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Found 10,109 Skills
Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.
2-layer parallel agent hierarchy. Layer 1 deploys 3-50+ agents, each with independent context. Layer 2 adds 2+ sub-agents per member. No upper limit on either layer.
Comprehensive security auditor for AI agent skills, prompts, and instructions. Checks for typosquatting, dangerous permissions, prompt injection, supply chain risks, and data exfiltration patterns — before you use any agent or skill.
The unified entry skill for awiki-cli, providing agent identity capabilities and IM capabilities including private chat, group chat, and attachment sending/receiving; end-to-end encrypted communication will be supported in the future, and it is responsible for task routing, minimal loading, security rules, and confirmation rules.
Use when executing implementation plans. Dispatches independent subagents for individual tasks with code review checkpoints between iterations for rapid, controlled development.
Use when dealing with 2 or more independent tasks that have no shared state or sequential dependencies
Shortcut alias for /superplan. Produce higher-quality code by breaking a feature into small, focused tasks the coding agent can nail one at a time. Works like an engineering team: feature → milestones → ~30-min tasks with specific files, acceptance criteria, and dependencies. Each task runs in a fresh context — narrow scope, full attention, one git commit per task.
Scaffolds a complete agent TUI in TypeScript using @openrouter/agent — like create-react-app for terminal agents. Generates a customizable terminal interface with three input styles, four tool display modes, ASCII banners, streaming output, session persistence, and configurable tools. Use when building an agent, creating a TUI, scaffolding an agent project, or building a coding assistant.
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.
Curate Claude Code's auto-memory into durable project knowledge. Analyze MEMORY.md for patterns, promote proven learnings to CLAUDE.md and .claude/rules/, extract recurring solutions into reusable skills. Use when: (1) reviewing what Claude has learned about your project, (2) graduating a pattern from notes to enforced rules, (3) turning a debugging solution into a skill, (4) checking memory health and capacity.
Use this skill when the user types "/notes" or "@notes" with phrases like "save this", "document this", "file this under <project/client>", "extract decisions", "extract action items", or "update notes from this discussion". The skill spawns the notes-librarian subagent to extract durable knowledge and file it into the right Docmost page using the existing workspace structure. Falls back to a configured inbox page when confidence is low.
Format a final summary message for Linear. Your output is automatically streamed to the Linear agent session — just format it well, do not post it yourself.