deep-research-pro
Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
Best use case
deep-research-pro is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
Teams using deep-research-pro should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deep-research-pro/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deep-research-pro Compares
| Feature / Agent | deep-research-pro | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Deep Research Pro 🔬
A powerful, self-contained deep research skill that produces thorough, cited reports from multiple web sources. No paid APIs required — uses DuckDuckGo search.
## How It Works
When the user asks for research on any topic, follow this workflow:
### Step 1: Understand the Goal (30 seconds)
Ask 1-2 quick clarifying questions:
- "What's your goal — learning, making a decision, or writing something?"
- "Any specific angle or depth you want?"
If the user says "just research it" — skip ahead with reasonable defaults.
### Step 2: Plan the Research (think before searching)
Break the topic into 3-5 research sub-questions. For example:
- Topic: "Impact of AI on healthcare"
- What are the main AI applications in healthcare today?
- What clinical outcomes have been measured?
- What are the regulatory challenges?
- What companies are leading this space?
- What's the market size and growth trajectory?
### Step 3: Execute Multi-Source Search
For EACH sub-question, run the DDG search script:
```bash
# Web search
/home/clawdbot/clawd/skills/ddg-search/scripts/ddg "<sub-question keywords>" --max 8
# News search (for current events)
/home/clawdbot/clawd/skills/ddg-search/scripts/ddg news "<topic>" --max 5
```
**Search strategy:**
- Use 2-3 different keyword variations per sub-question
- Mix web + news searches
- Aim for 15-30 unique sources total
- Prioritize: academic, official, reputable news > blogs > forums
### Step 4: Deep-Read Key Sources
For the most promising URLs, fetch full content:
```bash
curl -sL "<url>" | python3 -c "
import sys, re
html = sys.stdin.read()
# Strip tags, get text
text = re.sub('<[^>]+>', ' ', html)
text = re.sub(r'\s+', ' ', text).strip()
print(text[:5000])
"
```
Read 3-5 key sources in full for depth. Don't just rely on search snippets.
### Step 5: Synthesize & Write Report
Structure the report as:
```markdown
# [Topic]: Deep Research Report
*Generated: [date] | Sources: [N] | Confidence: [High/Medium/Low]*
## Executive Summary
[3-5 sentence overview of key findings]
## 1. [First Major Theme]
[Findings with inline citations]
- Key point ([Source Name](url))
- Supporting data ([Source Name](url))
## 2. [Second Major Theme]
...
## 3. [Third Major Theme]
...
## Key Takeaways
- [Actionable insight 1]
- [Actionable insight 2]
- [Actionable insight 3]
## Sources
1. [Title](url) — [one-line summary]
2. ...
## Methodology
Searched [N] queries across web and news. Analyzed [M] sources.
Sub-questions investigated: [list]
```
### Step 6: Save & Deliver
Save the full report:
```bash
mkdir -p ~/clawd/research/[slug]
# Write report to ~/clawd/research/[slug]/report.md
```
Then deliver:
- **Short topics**: Post the full report in chat
- **Long reports**: Post the executive summary + key takeaways, offer full report as file
## Quality Rules
1. **Every claim needs a source.** No unsourced assertions.
2. **Cross-reference.** If only one source says it, flag it as unverified.
3. **Recency matters.** Prefer sources from the last 12 months.
4. **Acknowledge gaps.** If you couldn't find good info on a sub-question, say so.
5. **No hallucination.** If you don't know, say "insufficient data found."
## Examples
```
"Research the current state of nuclear fusion energy"
"Deep dive into Rust vs Go for backend services in 2026"
"Research the best strategies for bootstrapping a SaaS business"
"What's happening with the US housing market right now?"
```
## For Sub-Agent Usage
When spawning as a sub-agent, include the full research request and context:
```
sessions_spawn(
task: "Run deep research on [TOPIC]. Follow the deep-research-pro SKILL.md workflow.
Read /home/clawdbot/clawd/skills/deep-research-pro/SKILL.md first.
Goal: [user's goal]
Specific angles: [any specifics]
Save report to ~/clawd/research/[slug]/report.md
When done, wake the main session with key findings.",
label: "research-[slug]",
model: "opus"
)
```
## Requirements
- DDG search script: `/home/clawdbot/clawd/skills/ddg-search/scripts/ddg`
- curl (for fetching full pages)
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