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Found 12,762 Skills
Use when executing multi-task plans where each task can be implemented independently by a subagent. Triggers when a plan has 3+ independent tasks, when speed of execution is important, when tasks have clear acceptance criteria suitable for delegation, or when two-stage review gates (spec compliance and code quality) are needed for iterative fix cycles.
Points agents to the public Phalcon Compliance documentation portal for compliance-oriented blockchain investigation and monitoring workflows. Use when the user asks about Phalcon Compliance docs, transaction-monitoring-style tooling references, or where to read product documentation alongside crypto-investigation-compliance—not for legal advice or unsubstantiated vendor claims.
Download and analyze social videos using frames + transcript for AI agent understanding at 50× lower cost than multimodal APIs
Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.
Draw diagrams, flowcharts, and visualizations on an Excalidraw canvas. Use when the user asks to draw, visualize, create diagrams, or sketch ideas.
Working memory management, context prioritization, and knowledge retention patterns for AI agents. Use when you need to maintain relevant context and avoid information loss during long tasks.
Use when working with error debugging multi agent review
Fully autonomous epic execution. Runs until ALL children are CLOSED. Local mode uses /swarm with runtime-native spawning (Codex sub-agents or Claude teams). Distributed mode uses /swarm --mode=distributed (tmux + Agent Mail) for persistence and coordination. NO human prompts, NO stopping.
Audits AGENTS.md and CLAUDE.md files using execution-first standards. Checks commands, gotchas, and signal-to-noise ratio. Use when asked to audit, review, score, refactor, or improve agent instruction files, fix stale commands, or reduce bloat.
Build autonomous game-playing agents using AI and reinforcement learning. Covers game environments, agent decision-making, strategy development, and performance optimization. Use when creating game-playing bots, testing game AI, strategic decision-making systems, or game theory applications.
Research agent for external documentation, best practices, and library APIs via MCP tools
Agent orchestration patterns for agentic loops, multi-agent coordination, alternative frameworks, and multi-scenario workflows. Use when building autonomous agent loops, coordinating multiple agents, evaluating CrewAI/AutoGen/Swarm, or orchestrating complex multi-step scenarios.