{"version": 1, "generated_at": "2026-05-02T10:39:07.529300+00:00", "skill_count": 2029, "skills": [{"name": "1password", "description": "Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.", "source": "official", "identifier": "official/security/1password", "trust_level": "builtin", "repo": "", "path": "security/1password", "tags": ["security", "secrets", "1password", "op", "cli"], "extra": {}}, {"name": "adversarial-ux-test", "description": "Roleplay the most difficult, tech-resistant user for your product. Browse the app as that persona, find every UX pain point, then filter complaints through a pragmatism layer to separate real problems", "source": "official", "identifier": "official/dogfood/adversarial-ux-test", "trust_level": "builtin", "repo": "", "path": "dogfood/adversarial-ux-test", "tags": ["qa", "ux", "testing", "adversarial", "dogfood", "personas", "user-testing"], "extra": {}}, {"name": "agentmail", "description": "Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).", "source": "official", "identifier": "official/email/agentmail", "trust_level": "builtin", "repo": "", "path": "email/agentmail", "tags": ["email", "communication", "agentmail", "mcp"], "extra": {}}, {"name": "base", "description": "Query Base (Ethereum L2) blockchain data with USD pricing \u2014 wallet balances, token info, transaction details, gas analysis, contract inspection, whale detection, and live network stats. Uses Base RPC ", "source": "official", "identifier": "official/blockchain/base", "trust_level": "builtin", "repo": "", "path": "blockchain/base", "tags": ["Base", "Blockchain", "Crypto", "Web3", "RPC", "DeFi", "EVM", "L2", "Ethereum"], "extra": {}}, {"name": "bioinformatics", "description": "Gateway to 400+ bioinformatics skills from bioSkills and ClawBio. Covers genomics, transcriptomics, single-cell, variant calling, pharmacogenomics, metagenomics, structural biology, and more. Fetches ", "source": "official", "identifier": "official/research/bioinformatics", "trust_level": "builtin", "repo": "", "path": "research/bioinformatics", "tags": ["bioinformatics", "genomics", "sequencing", "biology", "research", "science"], "extra": {}}, {"name": "blackbox", "description": "Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API k", "source": "official", "identifier": "official/autonomous-ai-agents/blackbox", "trust_level": "builtin", "repo": "", "path": "autonomous-ai-agents/blackbox", "tags": ["Coding-Agent", "Blackbox", "Multi-Agent", "Judge", "Multi-Model"], "extra": {}}, {"name": "blender-mcp", "description": "Control Blender directly from Hermes via socket connection to the blender-mcp addon. Create 3D objects, materials, animations, and run arbitrary Blender Python (bpy) code. Use when user wants to creat", "source": "official", "identifier": "official/creative/blender-mcp", "trust_level": "builtin", "repo": "", "path": "creative/blender-mcp", "tags": [], "extra": {}}, {"name": "canvas", "description": "Canvas LMS integration \u2014 fetch enrolled courses and assignments using API token authentication.", "source": "official", "identifier": "official/productivity/canvas", "trust_level": "builtin", "repo": "", "path": "productivity/canvas", "tags": ["Canvas", "LMS", "Education", "Courses", "Assignments"], "extra": {}}, {"name": "chroma", "description": "Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production c", "source": "official", "identifier": "official/mlops/chroma", "trust_level": "builtin", "repo": "", "path": "mlops/chroma", "tags": ["RAG", "Chroma", "Vector Database", "Embeddings", "Semantic Search", "Open Source", "Self-Hosted", "Document Retrieval", "Metadata Filtering"], "extra": {}}, {"name": "clip", "description": "OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content m", "source": "official", "identifier": "official/mlops/clip", "trust_level": "builtin", "repo": "", "path": "mlops/clip", "tags": ["Multimodal", "CLIP", "Vision-Language", "Zero-Shot", "Image Classification", "OpenAI", "Image Search", "Cross-Modal Retrieval", "Content Moderation"], "extra": {}}, {"name": "concept-diagrams", "description": "Generate flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mod", "source": "official", "identifier": "official/creative/concept-diagrams", "trust_level": "builtin", "repo": "", "path": "creative/concept-diagrams", "tags": ["diagrams", "svg", "visualization", "education", "physics", "chemistry", "engineering"], "extra": {}}, {"name": "distributed-llm-pretraining-torchtitan", "description": "Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs", "source": "official", "identifier": "official/mlops/torchtitan", "trust_level": "builtin", "repo": "", "path": "mlops/torchtitan", "tags": ["Model Architecture", "Distributed Training", "TorchTitan", "FSDP2", "Tensor Parallel", "Pipeline Parallel", "Context Parallel", "Float8", "Llama", "Pretraining"], "extra": {}}, {"name": "docker-management", "description": "Manage Docker containers, images, volumes, networks, and Compose stacks \u2014 lifecycle ops, debugging, cleanup, and Dockerfile optimization.", "source": "official", "identifier": "official/devops/docker-management", "trust_level": "builtin", "repo": "", "path": "devops/docker-management", "tags": ["docker", "containers", "devops", "infrastructure", "compose", "images", "volumes", "networks", "debugging"], "extra": {}}, {"name": "domain-intel", "description": "Passive domain reconnaissance using Python stdlib. Subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys ", "source": "official", "identifier": "official/research/domain-intel", "trust_level": "builtin", "repo": "", "path": "research/domain-intel", "tags": [], "extra": {}}, {"name": "drug-discovery", "description": "Pharmaceutical research assistant for drug discovery workflows. Search bioactive compounds on ChEMBL, calculate drug-likeness (Lipinski Ro5, QED, TPSA, synthetic accessibility), look up drug-drug inte", "source": "official", "identifier": "official/research/drug-discovery", "trust_level": "builtin", "repo": "", "path": "research/drug-discovery", "tags": ["science", "chemistry", "pharmacology", "research", "health"], "extra": {}}, {"name": "duckduckgo-search", "description": "Free web search via DuckDuckGo \u2014 text, news, images, videos. No API key needed. Prefer the `ddgs` CLI when installed; use the Python DDGS library only after verifying that `ddgs` is available in the c", "source": "official", "identifier": "official/research/duckduckgo-search", "trust_level": "builtin", "repo": "", "path": "research/duckduckgo-search", "tags": ["search", "duckduckgo", "web-search", "free", "fallback"], "extra": {}}, {"name": "faiss", "description": "Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search,", "source": "official", "identifier": "official/mlops/faiss", "trust_level": "builtin", "repo": "", "path": "mlops/faiss", "tags": ["RAG", "FAISS", "Similarity Search", "Vector Search", "Facebook AI", "GPU Acceleration", "Billion-Scale", "K-NN", "HNSW", "High Performance", "Large Scale"], "extra": {}}, {"name": "fastmcp", "description": "Build, test, inspect, install, and deploy MCP servers with FastMCP in Python. Use when creating a new MCP server, wrapping an API or database as MCP tools, exposing resources or prompts, or preparing ", "source": "official", "identifier": "official/mcp/fastmcp", "trust_level": "builtin", "repo": "", "path": "mcp/fastmcp", "tags": ["MCP", "FastMCP", "Python", "Tools", "Resources", "Prompts", "Deployment"], "extra": {}}, {"name": "fitness-nutrition", "description": "Gym workout planner and nutrition tracker. Search 690+ exercises by muscle, equipment, or category via wger. Look up macros and calories for 380,000+ foods via USDA FoodData Central. Compute BMI, TDEE", "source": "official", "identifier": "official/health/fitness-nutrition", "trust_level": "builtin", "repo": "", "path": "health/fitness-nutrition", "tags": ["health", "fitness", "nutrition", "gym", "workout", "diet", "exercise"], "extra": {}}, {"name": "gitnexus-explorer", "description": "Index a codebase with GitNexus and serve an interactive knowledge graph via web UI + Cloudflare tunnel.", "source": "official", "identifier": "official/research/gitnexus-explorer", "trust_level": "builtin", "repo": "", "path": "research/gitnexus-explorer", "tags": ["gitnexus", "code-intelligence", "knowledge-graph", "visualization"], "extra": {}}, {"name": "guidance", "description": "Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained