{"id":"eec0fbc4-5f18-4a1f-bf59-fb115bab7970","entityType":"agent","slug":"clawhub-ericn26-star-eric-deep-research-agent","name":"Deep Research Agent","canonicalUrl":"https://www.xpersona.co/agent/clawhub-ericn26-star-eric-deep-research-agent","canonicalPath":"/agent/clawhub-ericn26-star-eric-deep-research-agent","generatedAt":"2026-10-11T20:57:33.569Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-11T18:22:35.836Z","emptyReason":null},"description":"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","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. 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Use when users ask for deep research, comprehensive analysis, market research, academic surveys, com...\n\nTags: latest:11.0.0\n\nVersion history:\n\nv1.0.5 | 2026-05-05T03:57:40.015Z | auto\n\nNo user-facing changes in this version.\n\n- No file changes detected from the previous version.\n- Skill functionality, workflow, and documentation remain unchanged.\n\nv1.0.10 | 2026-05-04T17:57:40.811Z | auto\n\n- No visible file or documentation changes detected in this release.\n- Functionality, workflow, and usage remain consistent with the previous version.\n- No updates to features, process steps, or quality standards.\n- Release appears to be a maintenance or metadata update only.\n\nv1.1.0 | 2026-05-04T04:56:29.759Z | auto\n\nNo changes detected for version 1.1.0.\n\n- No file or documentation updates were made in this release.\n- Functionality, workflow, and usage instructions remain unchanged.\n\nv11.0.0 | 2026-05-03T10:04:27.024Z | auto\n\nNo user-facing changes in this version.\n\n- No modifications detected in any files.\n- Skill functionality, workflow, and documentation remain unchanged.\n\nv2.0.0 | 2026-05-03T04:59:29.152Z | auto\n\nMajor update: DeepResearch Agent fully redesigned for comprehensive, multi-phase research with high source validation.\n\n- Autonomous, multi-phase workflow now includes query decomposition, information gathering, verification, and structured report synthesis.\n- Strict quality standards: 100+ unique source citations required per research, with cross-verification and confidence indicators.\n- Supports diverse content types and languages, targeting recent and authoritative sources.\n- Error handling and progress tracking protocols implemented for reliability.\n- Tailored for deep market analysis, technology trends, academic surveys, and thorough competitive research.\n\nv10.0.1 | 2026-05-02T06:01:01.758Z | auto\n\nNo changes detected in this version.\n\n- No file or documentation updates.\n- Functionality remains identical to the previous release.\n\nv10.0.0 | 2026-05-01T22:05:09.799Z | auto\n\nNo functional or documentation changes detected in this version.  \n- Version incremented to 10.0.0, but content remains identical to previous release.\n- No updates to workflow, guidelines, or feature descriptions.\n\nv1.0.2 | 2026-05-01T12:57:24.665Z | auto\n\nNo user-facing or workflow changes in this version.\n\n- Version bump; no modifications or file changes detected.\n- All features, workflows, and quality standards remain unchanged.\n\nv1.0.1 | 2026-05-01T09:56:06.346Z | auto\n\nNo user-visible changes in this release.  \n- Version increment only; no modifications to code or documentation detected.\n\nv1.0.0 | 2026-05-01T08:56:54.486Z | auto\n\neric-deep-research-agent v1.0.0\n\n- Initial release of an autonomous, multi-phase research agent designed for deep investigations and comprehensive analysis.\n- Supports decomposition of research queries, multi-dimensional searches, and iterative information gathering from 100+ diverse sources.\n- Synthesizes structured, citation-rich reports with strict verification and diverse coverage (including Japanese and English language support).\n- Features include cross-verification, contradiction resolution, and parallel execution for efficiency.\n- Implements rigorous quality and relevance standards for all research outputs.