Skip to main content 홈 크리에이터 opencmit alphora deep-research
deep-research Use this skill for any task that requires in-depth research, investigation, or comprehensive report generation on a topic. This includes: producing industry analysis, market research, technology surveys, competitive intelligence, trend reports, or any deliverable that synthesizes information from multiple sources into a structured long-form document; answering complex questions that require gathering evidence from the web, analyzing data, and presenting findings with charts and citations; any request where the user explicitly asks for a 'report', 'research', 'survey', 'white paper', or 'deep dive'. Trigger especially when the task cannot be answered from memory alone and requires active information gathering. Do NOT trigger for simple factual Q&A, code-only tasks, single-file data analysis (use data-analysis skill), or creative writing without research backing.
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Zip 다운로드 다운로드 중... name deep-research description Use this skill for any task that requires in-depth research, investigation, or comprehensive report generation on a topic. This includes: producing industry analysis, market research, technology surveys, competitive intelligence, trend reports, or any deliverable that synthesizes information from multiple sources into a structured long-form document; answering complex questions that require gathering evidence from the web, analyzing data, and presenting findings with charts and citations; any request where the user explicitly asks for a 'report', 'research', 'survey', 'white paper', or 'deep dive'. Trigger especially when the task cannot be answered from memory alone and requires active information gathering. Do NOT trigger for simple factual Q&A, code-only tasks, single-file data analysis (use data-analysis skill), or creative writing without research backing. license Apache-2.0 metadata {"author":"alphora-team","version":"1.0","tags":["research","report","web-search","data-analysis","markdown"]}
Requirements for Outputs
Final Report
Structure
Title page : Topic, date, author lineExecutive summary : 3-5 sentence overview of key findings (written last)Table of contents : Auto-generated from headingsBody sections : Logically organized with H2/H3 headingsConclusion & recommendations : Actionable takeawaysReferences : Numbered list of all sources with URLs
Quality Standards
Every factual claim MUST cite its source with [n] notation linking to the references section
Charts and images MUST have captions explaining what they show
Data tables MUST include units and time periods
Minimum 3 distinct sources for any major conclusion
No hallucinated statistics — every number must trace to collected evidence or computed code output
Visual Requirements
Include at least one data-driven chart per major section (bar, line, pie, etc.)
Reference images should be downloaded locally and embedded via relative paths
All images saved under /mnt/workspace/report/assets/
Image references in markdown: 
Formatting
Use consistent heading hierarchy (H1 for title, H2 for sections, H3 for subsections)
Use tables for structured comparisons
Use blockquotes for key findings or direct quotes
Number formatting: thousands separator for large numbers, 1 decimal for percentages
Intermediate Artifacts All research materials MUST be persisted in the workspace so findings are not lost between iterations:
Directory Purpose /mnt/workspace/research/sources/Extracted web page content (.txt files) /mnt/workspace/research/data/Downloaded datasets and raw data /mnt/workspace/research/images/Downloaded reference images /mnt/workspace/research/notes/Research notes and outlines /mnt/workspace/report/Final report markdown /mnt/workspace/report/assets/Report images (charts + reference images)
Sandbox Environment Path Purpose Access /mnt/workspace/Working directory for all research materials and outputs Read/Write /mnt/skills/deep-research/Skill scripts and references Read-only
The sandbox has network access for web searches and downloads.
Available Scripts Script Purpose Usage scripts/web_search.pySearch the web and return structured results python /mnt/skills/deep-research/scripts/web_search.py "query" --max-results 10scripts/fetch_page.pyFetch a URL and extract clean text content python /mnt/skills/deep-research/scripts/fetch_page.py "https://example.com" --output /mnt/workspace/research/sources/page.txtscripts/download_file.pyDownload files (images, data, PDFs) to workspace python /mnt/skills/deep-research/scripts/download_file.py "https://example.com/chart.png" --output /mnt/workspace/research/images/chart.pngscripts/compile_report.pyValidate and compile the final report python /mnt/skills/deep-research/scripts/compile_report.py /mnt/workspace/report/report.md --validate --to-html
Reference Documentation Document Content references/REPORT_TEMPLATE.mdReport structure template — use as starting skeleton
Dependency Installation If dependencies are missing, install them first:
pip install requests beautifulsoup4 duckduckgo-search pandas matplotlib -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
Research Workflow
CRITICAL: Research is Iterative, Not Linear Deep research follows a collect → analyze → identify gaps → collect more loop. Do NOT attempt to write the final report in one pass. Instead, accumulate materials over multiple iterations, then synthesize.
