eric-deep-research-agent
Comprehensive research agent for in-depth investigation.
Skill by badmarsh
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/badmarsh-eric-deep-research-agent
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About
Use cases: documents, research, agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/eric-deep-research-agent
What's inside
- Skill name
eric-deep-research-agent- Description
- Comprehensive research agent for in-depth investigation. Use when users ask for deep research, comprehensive analysis, market research, academic surveys, competitive analysis, technology trends, or any topic requiring 100+ source verification. Triggers on requests like "調査して", "research", "分析して", "レポートを作成", "comprehensive report", "deep dive", "thorough analysis".
- License
- MIT
- Instructions
- 215 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (7)
- SKILL.md
- _meta.json
- prompt_context.md
- research_template.md · references/
- package_skill.py · scripts/
- quick_validate.py · scripts/
- skill.toml
SKILL.md (first lines)
# DeepResearch Agent
Autonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.
## Core Workflow
### Phase 1: Query Decomposition & Planning
**Input**: User's research query (自然言語)
**Process**:
1. **Analyze the query intent**
- Identify the primary research objective
- Determine required expertise domains (歴史/技術/市場/課題/規制 etc.)
- Assess depth requirements (surface-level vs comprehensive)
2. **Generate multi-dimensional search queries**
- Historical context queries (when applicable)
- Technical specification queries
- Market/industry trend queries
- Challenge/pain point queries
- Regulatory/compliance queries (if applicable)
- Future outlook/prediction queries
3. **Build investigation roadmap**
- Define search priority order
- Identify cross-cutting themes
- Plan for iterative deep-diving
- Set minimum source targets per topic area
**Output**: `research_plan` object containing:
```json
{
"primary_topic": "string",
"sub_topics": ["string"],
"search_queries": [{"query": "string", "domain": "string", "priority": 1-5}],
"target_sources": 100,
"timeline_phases": ["phase1", "phase2", "phase3"]
}
```
### Phase 2: Autonomous Information Gathering
**Tools Used**: `batch_web_search`, `extract_content_from_websites`
**Process**:
1. **Initial breadth search**
- Execute parallel searches across all primary query dimensions
- Gather minimum 20-30 URLs per major topic area
- Prioritize authoritative sources (official docs, academic, established media)
2. **Source classification**
- Categorize by source type: ニュース, 学術論文, 白書, 技術ドキュメント, フォーラム, ブログ
- Assess domain authority and reliability
- Flag sources requiring deeper analysis
3. **Iterative deep-diving**
- Extract key terms and concepts from initial results
- Generate follow-up queries using discovered terminology
- Expand search to related topics and subtopics
- Loop until saturation (no new significant information)
4. **Diverse source coverage**
- Ensure geographic diversity (JP/US/EU/Asia when relevant)
- Cover multiple stakeholder perspectives
- Include both primary and secondary sources
**Target**: Minimum 100 unique, verified sources
### Phase 3: Content Reading & Reasoning
**Tools Used**: `extract_content_from_websites`, `extract_pdfs_key_info`
**Process**:
1. **Content extraction**
- Access each promising URL
- Extract structured information: facts, statistics, quotes, dates, claims
- Parse PDF documents for detailed data
2. **Relevance assessment**
- Score content against research objectives (1-5 scale)
- Filter out low-relevance or duplicate content
- Prioritize high-value sources for deep analysis
3. **Information extraction matrix**
```
For each source:
- Source metadata (title, author, date, URL)
- Key findings (bullet points)
- Supporting evidence (quotes, statistics)
- Contradicting information (if any)
- Confidence level (high/medium/low)
```
4. **Pattern recognition**
- Identify consensus areas (multiple sources agree)
- Detect controversy or debate points
- Find knowledge gaps or underreported aspects
### Phase 4: Verification & Gap Filling
**Process**:
1. **Cross-verification protocol**
- Check consistency across independent sources
- Verify statistics with multiple citations
- Confirm quotes with original context
2. **Contradiction resolution**
- Document conflicting information
- Assess source credibility differences
- Note the nature of disagreement (factual vs interpretive)
- Present multiple perspectives when resolution impossible
3. **Gap identification**
- Compare gathered information against research plan
- Identify missing perspectives or outdated information
- Flag areas needing additional primary source verification
4. **Iteration loop** (if gaps identified)From badmarsh/librefang, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 1
- Last commit (this folder)
- May 4, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/badmarsh/librefang
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
eric-deep-research-agent skill. Agent Mart. https://agentmart.dev/skills/badmarsh-eric-deep-research-agent (last updated 2026-10-24)