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deep-research-pro
Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Transforms raw user requests into structured, outcome-focused prompts for Claude Cowork. Use when the user wants to optimize or rewrite a prompt for Cowork, needs help structuring a multi-step task for autonomous execution, or says things like "optimize this Cowork prompt", "rewrite for Cowork", or "make this a Cowork prompt". Outputs a single code block with the rewritten prompt following the GOAL/CONTEXT LOADING/IDENTITY/SUCCESS CRITERIA/INPUTS/CONSTRAINTS/CHECKPOINT RULE structure.
This skill should be used when the user asks to "brainstorm research ideas", "use 5W1H framework", "identify research gaps", "conduct gap analysis", "start research project", "conduct literature review", "define research question", "select research method", "plan research", or mentions research project initiation phase. Provides comprehensive guidance for research startup workflow from idea generation to planning.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
Match a pasted list of academic references against the Crossref REST API and produce a four-column markdown table (original, matched, confidence, flags) with canonical APA citations and DOIs. Use whenever the user pastes a bibliography or reference list and wants to verify, clean up, canonicalize, or find DOIs for those references — triggers include "verify bibliography", "match these references", "find DOIs for this reference list", "canonicalize my citations", "clean up the reference list against Crossref", "check these citations", or any pasted block of academic references accompanied by a request to normalize them.
| name | deep-research-pro |
| version | 1.0.0 |
| description | Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required. |
| homepage | https://github.com/paragshah/deep-research-pro |
| metadata | {"clawdbot":{"emoji":"🔬","category":"research"}} |
A powerful, self-contained deep research skill that produces thorough, cited reports from multiple web sources. No paid APIs required — uses DuckDuckGo search.
When the user asks for research on any topic, follow this workflow:
Ask 1-2 quick clarifying questions:
If the user says "just research it" — skip ahead with reasonable defaults.
Break the topic into 3-5 research sub-questions. For example:
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 5
Search strategy:
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.
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]
Save the full report:
mkdir -p ~/clawd/research/[slug]
# Write report to ~/clawd/research/[slug]/report.md
Then deliver:
"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?"
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"
)
/home/clawdbot/clawd/skills/ddg-search/scripts/ddg