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GitBook documentation platform. Use when creating, publishing, or managing docs sites — content structure, blocks, Git Sync, customization, AI search, collaboration, and the GitBook API.

MemPalace local-first AI memory system. Use when setting up persistent memory for Claude Code sessions, mining project files or conversation transcripts, querying past context, configuring MCP tools, managing the knowledge graph, or troubleshooting palace operations.

LangChain AWS integration — ChatBedrockConverse (Claude/Nova/Llama/Mistral on Bedrock), BedrockEmbeddings, AmazonKnowledgeBasesRetriever, BedrockAgentsRunnable, BedrockRerank, BedrockPromptCachingMiddleware, CodeInterpreterToolkit, BrowserToolkit (computer use), Neptune graph chains, and SageMaker endpoint.

LangChain Deep Agents (Python) — build, deploy, and customize stateful long-running agents with virtual filesystems, subagents, human-in-the-loop, and LangSmith observability. Also covers LangGraph, LangChain OSS chains/retrievers, and Agent Server API.

LangChain Exa integration — semantic web search with ExaSearchRetriever (RAG), ExaSearchResults (agent tool), and ExaFindSimilarResults (find similar URLs). Unique features: use_autoprompt (LLM query rewriting), highlights (excerpts), summary (per-result LLM summaries), livecrawl (real-time), and date filtering.

LangChain MCP Adapters — connect LangChain agents to MCP (Model Context Protocol) servers. Load MCP tools, prompts, and resources as LangChain-compatible objects. Supports stdio, SSE, StreamableHTTP, and WebSocket transports. Includes interceptors, callbacks, and multi-server management.

LangChain Neo4j integration — Neo4jGraph for Cypher queries and schema inspection, GraphCypherQAChain for natural-language-to-Cypher Q&A, Neo4jVector for vector/hybrid RAG, Neo4jSaver LangGraph checkpointer, Neo4jChatMessageHistory, and GraphDocument/Node/Relationship for knowledge graph construction.

LangChain Ollama integration — run local LLMs with ChatOllama (chat completions, tool calling, structured output, reasoning/thinking mode), OllamaLLM (raw text completions), and OllamaEmbeddings. Connects to a local Ollama server at localhost:11434.

Build onchain applications with React components and TypeScript utilities from Coinbase's OnchainKit. Use when users want to create crypto wallets, swap tokens, mint NFTs, build payments, display blockchain identities, or develop any onchain app functionality. Supports wallet connection, transaction building, token operations, identity management, and complete onchain app development workflows.

Use when placing any perpetual order on HyperLiquid to minimize fees via maker (post-only) order preference, understand when taker execution is justified, and calculate break-even fee impact on trade edge.

Use before entering or holding any perpetual futures position on HyperLiquid. Funding rate is a perpetuals-exclusive carry cost that has no analogue in spot or traditional futures. Elevated funding directly deducts from trade edge on every 8-hour settlement. This skill covers funding mechanics, carry cost calculation, regime classification, long/short asymmetry, funding-momentum as an independent alpha signal, crowding detection, and integration with position sizing and risk controls.

Use when evaluating any RSI-based reversal or mean-reversion signal on HyperLiquid perpetuals. RSI reversals have strong regime dependency — they work in ranging/mean-reverting conditions and fail catastrophically in trending conditions. This skill documents the full regime filter stack, divergence confirmation requirements, failure modes specific to RSI reversals in perpetuals, and how RSI signals coordinate with EMA cross signals in mixed-regime conditions.

Use when evaluating, entering, managing, or reviewing any EMA crossover strategy signal on HyperLiquid perpetuals. Documents the complete taxonomy of EMA cross failure modes, the pre-entry regime filters that suppress false signals, and the post-entry management rules that limit damage when failure occurs despite filtering. Apply before any EMA-based entry and during any post-trade analysis of a losing EMA cross trade.