Phase 1: Planning Before any searching, create a research plan:
plan = """
# Research Plan: [Topic]
## Core Questions
1. [Primary question to answer]
2. [Secondary question]
3. [...]
## Information Needs
- Market data: [what numbers/trends are needed]
- Expert opinions: [whose perspectives matter]
- Case studies: [specific examples to find]
- Comparisons: [what to compare against]
## Planned Sections
1. [Section title] — sources needed: [type]
2. [Section title] — sources needed: [type]
3. [...]
"""
with open ('/mnt/workspace/research/notes/plan.md' , 'w' ) as f:
f.write(plan)
Save the plan to /mnt/workspace/research/notes/plan.md so it persists across iterations.
Phase 2: Material Collection
Web Search Search broadly first, then narrow down:
python /mnt/skills/deep-research/scripts/web_search.py "topic overview" --max-results 10
python /mnt/skills/deep-research/scripts/web_search.py "topic specific aspect 2024 data" --max-results 5
Content Extraction For promising URLs, fetch the full content:
python /mnt/skills/deep-research/scripts/fetch_page.py "https://example.com/article" \
--output /mnt/workspace/research/sources/article_name.txt
Image and Data Collection Download relevant images, datasets, or charts:
python /mnt/skills/deep-research/scripts/download_file.py "https://example.com/chart.png" \
--output /mnt/workspace/research/images/market_share.png
Collection Strategy
Search in multiple languages if the topic is international
Use different query angles for the same topic (statistics, trends, opinions, case studies)
Save every useful source — you can filter later
Record the source URL in each saved file for citation
Phase 3: Data Analysis When you have quantitative data, analyze it with Python:
import pandas as pd
import matplotlib
matplotlib.use('Agg' )
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif' ] = ['Source Han Sans CN' , 'WenQuanYi Micro Hei' , 'SimHei' , 'DejaVu Sans' ]
plt.rcParams['axes.unicode_minus' ] = False
df = pd.read_csv('/mnt/workspace/research/data/market_data.csv' )
summary = df.groupby('category' )['revenue' ].sum ().sort_values(ascending=False )
print (summary)
fig, ax = plt.subplots(figsize=(10 , 6 ))
summary.plot(kind='bar' , ax=ax)
ax.set_title('Revenue by Category' )
ax.set_ylabel('Revenue (¥)' )
plt.tight_layout()
plt.savefig('/mnt/workspace/report/assets/revenue_by_category.png' , dpi=150 , bbox_inches='tight' )
plt.close()
print ("Chart saved." )
For predictive analysis, use appropriate statistical methods:
Trend extrapolation : Linear/polynomial regression with numpy.polyfitGrowth rates : Compound annual growth rate (CAGR) calculationsComparisons : Percentage differences, ratio analysis Always state the methodology and limitations of any prediction.
Phase 4: Report Drafting
Step 1: Load the template Read references/REPORT_TEMPLATE.md for the structural skeleton.
Step 2: Write section by section Do NOT write the entire report at once. Write each section as a separate operation, referencing your collected materials:
section = """
## Market Overview
The global market for [X] reached ¥{value} billion in 2024,
representing a {growth}% year-over-year increase [1].
*Figure 1: Market size evolution from 2020 to 2024. Source: [1]*
Key drivers include:
- **Factor A**: Description with evidence [2]
- **Factor B**: Description with evidence [3]
| Region | Market Share | Growth Rate |
|--------|-------------|-------------|
| Asia | 45.2% | 12.3% |
| Europe | 28.1% | 8.7% |
| Americas | 26.7% | 10.1% |
*Table 1: Regional market distribution. Source: [1][4]*
"""
Step 3: Assemble the full report Once all sections are written, assemble them into the final document and save to /mnt/workspace/report/report.md.
Step 4: Write executive summary last After the full report is written, compose the executive summary based on the actual findings.
Phase 5: Validation and Polish
Compile and validate python /mnt/skills/deep-research/scripts/compile_report.py \
/mnt/workspace/report/report.md --validate --to-html
All image references resolve to existing files
Citation numbers [n] match entries in the references section
Report structure is complete (title, TOC, body, references)
Review checklist
Verification Checklist
Research Quality
Report Completeness
Technical Accuracy
Error Handling
Search returns no results : Try alternative queries, different keywords, or broader termsPage fetch fails : Try with different headers, or note the URL for manual reviewImage download fails : Skip and note in report, or find alternative imageMissing dependencies : pip install requests beautifulsoup4 duckduckgo-search pandas matplotlib -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simpleNetwork timeout : Retry with longer timeout, or move to next sourceEncoding issues : Try encoding='utf-8', then 'gbk', then 'latin1' 