\n\nArchive index:\n\nArchive v1.0.5: 5 files, 8765 bytes\n\nFiles: _meta.json (143b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v1.0.5:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v1.0.5:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"1.0.5\",\n  \"publishedAt\": 1777953460015\n}\n\nFile v1.0.5:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v1.0.10: 5 files, 8767 bytes\n\nFiles: _meta.json (144b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v1.0.10:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v1.0.10:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"1.0.10\",\n  \"publishedAt\": 1777917460811\n}\n\nFile v1.0.10:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v1.1.0: 5 files, 8766 bytes\n\nFiles: _meta.json (143b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v1.1.0:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v1.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1777870589759\n}\n\nFile v1.1.0:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v11.0.0: 6 files, 9942 bytes\n\nFiles: _meta.json (144b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), skill-card.md (2060b), SKILL.md (7496b)\n\nFile v11.0.0:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v11.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"11.0.0\",\n  \"publishedAt\": 1777802667024\n}\n\nFile v11.0.0:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nFile v11.0.0:skill-card.md\n\n## Description:\n\nComprehensive research agent for in-depth investigation, including deep research, market research, academic surveys, competitive analysis, technology trends, and topics requiring 100+ source verification.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[ericn26-star](https://clawhub.ai/user/ericn26-star)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, analysts, and research users can use this skill to plan and conduct broad, multi-source investigations and synthesize cited research reports. It is suited to requests that require query decomposition, source collection, fact checking, and structured findings.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Large autonomous web and PDF research runs can incorporate untrusted, outdated, or low-quality source material.\n\nMitigation: Treat retrieved content as evidence to be checked, review source quality, and verify important claims against authoritative sources before relying on the report.\n\nRisk: Packaging the skill from a directory containing untrusted symlinks could include unintended files.\n\nMitigation: Run packaging only from a trusted skill directory and review package contents before distribution.\n\n## Reference(s):\n\n- [DeepResearch Agent Reference Documentation](references/research_template.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, JSON, guidance]\n\n**Output Format:** [Markdown research reports with inline citations and JSON planning or extraction structures]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include research plans, source matrices, confidence indicators, contradiction notes, tables, and citation lists.]\n\n## Skill Version(s):\n\n11.0.0 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers 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.\n\nArchive v2.0.0: 5 files, 8766 bytes\n\nFiles: _meta.json (143b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v2.0.0:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v2.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"2.0.0\",\n  \"publishedAt\": 1777784369152\n}\n\nFile v2.0.0:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v10.0.1: 5 files, 8767 bytes\n\nFiles: _meta.json (144b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v10.0.1:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v10.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"10.0.1\",\n  \"publishedAt\": 1777701661758\n}\n\nFile v10.0.1:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v10.0.0: 5 files, 8767 bytes\n\nFiles: _meta.json (144b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v10.0.0:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v10.