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Found 15 Skills
Analyzes high-performing content from URLs and builds a swipe file
Orchestrate comprehensive content research across X, Instagram, YouTube, and TikTok platforms. Runs all research skills in parallel via subagents, then aggregates findings into actionable content plans and platform-specific intelligence playbooks. Use when asked to: - Create a content plan for social media - Research content across all platforms - Generate content ideas from multiple sources - Build a content strategy playbook - Aggregate research from X, Instagram, YouTube, TikTok - Run comprehensive content research - Create platform playbooks Triggers: "content plan", "content planner", "research all platforms", "comprehensive research", "content strategy", "multi-platform research", "create playbooks", "aggregate research"
Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to 6 specialists, generates health score. Use when user says "audit", "full SEO check", "analyze my site", or "website health check".
Meta-skill for pplx-sdk development. Orchestrates code review, testing, scaffolding, SSE streaming, and Python best practices into a unified workflow. Use for any development task on this project.
Senior Multi-Agent Systems (MAS) Architect for 2026. Specialized in Model Context Protocol (MCP) orchestration, Agent-to-Agent (A2A) communication, and recursive delegation frameworks. Expert in managing complex task handoffs, shared memory state, and parallel subagent execution for high-autonomy engineering missions.
Execute from requirement analysis to frontend design document creation
Execute tasks from TODO file - Generic task runner [/todo-task-run xxx]
Convert a course outline into a fully fleshed out intelligent textbook. Use when: (1) User provides a URL containing a course outline/syllabus (2) User asks to "generate a course" or "create a textbook" from an outline (3) User wants to expand a course description into full learning materials Generates: learning graph, chapter structure, MicroSim specifications, quizzes, glossary, and FAQ following the intelligent textbooks framework and evidence-based learning design principles.
Aggressively clean up a codebase by removing AI slop, dead code, weak types, defensive over-engineering, duplication, and legacy cruft. Orchestrates 8 specialized subagents in parallel to deduplicate code, consolidate types, kill unused code, untangle circular dependencies, strengthen weak types, remove unnecessary try/catch, delete deprecated/legacy paths, and strip unhelpful comments. Use when the user asks to 'clean up the codebase', 'remove slop', 'improve code quality', 'remove dead code', 'kill AI slop', 'tighten types', 'remove legacy code', 'deduplicate code', 'DRY this up', 'untangle dependencies', or wants a thorough code quality pass. Also use when the user mentions code smells, technical debt cleanup, or refactoring for clarity — even if they don't use the word 'slop'.
Generates new content drafts based on reference content analysis
Create and maintain AI coding agent subagents (.claude/agents/*.md, .codex/agents/*.md) with YAML frontmatter (name/description/tools/model/permissionMode/skills/hooks), least-privilege tool selection, delegation patterns (Task), context budgeting, and safety best practices.
Review one pull request through a standalone, progressively disclosed workflow. Use when the user asks to review a PR, audit a pull request, prepare GitHub review comments, draft request-changes feedback, write a PR review file, or optionally post approved review comments. This skill handles exactly one PR; ask the user to choose one PR when multiple PR URLs are supplied.