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If you use Claude Code regularly, you quickly accumulate a large amount of interaction data. CCQL makes that data queryable with SQL so you can analyze how you actually use the agent.
*Examples*
```bash # What tools does Claude use most? ccql "SELECT tool_name, COUNT() AS uses FROM transcripts GROUP BY tool_name ORDER BY uses DESC LIMIT 10"
# Find prompts mentioning a topic ccql "SELECT display, timestamp FROM history WHERE display LIKE '%authentication%'"
# Cross-session analysis ccql "SELECT _session_id, COUNT() FROM transcripts GROUP BY _session_id" ```
*Built-in commands*
```bash ccql prompts --session ses_123 ccql duplicates --threshold 0.8 ccql search "error" ```
*Interesting technical bit* Claude Code stores its data in a structured local format. CCQL loads that data into an embedded SQL engine (GlueSQL) with a clean, read-first model, so you can safely explore usage patterns without mutating anything.
*Features*
* SQL queries across history, transcripts, prompts, sessions, and todos * Fuzzy duplicate detection (find repeated or near-duplicate prompts) * Full-text search with regex * Safe writes with automatic backups * Output formats: table, JSON, JSONL
*Install*
```bash # macOS brew install douglance/tap/ccql
# npm (any platform) npx @douglance/ccql "SELECT * FROM history LIMIT 10"
# Cargo cargo install ccql ```
Built in Rust using GlueSQL. MIT licensed.
GitHub: [https://github.com/douglance/ccql](https://github.com/douglance/ccql)
I’ve been using this to understand my own Claude usage patterns—what prompts I repeat, which tools get called most, and how conversations evolve across sessions. Curious what queries others would find useful.