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"10.0.0\",\n  \"publishedAt\": 1777673109799\n}\n\nFile v10.0.0:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v1.0.2: 5 files, 8767 bytes\n\nFiles: _meta.json (143b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v1.0.2:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"1.0.2\",\n  \"publishedAt\": 1777640244665\n}\n\nFile v1.0.2:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v1.0.1: 5 files, 8766 bytes\n\nFiles: _meta.json (143b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1777629366346\n}\n\nFile v1.0.1:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis\n\nArchive v1.0.0: 5 files, 8766 bytes\n\nFiles: _meta.json (143b), references/research_template.md (3721b), scripts/package_skill.py (2269b), scripts/quick_validate.py (4296b), SKILL.md (7496b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics, quotes, dates, claims\n   - Parse PDF documents for detailed data\n\n2. **Relevance assessment**\n   - Score content against research objectives (1-5 scale)\n   - Filter out low-relevance or duplicate content\n   - Prioritize high-value sources for deep analysis\n\n3. **Information extraction matrix**\n   ```\n   For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)\n   ```\n\n4. **Pattern recognition**\n   - Identify consensus areas (multiple sources agree)\n   - Detect controversy or debate points\n   - Find knowledge gaps or underreported aspects\n\n### Phase 4: Verification & Gap Filling\n\n**Process**:\n1. **Cross-verification protocol**\n   - Check consistency across independent sources\n   - Verify statistics with multiple citations\n   - Confirm quotes with original context\n\n2. **Contradiction resolution**\n   - Document conflicting information\n   - Assess source credibility differences\n   - Note the nature of disagreement (factual vs interpretive)\n   - Present multiple perspectives when resolution impossible\n\n3. **Gap identification**\n   - Compare gathered information against research plan\n   - Identify missing perspectives or outdated information\n   - Flag areas needing additional primary source verification\n\n4. **Iteration loop** (if gaps identified)\n   - Return to Phase 2 with targeted queries\n   - Focus on specific missing elements\n   - Repeat until research objectives are satisfied\n\n### Phase 5: Structured Report Synthesis\n\n**Output Format**: Comprehensive research report\n\n**Structure**:\n```\n# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]\n```\n\n**Quality Standards**:\n- Every factual claim MUST have inline citation [source_id]\n- Source attribution format: `[1] タイトル, 出版社/サイト, 发表日期, URL`\n- Minimum 100 unique sources required\n- Use tables for statistical comparisons\n- Include key quotes with proper attribution\n- Mark uncertain information with confidence indicators\n\n## Execution Guidelines\n\n### Parallel Execution Strategy\n- Run independent searches in parallel (up to 10 concurrent queries)\n- Process multiple content extractions simultaneously\n- Batch similar operations for efficiency\n\n### Quality Thresholds\n- Source minimum: 100 unique URLs successfully extracted\n- Citation minimum: 100 inline references in final report\n- Content relevance: Average score >= 3.0 out of 5\n- Source diversity: Minimum 3 different source types represented\n\n### Error Handling\n- Failed URLs: Log and skip, continue with alternative sources\n- Contradictory info: Document and present both perspectives\n- Insufficient coverage: Extend search phase until threshold met\n- Verification failures: Flag claims as unverified in final report\n\n### Progress Tracking\nMaintain research log with:\n- Sources examined (with success/failure status)\n- Key findings per sub-topic\n- Verification status\n- Remaining gaps\n\n## Example Research Queries\n\nThis skill excels at:\n- \"AI技術の最新動向を100以上のソースで調査して\"\n- \"Electric vehicle market trends 2024 comprehensive analysis\"\n- \"量子コンピューティングの産業応用に関する調査\"\n- \"Sustainable energy transition analysis with 100+ sources\"\n- \"[任意の専門分野]の包括的な市場調査レポートを作成して\"\n\n## Constraints\n\n- **Time budget**: Allow sufficient iteration time for 100+ source verification\n- **Source validation**: All statistics must have minimum 3 source verification\n- **Bias awareness**: Include diverse perspectives, not just mainstream views\n- **Currency**: Prioritize recent sources (within 2 years) for