Skill: aptratcn/deep-research-suite (version 1.0.0)

--- name: deep-research-suite version: 1.0.0 description: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. emoji: 🔬 tags: [research, automation, productivity, analysis, ai-agent] --- # Deep Research Suite 🔬 One command to aggregate, analyze, and synthesize research from multiple sources. ## What It Does ``` Input: "Research AI agent memory management trends 2026" Output: 1. Search 5+ sources 2. Extract key findings 3. Identify patterns 4. Generate structured report 5. Save to file for reference ``` ## Research Pipeline ### Stage 1: Multi-Source Search ``` Sources to check: - Web search (general) - GitHub (code/examples) - Hacker News (discussions) - ArXiv (papers, if relevant) - Reddit (community opinions) - News sites (recent articles) ``` ### Stage 2: Content Extraction ``` For each source: 1. Fetch content 2. Extract main points 3. Identify key facts/statistics 4. Note source credibility 5. Tag by topic relevance ``` ### Stage 3: Synthesis ``` Combine findings: - Group by theme - Identify consensus views - Note contradictions - Highlight emerging trends - Flag outdated info ``` ### Stage 4: Report Generation ``` Output format: # Research Report: [Topic] **Date**: YYYY-MM-DD **Sources**: X sources analyzed ## Executive Summary [2-3 sentence overview] ## Key Findings ### Trend 1: [Name] - Source: X, Y, Z - Evidence: ... - Implications: ... ### Trend 2: [Name] ... ## Contradictions / Debates - View A says: ... (Source: X) - View B says: ... (Source: Y) - Assessment: ... ## Actionable Insights 1. ... 2. ... ## Sources - [Source 1](url) - relevance: high - [Source 2](url) - relevance: medium ``` ## Usage Examples ### Quick Research ``` User: "What's new in LLM context window management?" Agent: [Searching 5 sources...] [Extracting key findings...] [Synthesizing...] ## LLM Context Window Management - 2026 Update ### Key Trends 1. **Dynamic Context Sizing** - Models now auto-adjust context based on task complexity 2. **Semantic Chunking** - Replace fixed-size chunks with meaning-based segments 3. **Context Compression** - 10x compression with minimal info loss [Full report saved to memory/research/llm-context-2026-04-21.md] ``` ### Deep Dive ``` User: "Deep research on AI agent subagent architecture patterns" Agent: [This will take 5-10 minutes for comprehensive analysis] Stage 1/4: Multi-source search... - GitHub: 15 repos found - Papers: 3 relevant - Discussions: 12 threads Stage 2/4: Content extraction... - Analyzing top 10 GitHub repos... - Extracting paper findings... - Summarizing discussions... Stage 3/4: Synthesis... - Identified 4 architecture patterns - Found 2 debates - 3 emerging trends Stage 4/4: Report generation... [Report saved to memory/research/subagent-architecture-2026-04-21.md] ``` ### Competitive Analysis ``` User: "Compare popular AI agent memory solutions" Agent: [Generating comparison matrix...] | Solution | Approach | Pros | Cons | Stars | |----------|----------|------|------|-------| | Mem0 | Persistent memory | Easy integration | Limited context | 25k | | Letta | Stateful agents | Full state | Complex setup | 15k | | LangGraph | Graph memory | Flexible | Learning curve | 100k | [Full comparison saved to memory/research/memory-solutions-comparison.md] ``` ## Output Files All research saved to `memory/research/`: ``` memory/research/ ├── llm-context-2026-04-21.md ├── subagent-architecture-2026-04-21.md └── memory-solutions-comparison.md ``` ## Integration with Other Skills - **Workflow Checkpoint** - Research is a multi-step workflow - **Memory Guard** - Save key findings to long-term memory - **Content Creator** - Generate polished reports ## Anti-Patterns ❌ Don't rely on single source ❌ Don't skip source credibility check ❌ Don't present outdated info as current ❌ Don't fabricate sources or statistics ## License MIT


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