What makes Deep Research Agent notable?

1K downloads. Trust evidence available

How should Deep Research Agent 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 Agent

Comprehensive research agent for in-depth investigation. Use when users ask for deep research, comprehensive analysis, market research, academic surveys, com... Skill: Deep Research Agent Owner: ericn26-star Summary: Comprehensive research agent for in-depth investigation. Use when users ask for deep research, comprehensive analysis, market research, academic surveys, com... Tags: latest:11.0.0 Version history: v1.0.5 | 2026-05-05T03:57:40.015Z | auto No user-facing changes in this version. - No file changes detected from the previous version. - Skill functionality, workflow

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

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 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed May 5, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4j37w3f2wyke5hajt5vtad85wyxf:eric-deep-research-agent

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
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-ericn26-star-eric-deep-research-agent/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

114,521 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: eric-deep-research-agent
description: Comprehensive research agent for in-depth investigation. Use when users ask for deep research, comprehensive analysis, market research, academic surveys, competitive analysis, technology trends, or any topic requiring 100+ source verification. Triggers on requests like "調査して", "research", "分析して", "レポートを作成", "comprehensive report", "deep dive", "thorough analysis".
---

# DeepResearch Agent

Autonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.

## Core Workflow

### Phase 1: Query Decomposition & Planning

**Input**: User's research query (自然言語)

**Process**:
1. **Analyze the query intent**
   - Identify the primary research objective
   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)
   - Assess depth requirements (surface-level vs comprehensive)

2. **Generate multi-dimensional search queries**
   - Historical context queries (when applicable)
   - Technical specification queries
   - Market/industry trend queries
   - Challenge/pain point queries
   - Regulatory/compliance queries (if applicable)
   - Future outlook/prediction queries

3. **Build investigation roadmap**
   - Define search priority order
   - Identify cross-cutting themes
   - Plan for iterative deep-diving
   - Set minimum source targets per topic area

**Output**: `research_plan` object containing:
```json
{
  "primary_topic": "string",
  "sub_topics": ["string"],
  "search_queries": [{"query": "string", "domain": "string", "priority": 1-5}],
  "target_sources": 100,
  "timeline_phases": ["phase1", "phase2", "phase3"]
}
```

### Phase 2: Autonomous Information Gathering

**Tools Used**: `batch_web_search`, `extract_content_from_websites`

**Process**:
1. **Initial breadth search**
   - Execute parallel searches across all primary query dimensions
   - Gather minimum 20-30 URLs per major topic area
   - Prioritize authoritative sources (official docs, academic, established media)

2. **Source classification**
   - Categorize by source type: ニュース, 学術論文, 白書, 技術ドキュメント, フォーラム, ブログ
   - Assess domain authority and reliability
   - Flag sources requiring deeper analysis

3. **Iterative deep-diving**
   - Extract key terms and concepts from initial results
   - Generate follow-up queries using discovered terminology
   - Expand search to related topics and subtopics
   - Loop until saturation (no new significant information)

4. **Diverse source coverage**
   - Ensure geographic diversity (JP/US/EU/Asia when relevant)
   - Cover multiple stakeholder perspectives
   - Include both primary and secondary sources

**Target**: Minimum 100 unique, verified sources

### Phase 3: Content Reading & Reasoning

**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`

**Process**:
1. **Content extraction**
   - Access each promising URL
   - Extract structured information: facts, statistics,

_meta.json

{
  "ownerId": "kn712egma3976f4b68hksznyrs85wsr5",
  "slug": "eric-deep-research-agent",
  "version": "1.0.5",
  "publishedAt": 1777953460015
}

references/research_template.md

# DeepResearch Agent - Reference Documentation

## Research Plan Template

```json
{
  "primary_topic": "テーマ名",
  "research_objective": "研究目的の詳細な説明",
  "sub_topics": [
    {
      "name": "サブトピック名",
      "queries": ["検索クエリ1", "検索クエリ2"],
      "priority": 1-5,
      "target_sources": 20
    }
  ],
  "target_sources": 100,
  "timeline_phases": ["Phase 1: Initial Research", "Phase 2: Deep Dive", "Phase 3: Verification"]
}
```

## Source Classification Framework

### Source Types

| Type | Japanese | Priority | Reliability Score |
|------|----------|----------|-------------------|
| Official Documents | 公式文書 | High | 5/5 |
| Academic Papers | 学術論文 | High | 5/5 |
| Government Reports | 政府報告書 | High | 5/5 |
| Industry Whitepapers | 業界白書 | High | 4/5 |
| Established News | 主要新聞・メディア | Medium | 4/5 |
| Company Reports | 企業レポート | Medium | 3/5 |
| Technical Blogs | 技術ブログ | Medium | 3/5 |
| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |

### Domain Authority Assessment

- **Authoritative**: 政府機関、学術機関、主要企業公式
- **Reliable**: established media, 業界リーダー企業
- **Moderate**: 専門ブログ、有名人ジャーナリスト
- **Caution**: 匿名投稿、更新日古參情報

## Search Query Templates

### Historical Context
```
"[topic] history"
"[topic] evolution"
"[topic] 歴史 背景"
"[topic] 発展 過程"
```

### Technical Specifications
```
"[topic] technical details"
"[topic] technology specifications"
"[topic] 技術 仕様"
"[topic] アーキテクチャ"
```

### Market Analysis
```
"[topic] market size"
"[topic] industry trends"
"[topic] 市場規模"
"[topic] 市場動向 分析"
```

### Challenges & Risks
```
"[topic] challenges"
"[topic] problems issues"
"[topic] 課題 課題点"
"[topic] リスク"
```

### Future Outlook
```
"[topic] future predictions"
"[topic] outlook forecast"
"[topic] 将来展望"
"[topic] 予測"
```

## Content Extraction Matrix

```json
{
  "source_id": "unique_identifier",
  "url": "https://...",
  "title": "Article Title",
  "source_type": "news|academic|whitepaper|blog|forum",
  "date_published": "YYYY-MM-DD",
  "author": "Author Name",
  "language": "ja|en|other",
  "relevance_score": 1-5,
  "key_findings": [
    {
      "finding": "finding description",
      "evidence_type": "statistic|quote|fact|analysis",
      "verification_status": "verified|unverified|contradicted"
    }
  ],
  "contradictions": [
    {
      "issue": "contradicting claim",
      "sources": ["source_id_1", "source_id_2"]
    }
  ],
  "confidence_level": "high|medium|low",
  "notes": "additional observations"
}
```

## Report Citation Format

### Inline Citation
```
Statistic or fact [1]
Quote or specific claim [2]
Analysis or interpretation [3-5]
```

### Reference Entry Format
```
[1] Author(s). "Title." Publication/Website. Date. URL.

Example:
[1] Sato, Y. "AI Technology Trends 2024." Tech Journal. 2024-03-15. https://example.com/article
```

## Quality Checklist

### Pre-Report
- [ ] Minimum 100 unique sources extracted
- [ ] All statistics have 3+ source verification
- [ ] Cross-domain coverage achieved
- [ ] Recent sources prioritized (within 2 years)
- [ ] Contradictions do

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