current topics\n- **Language**: Support Japanese, English, and other major languages as needed\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1777625814486\n}\n\nFile v1.0.0:references/research_template.md\n\n# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions documented\n\n### Report Structure\n- [ ] Executive summary present\n- [ ] All claims have citations\n- [ ] Source diversity visible\n- [ ] Tables and figures properly labeled\n- [ ] Conclusion clearly states findings\n\n### Verification\n- [ ] All links functional\n- [ ] Quotes verified against originals\n- [ ] Statistics match cited sources\n- [ ] No logical fallacies in synthesis","readmeExcerpt":"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","codeSnippets":[],"executableExamples":[{"language":"json","snippet":"{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}"},{"language":"text","snippet":"For each source:\n   - Source metadata (title, author, date, URL)\n   - Key findings (bullet points)\n   - Supporting evidence (quotes, statistics)\n   - Contradicting information (if any)\n   - Confidence level (high/medium/low)"},{"language":"text","snippet":"# [Research Title]\n\n## エグゼクティブサマリー\n[2-3 paragraph overview of key findings]\n\n## 1. 背景と目的\n[Context and research motivation]\n\n## 2. 主要な調査結果\n\n### 2.1 [Topic Area 1]\n#### 事実とデータ\n#### 分析と解釈\n#### 出典\n\n### 2.2 [Topic Area 2]\n... (repeat for all sub-topics)\n\n## 3. 市場動向と将来展望\n[Aggregated trends and predictions]\n\n## 4. 課題とリスク\n[Identified challenges with evidence]\n\n## 5. 機会と推奨事項\n[Actionable insights]\n\n## 6. 出典一覧\n[All 100+ sources in academic citation format]\n\n## 付録\n[Supplementary data, tables, charts]"},{"language":"json","snippet":"{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}"},{"language":"text","snippet":"\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\""},{"language":"text","snippet":"\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\""}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: eric-deep-research-agent\ndescription: 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\".\n---\n\n# DeepResearch Agent\n\nAutonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.\n\n## Core Workflow\n\n### Phase 1: Query Decomposition & Planning\n\n**Input**: User's research query (自然言語)\n\n**Process**:\n1. **Analyze the query intent**\n   - Identify the primary research objective\n   - Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)\n   - Assess depth requirements (surface-level vs comprehensive)\n\n2. **Generate multi-dimensional search queries**\n   - Historical context queries (when applicable)\n   - Technical specification queries\n   - Market/industry trend queries\n   - Challenge/pain point queries\n   - Regulatory/compliance queries (if applicable)\n   - Future outlook/prediction queries\n\n3. **Build investigation roadmap**\n   - Define search priority order\n   - Identify cross-cutting themes\n   - Plan for iterative deep-diving\n   - Set minimum source targets per topic area\n\n**Output**: `research_plan` object containing:\n```json\n{\n  \"primary_topic\": \"string\",\n  \"sub_topics\": [\"string\"],\n  \"search_queries\": [{\"query\": \"string\", \"domain\": \"string\", \"priority\": 1-5}],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"phase1\", \"phase2\", \"phase3\"]\n}\n```\n\n### Phase 2: Autonomous Information Gathering\n\n**Tools Used**: `batch_web_search`, `extract_content_from_websites`\n\n**Process**:\n1. **Initial breadth search**\n   - Execute parallel searches across all primary query dimensions\n   - Gather minimum 20-30 URLs per major topic area\n   - Prioritize authoritative sources (official docs, academic, established media)\n\n2. **Source classification**\n   - 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\n   - Assess domain authority and reliability\n   - Flag sources requiring deeper analysis\n\n3. **Iterative deep-diving**\n   - Extract key terms and concepts from initial results\n   - Generate follow-up queries using discovered terminology\n   - Expand search to related topics and subtopics\n   - Loop until saturation (no new significant information)\n\n4. **Diverse