Gemini History Analyzer

Gemini History Analyzer

Extract, categorize, and analyze Google Takeout ZIP exports of Gemini Apps activity. The skill handles:

• Extraction (with Chinese/Unicode filename support)
• Content categorization and topic analysis
• User profile extraction from conversation patterns
• Domain-specific keyword search with context verification
• PII/sensitive content detection
• Structured report generation with optional memory file creation

Quick Judgment: Meeting Transcripts vs Prompt-Response

Before full analysis, sample 3-5 files to determine the conversation type:

The analysis approach differs:

• Meeting transcripts → topic categorization, speaker analysis, decision tracking, user profile extraction
• Prompt-response → usage pattern analysis, capability assessment, interest domain mapping

Step 1: Extract the ZIP

Google Takeout files contain Chinese/Unicode filenames. macOS unzip corrupts these — always use unar.

# Install unar if missing
brew install unar 2>/dev/null || true

# Extract to a target directory
unar -o <output-dir> -q "<takeout-zip-path>"

The standard structure:

Takeout/
└── My Activity/
    └── Gemini Apps/
        ├── *.txt          # Conversation transcripts
        ├── *.html         # Web-format backups (rare)
        ├── *.png/jpg      # Images from conversations
        ├── *.mp3/mp4/wav  # Audio/video attachments
        ├── *.pdf          # Uploaded/shared documents
        ├── *.xlsx/docx    # Office documents
        └── *.zip          # Nested archives (animation frames, etc.)

Step 2: Inventory and Categorize

Run a full inventory before reading any single file:

# Count by extension
find <extract-dir> -type f | sed 's/.*\.//' | sort | uniq -c | sort -rn

# List all txt files (these are the primary content)
find <extract-dir> -name "*.txt" -type f | sort

# Get total size per type
find <extract-dir> -type f -exec du -sh {} \; | sort -rh | head -30

Build a table of:

• Total file count, total size
• Breakdown by file type (txt count/size, media count/size, etc.)
• Date range if file timestamps are meaningful

Step 3: Sample and Classify

Read 3-5 txt files spread across the file list (not just the first few — timestamps/file sizes may cluster by type). For each:

1. Read the first ~100 lines to determine conversation type
2. Identify language (Chinese/English/mixed)
3. Classify topic area at a high level
4. Note speaker count and pattern

Use this sample to decide the analysis strategy for the remaining files.

Step 4: Full Content Analysis

For each txt file, read and extract:

4a. Metadata

• Title (filename minus hash suffix)
• Approximate duration (from transcript timestamps)
• Speaker count and identities
• Language mix (Chinese % / English %)

4b. Topic Classification

Assign primary + secondary topics. Default categories (adjust based on actual content):

• Software Development
• AI/LLM Tools & Workflows
• Infrastructure/Cloud/DevOps
• Business/Strategy
• Product/Design
• Team/HR/Personnel
• Finance/Investment (this is often the target of analysis)
• Legal/Compliance
• Personal/Other

4c. Key Findings Per File

For each file, capture:

• Main discussion points (3-5 bullets)
• Decisions made or action items
• Notable quotes (verbatim, with context)
• Relationships mentioned (people, companies, projects)

Step 5: Domain-Specific Keyword Search

When looking for content in a specific domain (finance, legal, etc.):

1. Design keyword list with AND/OR logic — e.g., finance: stock OR investment OR fund OR portfolio OR 股票 OR 投资 OR 基金, but also domain-specific terms like EPS, P/E, dividend, arbitrage
2. Grep every txt file for each keyword batch
3. Read surrounding context (10 lines before/after each match) — keyword matching alone produces massive false positives. "Investment" can mean business strategy, "option" can mean UI choice, "fund" can mean insurance feature
4. Classify each match as:
   • Definite hit (the conversation is about this domain)
   • False positive (same word, different meaning)
   • Ambiguous (needs deeper reading)
5. Re-read full files for definite + ambiguous hits

Never stop at grep output — context verification is mandatory.

Step 6: Generate Report

Report Structure

# Gemini History Analysis Report
**Source**: <zip filename>
**Date analyzed**: <today>
**Extraction size**: <N files, N MB>

## 1. Content Overview
- Total conversations: N
- Conversation type: [Meeting Transcripts | Prompt-Response | Mixed]
- Language distribution: X% Chinese, Y% English, Z% Mixed
- Date range: <earliest> to <latest>
- Media attachments: N images, N audio, N video

## 2. Topic Distribution
| Category | Count | % | Notes |
|----------|-------|---|-------|
| ... | | | |

## 3. Key Findings
(Bulleted, organized by significance)

## 4. Domain-Specific Analysis
(If user requested finance/legal/etc. keyword search)
- Matches found: N
- Confirmed relevant: N
- False positives: N (with examples of what caused them)

## 5. Notable Documents
Non-txt files worth attention: PDFs, spreadsheets, etc.

## 6. Valuable Quotes
Verbatim quotes that capture key insights

## 7. PII / Sensitive Content
(Flag if detected — do NOT include the actual PII in the report)
- File: <name>, Type: <resume/background-check/etc.>, Risk: <high/medium/low>

Step 7: Memory File Generation (Optional)

If the user wants to persist findings as project memory (like .claude/projects/<path>/memory/):

When to generate memory files

• User explicitly asks ("build user profile", "remember this")
• The analysis reveals reusable insights about the user's preferences, workflows, or constraints
• The project has a memory system (check for existing memory/MEMORY.md)