dp stats

Show summary statistics

Usage

dp stats [flags]

Flags

Examples

$ dp stats
Desire Statistics

Total desires: 1,247
Unique tool patterns: 47
Unique sources: 3
Date range: 2026-01-15 09:23:11 to 2026-02-09 14:32:15

Activity windows:
  Last 24 hours: 89 desires
  Last 7 days: 412 desires
  Last 30 days: 1,247 desires

Top sources:
  claude-code: 1,089 (87.3%)
  cursor: 142 (11.4%)
  copilot: 16 (1.3%)

Top desires:
  read_file: 142 (11.4%)
  file_read: 89 (7.1%)
  grep_search: 67 (5.4%)
  edit_document: 45 (3.6%)
  write_file: 38 (3.0%)

Top tools (by alias):
  Read: 231 (18.5%)
  Grep: 98 (7.9%)
  Edit: 76 (6.1%)
  Write: 57 (4.6%)
  Bash: 52 (4.2%)

$ dp stats --invocations
Invocation Statistics

Total invocations: 8,432
Successful: 7,185 (85.2%)
Failed: 1,247 (14.8%)
Unique tools: 23
Unique sources: 3
Date range: 2026-01-15 09:23:11 to 2026-02-09 14:32:15

Activity windows:
  Last 24 hours: 645 invocations (89 failures)
  Last 7 days: 3,128 invocations (412 failures)
  Last 30 days: 8,432 invocations (1,247 failures)

Top sources:
  claude-code: 7,344 (87.1%)
  cursor: 1,028 (12.2%)
  copilot: 60 (0.7%)

Top tools:
  Read: 2,847 (33.8%)
  Bash: 1,923 (22.8%)
  Edit: 1,204 (14.3%)
  Write: 891 (10.6%)
  Grep: 745 (8.8%)

Failure rates by tool:
  Read: 231/2,847 (8.1%)
  Grep: 98/745 (13.2%)
  Edit: 76/1,204 (6.3%)
  Write: 57/891 (6.4%)
  Bash: 52/1,923 (2.7%)

Details

The stats command provides a high-level overview of your desire_path data. It’s useful for:

• Understanding the scale of tool call failures
• Identifying which AI tools have the most integration issues
• Tracking improvement over time
• Prioritizing which patterns to fix first

By default, stats shows desire (failure) data. Use --invocations to see statistics about all tool invocations, both successful and failed. Invocation tracking must be enabled with dp init --track-all for this data to be available.

Activity windows show rolling counts for the last 24 hours, 7 days, and 30 days. This helps identify trends: is the failure rate increasing, decreasing, or stable?

Top sources reveal which AI tools are generating the most failures. A high failure rate from one source might indicate a configuration issue or incompatibility.

Top desires show the most frequently attempted tool names. These are your highest-priority candidates for creating aliases.

When invocation tracking is enabled, the failure rate breakdown shows which tools have the highest error rates. This can reveal whether certain tools are more prone to naming mismatches or integration issues.

Run stats periodically to track the health of your AI coding tool integrations.