\n        \"\"\"Search images by text query.\"\"\"\n        # Get text features\n        text = self.txt_processors[\"eval\"](query)\n        text_input = {\"text_input\": [text]}\n\n        with torch.no_grad():\n            text_features = self.model.extract_features(text_input, mode=\"text\")\n            text_embeds = text_features.text_embeds_proj[:, 0].cpu().numpy()\n\n        # Compute similarities\n        similarities = np.dot(self.image_features, text_embeds.T).squeeze()\n        top_indices = np.argsort(similarities)[::-1][:top_k]\n\n        return [(self.image_paths[i], similarities[i]) for i in top_indices]\n\n# Usage\nengine = ImageSearchEngine()\nengine.index_images([\"img1.jpg\", \"img2.jpg\", \"img3.jpg\", ...])\n\n# Search\nresults = engine.search(\"a sunset over the ocean\", top_k=5)\nfor path, score in results:\n    print(f\"{path}: {score:.3f}\")\n```\n\n## Output format\n\n### Generation output\n\n```python\n# Direct generation returns token IDs\ngenerated_ids = model.generate(**inputs, max_new_tokens=50)\n# Shape: [batch_size, sequence_length]\n\n# Decode to text\ntext = processor.batch_decode(generated_ids, skip_special_tokens=True)\n# Returns: list of strings\n```\n\n### Feature extraction output\n\n```python\n# Q-Former outputs\nfeatures = model.extract_features({\"image\": image}, mode=\"image\")\n\nfeatures.image_embeds          # [B, 32, 768] - Q-Former outputs\nfeatures.image_embeds_proj     # [B, 32, 256] - Projected for matching\nfeatures.text_embeds          # [B, seq_len, 768] - Text features\nfeatures.text_embeds_proj     # [B, 256] - Projected text (CLS)\n```\n\n## Performance optimization\n\n### GPU memory requirements\n\n| Model | FP16 VRAM | INT8 VRAM | INT4 VRAM |\n|-------|-----------|-----------|-----------|\n| blip2-opt-2.7b | ~8GB | ~5GB | ~3GB |\n| blip2-opt-6.7b | ~16GB | ~9GB | ~5GB |\n| blip2-flan-t5-xl | ~10GB | ~6GB | ~4GB |\n| blip2-flan-t5-xxl | ~26GB | ~14GB | ~8GB |\n\n### Speed optimization\n\n```python\n# Use Flash Attention if available\nmodel = Blip2ForConditionalGeneration.from_pretrained(\n    \"Salesforce\u002Fblip2-opt-2.7b\",\n    torch_dtype=torch.float16,\n    attn_implementation=\"flash_attention_2\",  # Requires flash-attn\n    device_map=\"auto\"\n)\n\n# Compile model (PyTorch 2.0+)\nmodel = torch.compile(model)\n\n# Use smaller images (if quality allows)\nprocessor = Blip2Processor.from_pretrained(\"Salesforce\u002Fblip2-opt-2.7b\")\n# Default is 224x224, which is optimal\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| CUDA OOM | Use INT8\u002FINT4 quantization, smaller model |\n| Slow generation | Use greedy decoding, reduce max_new_tokens |\n| Poor captions | Try FlanT5 variant, use prompts |\n| Hallucinations | Lower temperature, use beam search |\n| Wrong answers | Rephrase question, provide context |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Fine-tuning, integration, deployment\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.12597\n- **GitHub (LAVIS)**: https:\u002F\u002Fgithub.com\u002Fsalesforce\u002FLAVIS\n- **HuggingFace**: https:\u002F\u002Fhuggingface.co\u002FSalesforce\u002Fblip2-opt-2.7b\n- **Demo**: https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FSalesforce\u002FBLIP2\n- **InstructBLIP**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.06500 (successor)\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm GPU memory, model checkpoint and licence, framework versions and input formats.\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.** Run inference on a single sample at low resolution or short length first.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Inspect outputs qualitatively and with task metrics; check licence restrictions for generated content.\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| Out-of-memory on large inputs | Reduce resolution or sequence length, use half precision, or tile the input. |\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- Check model and dataset licences before commercial or published use.\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- `clip`: OpenAI's model connecting vision and language.\n- `llava`: Large Language and Vision Assistant.\n- `quantizing-models-bitsandbytes`: Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.\n",{"name":5,"license":15,"metadata":49,"description":7},{"tags":50,"version":51,"category":52,"maintainer":53,"dependencies":54},"Multimodal, Vision-Language, Image Captioning, VQA, Zero-Shot","1.0.0","multimodal-and-emerging","Kalaris Labs","transformers>=4.30.0, torch>=1.10.0, Pillow","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fblip-2-vision-language",483,"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.","2026-11-13 02:16:32",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill blip-2-vision-language",{"tool":71,"command":72},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill blip-2-vision-language",{"tool":74,"command":75},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill blip-2-vision-language","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\u002Fblip-2-vision-language\u002FSKILL.md",true,[79,85,89],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fadvanced-usage-2.md","REFERENCE","参考文档",3077,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fblip-2-vision-language\u002Freferences\u002Fadvanced-usage-2.md",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fadvanced-usage.md",18889,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fblip-2-vision-language\u002Freferences\u002Fadvanced-usage.md",{"path":90,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Ftroubleshooting.md",11981,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fblip-2-vision-language\u002Freferences\u002Ftroubleshooting.md",[94,103,110,117,144,169],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":102},"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":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":102},"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":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":102},"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":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":44},"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",[],[124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":44},"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",[],[151,152,153,139,154,155,156,157,158,159,160,161,162,163,129,164,165,166,167,168],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":98,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":182,"publishedAt":44},"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",[175,176,179],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":177,"categoryName":178,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":180,"categoryName":181,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[171,183,184,185,186,187,188,165,189,130,190,191,192,193,135,29,194,195,196,197],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","mcp server","schedule","api","publish"]