llm-ai
4K
founderclaw
FounderClaw — Multi-agent engineering team for OpenClaw. 29 skills, 6 agents (CEO + 5 departments), structured workspace, auto mode, vision sub-agent routing. Build startups with AI agents.
openclaw
data-ai
结果数
53,183
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machine-learning
4K
lead-scoring
AI-powered B2B lead scoring model. Predicts conversion probability for potential customers using machine learning (LightGBM + SHAP). CSV upload or API integration.
openclaw
data-ai
machine-learning
4K
senior-computer-vision
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.
openclaw
data-ai
machine-learning
4K
ima-sevio-ai-generation
IMA model generation with exactly two Sevio models: Ima Sevio 1.0 and Ima Sevio 1.0-Fast. Supports text-to-video, image-to-video, first-last-frame, and reference-image workflows. Keeps the same API flow, reflection retry mechanism, and interface contract as ima-video-ai. Requires IMA API key.
openclaw
data-ai
machine-learning
4K
ima-ai-video-generator
AI video generator with premier models: Wan 2.6, Kling O1/2.6, Google Veo 3.1, Sora 2 Pro, Pixverse V5.5, Hailuo 2.0/2.3, SeeDance 1.5 Pro, Vidu Q2. Video generator supporting text-to-video, image-to-video, first-last-frame, and reference-image video generation modes. Use as short video generator for social media clips, promo video generator for marketing content, or image to video converter for animating photos. AI video generation with character consistency via reference images, multi-shot production, and knowledge base guidance via ima-knowledge-ai. Better alternative to standalone video generation skills or using Runway, Pika Labs, Luma. Requires IMA_API_KEY.
openclaw
data-ai
machine-learning
4K
cognitive-flexibility
Cognitive Flexibility Skill - AI cognitive flexibility with 4 modes. Supports automatic mode switching and metacognitive monitoring. Use when: - Complex reasoning and multi-step thinking needed - Self-assessment and reflection required - Cross-scenario knowledge transfer - Creative problem solving - Task complexity > medium (estimated >2 hours)
openclaw
data-ai
machine-learning
4K
pydantic-ai-model-integration
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
openclaw
data-ai
machine-learning
4K
elpa
Orchestrate real ELPA-style ensemble forecasting workflows by triggering external sub-model training jobs (for example PyTorch/Prophet/TiDE/transformers), then computing ELPA online/offline weights from validation errors. Use when you need production-oriented ensemble training instead of lightweight simulation adapters.
openclaw
data-ai
machine-learning
4K
hair-cam-anno
安防摄像头视频 VL 模型微调数据集标注工具。用于从安防摄像头视频中提取关键帧、分析视频内容、生成结构化标注（含环境/人物/行为/风险描述），并输出符合 dataset.jsonl 格式的微调训练数据。Use when 用户需要对安防摄像头视频进行数据标注、生成 VL 模型训练数据集、处理 /root/hair-cam 目录下的视频数据，或提及 "hair-cam"、"数据标注"、"视频标注"、"VL模型微调"。
openclaw
data-ai
machine-learning
4K
robotics-vla
Expert guidance for Vision-Language-Action (VLA) robot foundation models — covering architecture design, training pipelines, data strategy, deployment, and evaluation. Use when (1) designing or implementing a generalist robot policy (VLA model), (2) setting up pre-training or fine-tuning pipelines for robot manipulation, (3) choosing action representations (flow matching vs. diffusion vs. autoregressive), (4) structuring multi-embodiment robot datasets, (5) evaluating dexterous manipulation tasks, (6) implementing action chunking or high-level policy decomposition. Based on the pi0 architecture (Physical Intelligence, 2024).
openclaw
data-ai
machine-learning
4K
auto-dream
Memory consolidation skill that replicates Anthropic's Auto Dream feature. Runs a 4-phase reflective pass over memory files: Orient → Gather → Merge → Prune. Use when: (1) Context window feels cluttered with stale info, (2) After long coding sessions, (3) Manually triggered with /dream, (4) Automatically after daily-reflection. Keeps memories tight, removes contradictions, converts relative dates to absolute.
openclaw
data-ai
machine-learning
4K
gear-ratio-mechanical-drive-calculator
Use when calculating gear ratios, converting RPM between shafts, computing torque output, analyzing drivetrain configurations, or selecting motors for mechanical systems.
openclaw
data-ai
machine-learning
4K
afrexai-cybersecurity-engine
Complete cybersecurity assessment, threat modeling, and hardening system. Use when conducting security audits, threat modeling, penetration testing, incident response, or building security programs from scratch. Works with any stack — zero external dependencies.
openclaw
data-ai
machine-learning
4K
value-mining-lengthybooks
Extract actionable insights from books using Four-Layer Methodology: (1) Skeleton - conceptual frameworks and mental models, (2) Flesh - 2-3 detailed case studies including original examples, cross-industry analogies, and real-world applications, (3) Essence - cross-industry migration matrices with specific industry adaptations and 3-5 step executable SOPs, (4) Residue - critical analysis of boundaries, limitations, and failure conditions. Dual processing modes: Quick (5 core points, 10-15 min) for rapid assessment and Deep (10-20 comprehensive points, 30-45 min) for systematic learning. Includes Feynman validation testing with scenario-based problems and scoring rubrics. Generates structured reports in Markdown/PDF/Word formats. Use when user requests systematic knowledge extraction, concept distillation, or implementation guidance from methodology/business/psychology/self-help books with emphasis on practical application and cross-domain transfer.
openclaw
data-ai