القائمة
Routes tasks to appropriate Task tool agents (subagents). Triggers on complex multi-step tasks, research, code review, exploration, or any task benefiting from specialized agent execution. Matches intent to agent types.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use this skill whenever the user wants high-quality semantic retrieval, cross-textbook synthesis, reference verification, or authority-ranked question answering over the PageIndex corpus. Trigger on requests like "find which texts cover X", "compare sources", "retrieve semantically", "verify this claim from my textbooks", "build a reading list from the corpus", "cluster by topic", or any PageIndex question where naïve filename search would miss relevant material. This skill teaches a state-of-the-art hybrid retrieval workflow over the PageIndex catalog and live MCP extraction surface.
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
Real-time observability dashboard for multi-agent Claude Code sessions. Visualize agent interactions, tool usage, and session flows in real-time through a web dashboard. Track multiple agents running in parallel with swim lane visualization, event filtering, and live charts. **Key Features:** - 🔴 Real-time event streaming via WebSocket - 📊 Agent swim lanes showing parallel execution - 🔍 Event filtering by agent, session, event type - 📈 Live charts for tool usage patterns - 💾 Filesystem-based (no database required) **Inspired by [@indydevdan](https://github.com/indydevdan)**'s work on multi-agent observability. **Our approach:** Filesystem + in-memory streaming vs. indydevdan's SQLite database approach.
Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
Create irresistible offers and pitches using Alex Hormozi's methodology from $100M Offers. Guides through value equation, guarantee frameworks, pricing psychology, and creating offers "too good not to take" for any product or service.
| name | agents-router |
| description | Routes tasks to appropriate Task tool agents (subagents). Triggers on complex multi-step tasks, research, code review, exploration, or any task benefiting from specialized agent execution. Matches intent to agent types. |
Routes tasks to appropriate Task tool subagent types for parallel or specialized execution.
| Agent | Purpose | Best For |
|---|---|---|
| general-purpose | Multi-step research | Complex questions, code search |
| Explore | Codebase exploration | Find files, understand patterns |
| Plan | Implementation planning | Architecture, strategy |
| claude-code-guide | Claude Code help | Feature questions, how-to |
| orchestrator | Multi-domain coordination | Complex multi-agent workflows |
| strategy-analyzer | Deep strategic analysis | Refactoring, optimization |
| architect | Recursive decomposition | Complex system design |
| gemini | Long context analysis | Large codebases, multi-file bugs |
| researcher | Web research | Finding answers, investigating |
| engineer | Professional implementation | PRD execution, debugging |
Activate when task involves:
# Complexity scoring
complexity_factors:
files_affected: weight 0.3
domains_involved: weight 0.25
steps_required: weight 0.25
research_needed: weight 0.2
# Thresholds
spawn_agent_if:
complexity >= 0.7 OR
files > 20 OR
domains > 2 OR
explicit_request
Task Complexity Assessment
│
├── Exploration needed?
│ ├── Quick search? → Explore (quick)
│ ├── Pattern finding? → Explore (medium)
│ └── Deep analysis? → Explore (very thorough)
│
├── Implementation?
│ ├── From PRD? → engineer
│ ├── Architecture? → architect
│ └── Strategy? → strategy-analyzer
│
├── Research?
│ ├── Web search? → researcher
│ ├── Claude Code docs? → claude-code-guide
│ └── Codebase context? → gemini
│
├── Multi-domain?
│ └── orchestrator
│
└── Code review?
└── superpowers:code-reviewer (via skill)
| Task Type | Primary Agent | Alternatives |
|---|---|---|
| Find files by pattern | Explore | general-purpose |
| Search code for keyword | Explore | gemini |
| Plan implementation | Plan | architect |
| Deep strategic analysis | strategy-analyzer | architect |
| Large codebase analysis | gemini | Explore |
| Web research | researcher | general-purpose |
| Multi-agent coordination | orchestrator | general-purpose |
| PRD implementation | engineer | general-purpose |
| Claude Code help | claude-code-guide | - |
When tasks are independent:
Task tool (agent1) + Task tool (agent2) in parallel
When tasks depend on each other:
Task tool (research) → Task tool (implement)
For long-running tasks:
Task tool with run_in_background=true
→ TaskOutput to retrieve results
# Quick codebase search
agent: Explore
thoroughness: quick
# Deep implementation planning
agent: Plan
complexity: high
# Large codebase bug
agent: gemini
context: 2M tokens
# Professional implementation
agent: engineer
from: PRD
# Multi-domain coordination
agent: orchestrator
domains: [frontend, backend, database]