What makes Deep Research Suite notable?

969 downloads. Trust evidence available

How should Deep Research Suite be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Deep Research Suite

Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Skill: Deep Research Suite Owner: aptratcn Summary: Deep Research Suite - One command to aggregate, analyze, and synthesize research from multiple sources. Search → Extract → Summarize → Report. Tags: analysis:1.0.0, automation:1.0.0, latest:1.0.0, productivity:1.0.0, research:1.0.0 Version history: v1.0.0 | 2026-04-20T23:40:04.215Z | user Initial release: One command to aggregate, analyze, and synthesize research fr

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

969

Updated

Oct 12, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 969 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 969 downloadsadoption · observed Oct 12, 2026
Latest release: 1.0.0release · observed Apr 20, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.0release
Initial release: One command to aggregate, analyze, and synthesize research from multiple sources
Observed Apr 20, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17cpfvkz8jmsw8vpgqypg10vh857dxa:deep-research-suite

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-aptratcn-deep-research-suite/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

6,587 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
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...]

|

_meta.json

{
  "ownerId": "kn74w4pv5ms0vhet3bstvw95a5833msw",
  "slug": "deep-research-suite",
  "version": "1.0.0",
  "publishedAt": 1776728404215
}

skill-card.md

## Description:

Deep Research Suite guides an agent through multi-source search, extraction, synthesis, and structured research report generation.

This skill is ready for commercial/non-commercial use.

## Publisher:

[aptratcn](https://clawhub.ai/user/aptratcn)

### License/Terms of Use:

MIT-0

## Use Case:

Researchers, analysts, and developers use this skill to gather public-source information, compare findings, and produce concise research reports for decision support or follow-up work.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Research outputs may persist under memory/research/ and retain sensitive prompts, summaries, or source notes.

Mitigation: Avoid private or sensitive prompts unless local storage is acceptable, and delete generated reports when they are no longer needed.

Risk: Public-source research can include outdated, contradictory, or low-credibility information.

Mitigation: Check source dates, credibility, contradictions, and cited URLs before relying on the report.

## Reference(s):

- [Source skill artifact](artifact/SKILL.md)
- [ClawHub skill page](https://clawhub.ai/aptratcn/skills/deep-research-suite)

## Skill Output:

**Output Type(s):** [text, markdown, guidance, files]

**Output Format:** [Markdown research reports with source lists and optional comparison tables]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Reports may be saved under memory/research/ when the workflow is followed.]

## Skill Version(s):

1.0.0 (source: server release metadata and SKILL.md frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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