prompt-analysis

Prompt Analysis Skill

Analyze AI prompting patterns using the local prompts.db SQLite database.

What is Git AI?

Git AI is a tool that tracks AI-generated code and prompts in git. It stores:

• Every AI conversation (prompts and responses)
• Which lines of code came from AI vs human edits
• Acceptance rates (how much AI code was kept vs modified)
• Associated commits and authors

This skill queries that data to help users understand their AI coding patterns.

Initialization

First, determine scope from the user's question:

User mentions	Flags to use
"my prompts" or nothing specified	(default - current user, current repo)
"team", "everyone", "all authors"	--all-authors
specific person's name	--author "<name>"
specific time range	--since <days> (default: 30)

Discovery is notes-only — git-ai prompts always operates on the current repository (the working directory must be inside a git repo). To analyze multiple repos, run the command separately in each.

Run initialization:

git-ai prompts [flags]

This creates/updates prompts.db in the current directory.

Schema Reference

The prompts table contains:

• seq_id - Auto-increment ID for iteration
• id - Unique prompt identifier
• tool - Tool used (e.g., "claude-code", "cursor")
• model - Model name (e.g., "claude-sonnet-4-20250514")
• human_author - Git user who created the prompt
• commit_sha - Associated commit (if any)
• total_additions, total_deletions - Lines of code changed
• accepted_lines, overridden_lines - Lines kept vs modified by human
• accepted_rate - Ratio: accepted / (accepted + overridden)
• messages - JSON array of the conversation
• start_time, last_time - Unix timestamps

Analysis Approaches

For aggregate questions (metrics, comparisons)

Use direct SQL queries:

git-ai prompts exec "SELECT model, AVG(accepted_rate), COUNT(*) FROM prompts GROUP BY model"