genera", "source": "official", "identifier": "official/mlops/guidance", "trust_level": "builtin", "repo": "", "path": "mlops/guidance", "tags": ["Prompt Engineering", "Guidance", "Constrained Generation", "Structured Output", "JSON Validation", "Grammar", "Microsoft Research", "Format Enforcement", "Multi-Step Workflows"], "extra": {}}, {"name": "here.now", "description": "Publish static sites to {slug}.here.now and store private files in cloud Drives for agent-to-agent handoff.", "source": "official", "identifier": "official/productivity/here-now", "trust_level": "builtin", "repo": "", "path": "productivity/here-now", "tags": ["here.now", "herenow", "publish", "deploy", "hosting", "static-site", "web", "share", "URL", "drive", "storage"], "extra": {}}, {"name": "hermes-atropos-environments", "description": "Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and t", "source": "official", "identifier": "official/mlops/hermes-atropos-environments", "trust_level": "builtin", "repo": "", "path": "mlops/hermes-atropos-environments", "tags": ["atropos", "rl", "environments", "training", "reinforcement-learning", "reward-functions"], "extra": {}}, {"name": "honcho", "description": "Configure and use Honcho memory with Hermes -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Us", "source": "official", "identifier": "official/autonomous-ai-agents/honcho", "trust_level": "builtin", "repo": "", "path": "autonomous-ai-agents/honcho", "tags": ["Honcho", "Memory", "Profiles", "Observation", "Dialectic", "User-Modeling", "Session-Summary"], "extra": {}}, {"name": "huggingface-accelerate", "description": "Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). I", "source": "official", "identifier": "official/mlops/accelerate", "trust_level": "builtin", "repo": "", "path": "mlops/accelerate", "tags": ["Distributed Training", "HuggingFace", "Accelerate", "DeepSpeed", "FSDP", "Mixed Precision", "PyTorch", "DDP", "Unified API", "Simple"], "extra": {}}, {"name": "huggingface-tokenizers", "description": "Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignme", "source": "official", "identifier": "official/mlops/huggingface-tokenizers", "trust_level": "builtin", "repo": "", "path": "mlops/huggingface-tokenizers", "tags": ["Tokenization", "HuggingFace", "BPE", "WordPiece", "Unigram", "Fast Tokenization", "Rust", "Custom Tokenizer", "Alignment Tracking", "Production"], "extra": {}}, {"name": "inference-sh-cli", "description": "Run 150+ AI apps via inference.sh CLI (infsh) \u2014 image generation, video creation, LLMs, search, 3D, social automation. Uses the terminal tool. Triggers: inference.sh, infsh, ai apps, flux, veo, image ", "source": "official", "identifier": "official/devops/cli", "trust_level": "builtin", "repo": "", "path": "devops/cli", "tags": ["AI", "image-generation", "video", "LLM", "search", "inference", "FLUX", "Veo", "Claude"], "extra": {}}, {"name": "instructor", "description": "Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-te", "source": "official", "identifier": "official/mlops/instructor", "trust_level": "builtin", "repo": "", "path": "mlops/instructor", "tags": ["Prompt Engineering", "Instructor", "Structured Output", "Pydantic", "Data Extraction", "JSON Parsing", "Type Safety", "Validation", "Streaming", "OpenAI", "Anthropic"], "extra": {}}, {"name": "lambda-labs-gpu-cloud", "description": "Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clust", "source": "official", "identifier": "official/mlops/lambda-labs", "trust_level": "builtin", "repo": "", "path": "mlops/lambda-labs", "tags": ["Infrastructure", "GPU Cloud", "Training", "Inference", "Lambda Labs"], "extra": {}}, {"name": "llava", "description": "Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, vi", "source": "official", "identifier": "official/mlops/llava", "trust_level": "builtin", "repo": "", "path": "mlops/llava", "tags": ["LLaVA", "Vision-Language", "Multimodal", "Visual Question Answering", "Image Chat", "CLIP", "Vicuna", "Conversational AI", "Instruction Tuning", "VQA"], "extra": {}}, {"name": "mcporter", "description": "Use the mcporter CLI to list, configure, auth, and call MCP servers/tools directly (HTTP or stdio), including