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Found 313 Skills
Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.
Delegate noisy investigation to one or more subagents so the orchestrator's context stays clean, then work from the distilled answer. Use this skill whenever answering a question would require reading many files, long logs, large diffs, or wide codebase surveys — i.e. when producing the answer generates far more noise than the answer itself. Use it for "how does X work", "where is Y used", "what's the root cause of Z", "summarize this PR/log" style questions, and reach for it liberally before reading a pile of files inline.
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.
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".
Pattern for progressively refining context retrieval to solve the subagent context problem
CRITICAL skill for executing multiple runSubagent calls in a SINGLE function_calls block for true parallelism. Essential for efficient multi-task workflows, subagent coordination, and maximizing throughput.
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"
Creates Cursor-specific AI subagents with isolated context for complex multi-step workflows. Use when creating subagents for Cursor editor specifically, following Cursor's patterns and directories (.cursor/agents/). Triggers on "cursor subagent", "cursor agent".
Code review practices with technical rigor and verification gates. Use for receiving feedback, requesting code-reviewer subagent reviews, or preventing false completion claims in pull requests.
Orchestrate in-session Task tool teams for parallel work. Fan-out research, implementation, review, and documentation across subagents. Use when: parallel tasks, fan-out, subagent team, Task tool, in-session agents.
Research-driven code review and validation at multiple levels of abstraction. Two modes: (1) Session review — after making changes, review and verify work using parallel reviewers that research-validate every assumption; (2) Full codebase audit — deep end-to-end evaluation using parallel teams of subagent-spawning reviewers. Use when reviewing changes, verifying work quality, auditing a codebase, validating correctness, checking assumptions, finding defects, reducing complexity. NOT for writing new code, explaining code, or benchmarking.
Sequential subagent execution with two-stage review gates for implementation plans. Use when executing multi-task plans in current session, when tasks need fresh subagent context to avoid pollution, when formal review cycles (spec compliance then code quality) are required between tasks, or when you need diff-based validation of each task before proceeding.