Deep Research Suite

Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report.

Security Scan

The skill's requests and instructions are internally consistent with its stated goal of multi-source research aggregation and reporting.

Purpose & Capability

The name/description match the SKILL.md: it instructs web/GitHub/HN/ArXiv/Reddit/news searches, extraction, synthesis, and saving reports. No unrelated binaries, credentials, or config paths are requested.

Instruction Scope

Instructions are limited to searching public sources, extracting and synthesizing content, and saving reports to memory/research/. The SKILL.md references writing files to memory/research/ but the skill declares no config paths — this assumes the agent runtime grants write access to its memory/filesystem. The instructions do not ask to read unrelated local files or secrets and explicitly warn against fabricating sources.

Install Mechanism

No install spec or code files (instruction-only). This is low-risk: nothing is downloaded or written to disk by an installer.

Credentials

The skill requires no environment variables, credentials, or external config. That is proportionate for a research-aggregation instruction set. (If the agent were to access paywalled or private sources, additional credentials would be needed, but none are requested here.)

Persistence & Privilege

always is false and no special privileges are requested. The skill can be invoked autonomously by the agent (platform default) but it does not request elevated or persistent system-wide privileges.

Assessment

This skill is coherent with its stated purpose, but before installing check: (1) whether your agent runtime allows web access and has permission to write to the memory/research/ location (the SKILL.md assumes that), (2) whether you want the agent to autonomously run repeated web searches (you can disable autonomous invocation if concerned), and (3) that saving aggregated content may store copyrighted or sensitive material—review reports and sources for confidentiality and accuracy. If you need access to paywalled/private sources, expect additional credential requests at that time.

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