ad-hoc servers, config edits, and CLI/type generation.", "source": "official", "identifier": "official/mcp/mcporter", "trust_level": "builtin", "repo": "", "path": "mcp/mcporter", "tags": ["MCP", "Tools", "API", "Integrations", "Interop"], "extra": {}}, {"name": "meme-generation", "description": "Generate real meme images by picking a template and overlaying text with Pillow. Produces actual .png meme files.", "source": "official", "identifier": "official/creative/meme-generation", "trust_level": "builtin", "repo": "", "path": "creative/meme-generation", "tags": ["creative", "memes", "humor", "images"], "extra": {}}, {"name": "memento-flashcards", "description": "Spaced-repetition flashcard system. Create cards from facts or text, chat with flashcards using free-text answers graded by the agent, generate quizzes from YouTube transcripts, review due cards with ", "source": "official", "identifier": "official/productivity/memento-flashcards", "trust_level": "builtin", "repo": "", "path": "productivity/memento-flashcards", "tags": ["Education", "Flashcards", "Spaced Repetition", "Learning", "Quiz", "YouTube"], "extra": {}}, {"name": "modal-serverless-gpu", "description": "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 scal", "source": "official", "identifier": "official/mlops/modal", "trust_level": "builtin", "repo": "", "path": "mlops/modal", "tags": ["Infrastructure", "Serverless", "GPU", "Cloud", "Deployment", "Modal"], "extra": {}}, {"name": "nemo-curator", "description": "GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16\u00d7 faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, N", "source": "official", "identifier": "official/mlops/nemo-curator", "trust_level": "builtin", "repo": "", "path": "mlops/nemo-curator", "tags": ["Data Processing", "NeMo Curator", "Data Curation", "GPU Acceleration", "Deduplication", "Quality Filtering", "NVIDIA", "RAPIDS", "PII Redaction", "Multimodal", "LLM Training Data"], "extra": {}}, {"name": "neuroskill-bci", "description": "Connect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40", "source": "official", "identifier": "official/health/neuroskill-bci", "trust_level": "builtin", "repo": "", "path": "health/neuroskill-bci", "tags": ["BCI", "neurofeedback", "health", "focus", "EEG", "cognitive-state", "biometrics", "neuroskill"], "extra": {}}, {"name": "one-three-one-rule", "description": "Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strat", "source": "official", "identifier": "official/communication/one-three-one-rule", "trust_level": "builtin", "repo": "", "path": "communication/one-three-one-rule", "tags": ["communication", "decision-making", "proposals", "trade-offs"], "extra": {}}, {"name": "openclaw-migration", "description": "Migrate a user's OpenClaw customization footprint into Hermes Agent. Imports Hermes-compatible memories, SOUL.md, command allowlists, user skills, and selected workspace assets from ~/.openclaw, then ", "source": "official", "identifier": "official/migration/openclaw-migration", "trust_level": "builtin", "repo": "", "path": "migration/openclaw-migration", "tags": ["Migration", "OpenClaw", "Hermes", "Memory", "Persona", "Import"], "extra": {}}, {"name": "optimizing-attention-flash", "description": "Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory iss", "source": "official", "identifier": "official/mlops/flash-attention", "trust_level": "builtin", "repo": "", "path": "mlops/flash-attention", "tags": ["Optimization", "Flash Attention", "Attention Optimization", "Memory Efficiency", "Speed Optimization", "Long Context", "PyTorch", "SDPA", "H100", "FP8", "Transformers"], "extra": {}}, {"name": "oss-forensics", "description": "Supply chain investigation, evidence recovery, and forensic analysis for GitHub repositories.\nCovers deleted commit recovery, force-push detection, IOC extraction, multi-source evidence\ncollection, hy", "source": "official", "identifier": "official/security/oss-forensics", "trust_level": "builtin", "repo": "", "path": "security/oss-forensics", "tags": [], "extra": {}}, {"name": "page-agent", "description": "Embed alibaba/page-agent into your own web application \u2014 a pure-JavaScript in-page GUI agent that ships as a single