source coverage**\n   - Ensure geographic diversity (JP/US/EU/Asia when relevant)\n   - Cover multiple stakeholder perspectives\n   - Include both primary and secondary sources\n\n**Target**: Minimum 100 unique, verified sources\n\n### Phase 3: Content Reading & Reasoning\n\n**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`\n\n**Process**:\n1. **Content extraction**\n   - Access each promising URL\n   - Extract structured information: facts, statistics,"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn712egma3976f4b68hksznyrs85wsr5\",\n  \"slug\": \"eric-deep-research-agent\",\n  \"version\": \"1.0.5\",\n  \"publishedAt\": 1777953460015\n}"},{"path":"references/research_template.md","content":"# DeepResearch Agent - Reference Documentation\n\n## Research Plan Template\n\n```json\n{\n  \"primary_topic\": \"テーマ名\",\n  \"research_objective\": \"研究目的の詳細な説明\",\n  \"sub_topics\": [\n    {\n      \"name\": \"サブトピック名\",\n      \"queries\": [\"検索クエリ1\", \"検索クエリ2\"],\n      \"priority\": 1-5,\n      \"target_sources\": 20\n    }\n  ],\n  \"target_sources\": 100,\n  \"timeline_phases\": [\"Phase 1: Initial Research\", \"Phase 2: Deep Dive\", \"Phase 3: Verification\"]\n}\n```\n\n## Source Classification Framework\n\n### Source Types\n\n| Type | Japanese | Priority | Reliability Score |\n|------|----------|----------|-------------------|\n| Official Documents | 公式文書 | High | 5/5 |\n| Academic Papers | 学術論文 | High | 5/5 |\n| Government Reports | 政府報告書 | High | 5/5 |\n| Industry Whitepapers | 業界白書 | High | 4/5 |\n| Established News | 主要新聞・メディア | Medium | 4/5 |\n| Company Reports | 企業レポート | Medium | 3/5 |\n| Technical Blogs | 技術ブログ | Medium | 3/5 |\n| Forums/Communities | フォーラム・コミュニティ | Low | 2/5 |\n\n### Domain Authority Assessment\n\n- **Authoritative**: 政府機関、学術機関、主要企業公式\n- **Reliable**: established media, 業界リーダー企業\n- **Moderate**: 専門ブログ、有名人ジャーナリスト\n- **Caution**: 匿名投稿、更新日古參情報\n\n## Search Query Templates\n\n### Historical Context\n```\n\"[topic] history\"\n\"[topic] evolution\"\n\"[topic] 歴史 背景\"\n\"[topic] 発展 過程\"\n```\n\n### Technical Specifications\n```\n\"[topic] technical details\"\n\"[topic] technology specifications\"\n\"[topic] 技術 仕様\"\n\"[topic] アーキテクチャ\"\n```\n\n### Market Analysis\n```\n\"[topic] market size\"\n\"[topic] industry trends\"\n\"[topic] 市場規模\"\n\"[topic] 市場動向 分析\"\n```\n\n### Challenges & Risks\n```\n\"[topic] challenges\"\n\"[topic] problems issues\"\n\"[topic] 課題 課題点\"\n\"[topic] リスク\"\n```\n\n### Future Outlook\n```\n\"[topic] future predictions\"\n\"[topic] outlook forecast\"\n\"[topic] 将来展望\"\n\"[topic] 予測\"\n```\n\n## Content Extraction Matrix\n\n```json\n{\n  \"source_id\": \"unique_identifier\",\n  \"url\": \"https://...\",\n  \"title\": \"Article Title\",\n  \"source_type\": \"news|academic|whitepaper|blog|forum\",\n  \"date_published\": \"YYYY-MM-DD\",\n  \"author\": \"Author Name\",\n  \"language\": \"ja|en|other\",\n  \"relevance_score\": 1-5,\n  \"key_findings\": [\n    {\n      \"finding\": \"finding description\",\n      \"evidence_type\": \"statistic|quote|fact|analysis\",\n      \"verification_status\": \"verified|unverified|contradicted\"\n    }\n  ],\n  \"contradictions\": [\n    {\n      \"issue\": \"contradicting claim\",\n      \"sources\": [\"source_id_1\", \"source_id_2\"]\n    }\n  ],\n  \"confidence_level\": \"high|medium|low\",\n  \"notes\": \"additional observations\"\n}\n```\n\n## Report Citation Format\n\n### Inline Citation\n```\nStatistic or fact [1]\nQuote or specific claim [2]\nAnalysis or interpretation [3-5]\n```\n\n### Reference Entry Format\n```\n[1] Author(s). \"Title.\" Publication/Website. Date. URL.\n\nExample:\n[1] Sato, Y. \"AI Technology Trends 2024.\" Tech Journal. 2024-03-15. https://example.com/article\n```\n\n## Quality Checklist\n\n### Pre-Report\n- [ ] Minimum 100 unique sources extracted\n- [ ] All statistics have 3+ source verification\n- [ ] Cross-domain coverage achieved\n- [ ] Recent sources prioritized (within 2 years)\n- [ ] Contradictions do"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"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... 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