deep-research-pro
Skill4 starsChanged 8 months agoMITView on GitHub
Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
We split this skill into the rules it gives an agent
12 rules. About 0 are common, 12 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈12
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
When the user asks for research on any topic, follow this workflow:
- Distinctive: line 16
Ask 1-2 quick clarifying questions:
- Distinctive: line 20
If the user says "just research it" — skip ahead with reasonable defaults.
- Distinctive: line 45
Use 2-3 different keyword variations per sub-question
- Distinctive: line 65
Read 3-5 key sources in full for depth.
- Distinctive: line 117
Every claim needs a source: No unsourced assertions.
- Distinctive: line 118
Cross-reference: If only one source says it, flag it as unverified.
- Distinctive: line 119
Recency matters: Prefer sources from the last 12 months.
- Distinctive: line 120
Acknowledge gaps: If you couldn't find good info on a sub-question, say so.
- Distinctive: line 121
No hallucination: If you don't know, say "insufficient data found."
- Distinctive: line 134
When spawning as a sub-agent, include the full research request and context:
- Distinctive: line 153
No API keys needed!
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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:
# 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 5Search 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:
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:
# [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:
mkdir -p ~/clawd/research/[slug]
# Write report to ~/clawd/research/[slug]/report.mdThen deliver:
- Short topics: Post the full report in chat
- Long reports: Post the executive summary + key takeaways, offer full report as file
Quality Rules
- Every claim needs a source. No unsourced assertions.
- Cross-reference. If only one source says it, flag it as unverified.
- Recency matters. Prefer sources from the last 12 months.
- Acknowledge gaps. If you couldn't find good info on a sub-question, say so.
- 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)
- No API keys needed!
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 4
- AlekseiUL/sprut-agent-kit★ 66same name
- AlekseiUL/heisenberg-team★ 17same name
- AlekseiUL/heisenberg-team-GPT★ 9same name
- drewid74/ai_skills★ 8same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository112 other files it gives its agents.
- agentic-evalSkill
- agent-memorySkill
- agent-reachSkill
- agent-team-orchestrationSkill
- claude-apiSkill
- context-engineering-collectionSkill
- developer-growth-analysisSkill
- eval-harnessSkill
- exa-searchSkill
- langsmith-fetchSkill
- mcp-builderSkill
- prompt-engineering-patternsSkill
- rag-implementationSkill
- skill-creatorSkill
- subagent-driven-developmentSkill
- api-design-principlesSkill
- architecture-patternsSkill
- artifacts-builderSkill
- backend-patternsSkill
- changelog-generatorSkill
- clean-content-fetchSkill
- code-reviewSkill
- coding-standardsSkill
- computer-useSkill
- debug-proSkill
- docker-essentialsSkill
- e2e-testingSkill
- frontend-designSkill
- frontend-patternsSkill
- git-essentialsSkill
- microservices-patternsSkill
- modern-javascript-patternsSkill
- nextjs-app-router-patternsSkill
- postgresql-table-designSkill
- python-design-patternsSkill
- python-performance-optimizationSkill
- python-testing-patternsSkill
- react-state-managementSkill
- security-auditorSkill
- sql-toolkitSkill
- tdd-workflowSkill
- typescript-advanced-typesSkill
- using-git-worktreesSkill
- vue-best-practicesSkill
- webapp-testingSkill
- algorithmic-artSkill
- brand-guidelinesSkill
- canvas-designSkill
- comic-creatorSkill
- cover-imageSkill
- doc-coauthoringSkill
- docxSkill
- fal-ai-mediaSkill
- frontend-slidesSkill
- image-genSkill
- markdown-to-htmlSkill
- pdfSkill
- pptxSkill
- slide-deckSkill
- slidevSkill
- The first 60 shown.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

