Agent skill · data analytics · daymade
gemini-history-analyzer
Analyze Google Takeout exports of Gemini conversation history. Use when the user mentions Gemini takeout, Gemini export, Gemini history, Gemini conversation analysis, Google Takeout zip analysis, or drags a takeout zip into the project. Also use when the user asks to "analyze my Gemini data", "what did I talk to Gemini about", or wants to extract insights from Gemini chat logs.
Why this skill is useful
Adds domain-specific analysis procedures for Google Takeout exports that the AI wouldn't reliably generate on its own.
What it needs
Requires unar installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 1,320 stars on the source repository.
What this skill does
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: Signal Meeting Transcript Prompt-Response -------- ------------------- ----------------- Speaker labels "张三:"、"李四:"、"Speaker 1:" "User:"、"Assistant:"、"You:" Content pattern Discussion flow, turn-taking, small talk Q&A, instruction→output Length distribution Variable, lots of short turns Longer assistant responses File naming Often Chinese, meeting-style titles English, topic-based titles 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. The standard structure: Step 2: Inventory and Categorize Run a full inventory before reading any single file: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills daymade/gemini-history-analyzer