Privacy-first tools for analyzing your personal LLM usage from official ChatGPT and Claude exports. Everything runs locally. Nothing leaves your machine.
Turn your raw export data into: model adoption timelines, topic breakdowns, prompt engineering effectiveness metrics, cost efficiency analysis, and formatted reports.
git clone https://github.com/noah-chelednik/llm-export-analytics.git
cd llm-export-analytics
./run_pipeline.sh --sample
This runs the full pipeline against included sample data so you can see what it produces before using your own exports.
Model adoption timeline shows which AI models you used and when you switched:
2025-01 ████████████████████████████████ GPT-4o (100%)
2025-02 ██████████████████████████ GPT-5 (66%) ████ GPT-4o (21%)
2025-03 ████████████████████████ GPT-5.2-T (78%) ████ GPT-5 (22%)
Prompt technique tracking measures how your prompting style evolves over time:
| Technique | Adoption Rate | First Appeared |
|---|---|---|
| Constraint specification | 6.5% | 2023-08 |
| Context front- | 4.6% | 2024-02 |
| Code inclusion | 2.7% | 2023-10 |
| Multi-step instructions | 1.7% | 2023-11 |
Cost efficiency computes your Productive Output per Dollar (POD) from subscription costs and total output words. Also computes quality-adjusted POE with sensitivity analysis.
Plus: topic distribution, session dynamics, interaction style breakdown, conversation outcome classification, industry benchmark comparison, and more.
Three methodology papers are included:
: Rigorous methodology for quantifying LLM practice hours from export data. Tiered claims with sensitivity analysis and stress testing.LLM_Practice_Hours_Methodology_GIT.pdf - : Comprehensive data-driven usage profile covering model adoption (21 model versions), domain portfolio, prompt engineering effectiveness, and interaction patterns.Deep_LLM_Usage_Analysis.pdf - : The first standardized methodology for measuring individual LLM cost efficiency. Defines Productive Output per Dollar (POD), Deliverable-Linked Output per Dollar (DLOD), and quality-adjusted Productive Output Efficiency (POE).Productive_Output_Efficiency.pdf
scripts/
analyze_chatgpt.py # ChatGPT export normalization and basic stats
analyze_claude.py # Claude export normalization and basic stats
analyze_combined.py # Combined cross-platform analysis
scripts/deep_analysis/
extract_chatgpt_metadata.py # Model versions, tools, branching, reasoning
classify_and_link.py # Topic classification + project attribution
analyze_effectiveness.py # Prompt techniques, outcomes, interaction patterns
generate_tables_and_charts.py # Formatted tables and ASCII charts
compute_pod.py # Productive Output per Dollar
compute_dlod.py # Deliverable-Linked Output per Dollar
compute_poe.py # Quality-adjusted POE with sensitivity analysis
compare_benchmarks.py # Industry benchmark comparison
examples/
sample_chatgpt_export.json # Synthetic sample data for testing
sample_claude_export.json # Synthetic sample data for testing
templates/
example_projects.json # Example project config (customize for your projects)
cost_log_template.json # Template for subscription cost tracking
deliverable_inventory_template.json # Template for verified deliverables
quality_params.json # Q coefficient configurations for POE
benchmarks.json # Curated industry benchmark data with citations
docs/
DEEP_ANALYSIS_GUIDE.md # Step-by-step guide to the full pipeline
Deep_LLM_Usage_Analysis.pdf
Productive_Output_Efficiency.pdf
Prompting_One_Pager.pdf
results/
usage_snapshot_2025-12-09.md # Historical snapshot
usage_snapshot_2026-05-26.md # Current snapshot
ChatGPT: Settings > Data controls > Export data. You'll receive a ZIP containing conversations.json
(or multiple shards).
Claude: Settings > Account > Export data. You'll receive a ZIP containing conversations.json
.
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python scripts/analyze_chatgpt.py --input /path/to/conversations.json --out outputs --utc
python scripts/analyze_claude.py --input /path/to/conversations.json --out outputs --utc
python scripts/analyze_combined.py \
--chatgpt outputs/chatgpt_messages_normalized.csv \
--claude outputs/claude_messages_normalized.csv --utc
Run the basic scripts with --include-content
first, then see the Deep Analysis Guide for the full pipeline: model tracking, topic classification, prompt effectiveness, cost efficiency, and industry benchmarks.
All processing is local. No network calls. No telemetry.
By default, scripts produce privacy-minimized CSVs (timestamps, IDs, roles only). Content analysis requires the explicit --include-content
flag. Never commit or share CSVs generated with content enabled.
ChatGPT: Reconstructs the primary conversation path from the mapping tree. Walks parent pointers from the current node back to root. Extracts per-message model metadata, tool usage, branching, and reasoning information.
Claude: Extracts text blocks from content arrays. Normalizes roles to match the shared schema. Supports both web/app and Claude Code CLI sessions.
Deep analysis: Adds topic classification via keyword taxonomy with content fallback, optional project attribution from user-provided config, prompt technique detection (10 techniques tracked), conversation outcome classification, interaction style analysis, and cost efficiency computation.
- Results depend on platform export formats which may change
- ChatGPT analysis reconstructs primary path only, not full conversation trees
- Topic classification uses keyword matching (expect 40-50% "Other" for casual conversation titles)
- Project attribution requires user-provided config and represents a lower bound
- Cost metrics are designed for subscription pricing, not API pricing
- Token counts are approximate and encoding-dependent
This repository contains no platform export files and distributes no conversation data. It exists to make long-horizon LLM usage measurable, reproducible, and inspectable without turning private conversations into a dataset.