Use before opening any new position to determine whether adding it would violate concurrent position limits. Enforces per-strategy, per-direction, per-correlation-cluster, and total portfolio limits. Integrates kill-switch tier state and regime conditions to dynamically tighten caps. Always consult this skill after kelly-position-sizing-perps and before slippage-budget-enforcement.

Use when monitoring portfolio drawdown and session P&L to determine whether any circuit-breaker threshold has been breached, which positions must be closed, what trading suspension duration applies, and how to restore trading with a reduced Kelly multiplier after cooldown. This skill governs all kill-switch logic — never bypass it.

Use when sizing any perpetual position on HyperLiquid to compute the Kelly-optimal notional, apply fractional Kelly for risk management, cap by portfolio exposure limits, and adjust for leverage, funding carry cost, and correlation to existing positions.

Integrate Cloudflare services with Dokploy templates: R2 storage, DNS challenge for SSL, Zero Trust Access, Workers, WAF, and Tunnel. Default to CF services for external dependencies.

Generate Docker Compose files following Dokploy conventions with proper networking, volumes, and service patterns. Use when creating new Dokploy templates or converting existing compose files.

Environment variable patterns for Dokploy templates including required vs optional syntax, secrets, connection strings, and configuration organization.

Health check patterns for different service types in Dokploy templates. Covers HTTP, PostgreSQL, MongoDB, Redis, MySQL, and custom health checks.

Multi-service architecture patterns for Dokploy templates including dependency chains, service communication, and complex stack design. Use when building templates with 2+ services.

Multi-tenancy patterns for Dokploy templates with network isolation: separate docker networks per tenant, shared infrastructure, and tenant-specific configuration.

Security best practices for Dokploy templates: secrets management, network isolation, least privilege, image security, and hardening recommendations.

Generate template.toml configuration for Dokploy templates with variables, domains, environment mappings, and file mounts. Use when finalizing Dokploy templates.

The social network for AI agents. Post, comment, upvote, and create communities.

The social network for AI agents. Post, comment, upvote, and create communities.

ClawSec suite manager with embedded advisory-feed monitoring, approval-gated malicious-skill response, and guided setup for additional security skills.

A channel plugin that connects OpenClaw to the MoltChat network (wss://wss.molt-chat.com). Enables participation in AI social chat rooms with message batching and anti-spam protection.

Long-form publishing platform for philosophical essays and technical writing.

X for agents. Post, reply, like, follow, and build feeds.

Entry-point skill for using claude-mem to keep CLAUDE.md and MEMORY.md in sync so Claude learns from past work and avoids repeating mistakes.

Manage UniFi network infrastructure via the UniFi MCP Server. Use this skill for any task involving UniFi devices, clients, networks, VLANs, WiFi, firewall rules, zone-based firewall, VPNs, traffic monitoring, backups, RADIUS, QoS, DPI, port forwarding, ACLs, DHCP, DNS, or site management. Triggers when the user mentions UniFi, Ubiquiti, network clients, APs, switches, gateways, firewall policies, or traffic flows in a network management context.

Use when creating or modifying Model Context Protocol (MCP) servers with FastMCP framework - guides through tools, resources, prompts, authentication, Claude Desktop integration, and production deployment with Python and TypeScript examples

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Specialized guide for adding new MCP tools to the UniFi MCP Server following project standards, UniFi API patterns, and test-driven development practices. Use when implementing new UniFi Network Controller features as MCP tools.

MemPalace — Local AI memory with 96.6% recall. Semantic search, temporal knowledge graph, palace architecture (wings/rooms/drawers). Free, no cloud, no API keys.

Async self-improvement loop. Reviews recent work in MemPalace, finds skill gaps, discovers or authors new Hermes skills, and writes a diary entry. Runs unattended via hermes cron or post-task hooks.

Enables Hermes to run a full self-reflection and self-improvement cycle using its Memory Palace, task history, error logs, and electric-sheep outputs; distill durable lessons; execute approved improvements; document the cycle in the LLM-Wiki; and archive both reflection details and generalized lessons for future behavior.