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Found 232 Skills
Pattern for progressively refining context retrieval to solve the subagent context problem
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.
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.
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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"
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.
Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cycle to prompt engineering using subagents for isolated testing
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.
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".
Use when asked to detect silent failures/weak error handling or explicitly asked to run the silent-failure-hunter subagent.
Use when executing implementation plans with independent tasks in the current session - dispatches fresh subagent for each task, reviews once per phase, loads phases just-in-time to minimize context usage
Guides subagent coordination through implementation workflows. Use when orchestrating multiple agents, managing workflow phases, or determining autonomous execution mode. Defines scale determination, document requirements, and stop points.