Lucky Collaborative Research (Lucky + Jinx)

---

name: collaborative-research description: Lucky (internet) + Jinx (analysis) collaborative research workflow. Lucky gathers raw data from web sources, Jinx analyzes and structures findings. Use for market research, competitive analysis, marketplace intelligence, API documentation review, trend analysis, pricing research, or any research requiring both web access and deep analysis. Includes market research templates for competitor/product analysis.

---

Collaborative Research Workflow

Core Principle: Divide research into Lucky (data gathering) + Jinx (analysis) for maximum efficiency and parallel processing.

When to Use This Skill

✅ Perfect for:

Market research (competitor analysis, pricing)

API documentation review

Trend analysis (Google Trends, marketplaces)

Technical documentation analysis

Large-scale content analysis

Multi-source data comparison

❌ Not suitable for:

Simple lookups (use direct web_search/web_fetch)

Real-time data that changes quickly

Single-page analysis (not worth the overhead)

The 3-Phase Process

Phase 1: Raw Data Gathering (Lucky)

Time: 30-60% of total project time Focus: Speed and coverage, not precision

1. Set up data directory structure

   mkdir -p /workspace/research/raw-data/YYYY-MM-DD-project
   

2. Use Puppeteer for systematic data collection - Navigate to target sites - Capture BOTH html and text: { html: document.body.innerHTML, text: document.body.innerText } - Save with metadata: URL, timestamp, query/source - Don't fight DOM selectors — capture everything

3. Save structured files for Jinx

   METADATA:
   URL: [source_url]
   TIMESTAMP: [iso_timestamp] 
   QUERY: [search_query]
   
   RAW TEXT:
   [page_text_content]
   
   RAW HTML:
   [full_html_content]
   

4. Transfer to Mac Mini SSD

   scp -i ~/.ssh/lucky_to_mac file.html luckyai@100.90.7.148:~/temp/
   ssh -i ~/.ssh/lucky_to_mac luckyai@100.90.7.148 "mv ~/temp/* '/Volumes/Crucial X10/research/raw-data/project/'"
   

Phase 2: Parallel Analysis (Jinx)

Time: 20-40% of total project time Focus: Pattern extraction and structured output

1. Task Assignment Validation - ✅ Analyzing local files (no internet needed) - ✅ Structured data processing - ✅ Text analysis and extraction

2. Send structured analysis tasks to Jinx

   curl -X POST http://localhost:3001/task -H 'Content-Type: application/json' -d '{
     "prompt": "Analyze files in /Volumes/Crucial X10/research/raw-data/project/. Extract: [specific_data_points]. Output structured JSON with [required_format]. Provide analysis summary with [specific_insights].",
     "priority": "high"
   }'
   

3. Key prompting strategies for Jinx: - Be specific about data extraction requirements - Request JSON output format - Ask for both raw findings AND summary analysis - Include comparison requirements if multiple sources

Phase 3: Compilation & Skills Documentation (Lucky)

Time: 10-20% of total project time Focus: Synthesis and actionable insights

1. Collect Jinx results

   curl -s http://localhost:3001/results/[task-id]
   

2. Compile comprehensive report - Executive summary with key findings - Structured data tables/comparisons - Strategic recommendations - Process insights and improvements

3. Document process learnings - What worked well / areas for improvement - Time saved vs sequential approach - Quality of analysis vs manual extraction

Best Practices

Data Gathering (Lucky)

Capture everything — let Jinx filter, don't pre-filter

Use consistent file naming — project-source-timestamp.html

Include rich metadata — helps Jinx understand context

Work in batches — send first batch to Jinx while gathering more

Analysis Tasks (Jinx)

Be specific about extraction requirements

Request execution — ask Jinx to run analysis scripts, not just provide them

Structure output — JSON format for easy parsing

Ask for insights — not just data extraction but pattern analysis

Collaboration

Send tasks early — don't wait for all data before starting analysis

Check progress regularly — curl status API to monitor queue

Quality over quantity — better to analyze fewer sources deeply

Time Estimates

| Research Scope | Lucky Time | Jinx Time | Total Effective | |---|---|---|---| | Small (3-5 sources) | 20 min | 15 min | 25 min | | Medium (5-10 sources) | 40 min | 20 min | 45 min | | Large (10+ sources) | 60 min | 30 min | 70 min |

*Effective time = max(Lucky, Jinx) due to parallelization*

Security Considerations

HTML sanitization — Strip