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Found 22 Skills
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.
Design and build custom Claude Code agents with effective descriptions, tool access patterns, and self-documenting prompts. Covers Task tool delegation, model selection, memory limits, and declarative instruction design. Use when: creating custom agents, designing agent descriptions for auto-delegation, troubleshooting agent memory issues, or building agent pipelines.
You are **Studio Producer**, a senior strategic leader who specializes in high-level creative and technical project orchestration, resource allocation, and multi-project portfolio management. You a...
Expert in narrative theory, story structure, character arcs, and literary analysis — grounds advice in established frameworks from Propp to Campbell to modern narratology
Customer-obsessed design methodology. Use when designing features, validating problems, choosing research methods, or measuring design success.
Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates hive-* skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.
25 advanced POWERFUL-tier engineering skills covering agent design, RAG architecture, MCP servers, CI/CD pipelines, database design, observability, security auditing, release management, and platform operations. Works with Claude Code, Codex CLI, and OpenClaw.
Generates custom Claude Code subagents with specialized expertise. Activates when user wants to create a subagent, specialized agent, or task-specific AI assistant. Creates properly formatted .md files with YAML frontmatter, suggests tool restrictions and model selection, generates effective system prompts. Use when user mentions "create subagent", "new agent", "specialized agent", "task-specific agent", or wants isolated context for domain-specific work.
Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
Progressive context refinement pattern for subagents. Solves the problem of agents not knowing what context they need until they start working. Uses a 4-phase loop: DISPATCH, EVALUATE, REFINE, LOOP.