vultisig
MPC-based multi-chain wallet SDK and CLI for AI agents and developers. Perform secure, threshold-signed crypto operations (send, swap, sign) across 40+ blockchains without seed phrases.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
371 skills found
MPC-based multi-chain wallet SDK and CLI for AI agents and developers. Perform secure, threshold-signed crypto operations (send, swap, sign) across 40+ blockchains without seed phrases.
Manage and sync multiple Google Calendar accounts with parallel querying, scheduling, and conflict detection.
A unified interface for integrating and managing LLM chat providers like OpenAI, Anthropic, Google, Azure, and Bedrock within LangChain applications.
Behavioral guidelines for LLMs to reduce coding mistakes, follow best practices, and improve output quality by enforcing simplicity, surgical changes, and goal-driven verification.
Create professional technical diagrams and flowcharts using LaTeX TikZ with standardized Google Material and Anthropic-inspired design themes.
Enforces structured self-assessment checkpoints to validate approach, mitigate risks, and ensure quality before, during, and after task execution.
Generate professional multi-platform ad campaigns from a URL. Get ad copy, audience targeting, creative specs, and budget strategies ready for media buying.
Expert LangGraph architect skill for designing stateful, multi-actor AI agent workflows with robust persistence, conditional branching, and ReAct patterns.
A framework for crafting suspense, detective, and mystery narratives, emphasizing fair play principles, clue placement, and plot structure.
Autonomous multi-agent orchestration framework for Claude Code with memory-driven workflows, parallel-first task execution, Aristotle-based deconstruction, and multi-stage quality gates.
Repository implementation guide for local-skills-mcp. Provides technical documentation on MCP tool handlers, skill loading, aggregation logic, and project structure for developers.
Aggressively prune grammatical scaffolding and filler text from inputs to optimize LLM token usage while retaining core semantic content.