Best use case
ralph-analytics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Show analytics and metrics from agent loop execution history
Teams using ralph-analytics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ralph-analytics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ralph-analytics Compares
| Feature / Agent | ralph-analytics | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Show analytics and metrics from agent loop execution history
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Al Analytics Command Display aggregate analytics and metrics from agent loop execution history. ## Instructions When invoked, analyze agent loop data and present metrics: 1. **Scan Loop History** - Load all loop records from `.aiwg/ralph/` - Load reflections from `.aiwg/ralph/reflections/` - Load debug memory from `.aiwg/ralph/debug-memory/` 2. **Calculate Metrics** - **Success rate**: % of loops that completed successfully - **Average iterations**: Mean iterations to completion - **Reflection reuse rate**: % of reflections applied in subsequent loops - **Stuck loop rate**: % of loops that hit stuck detection - **Escalation rate**: % requiring human intervention 3. **Pattern Analysis** - Most common failure types - Most effective fix patterns - Average time per iteration - Quality trajectory per loop 4. **Display Dashboard** - Summary metrics table - Trend indicators (improving/stable/degrading) - Recommendations for improvement ## Arguments - `--since [date]` - Analyze loops from date (default: all) - `--loop [id]` - Analyze specific loop - `--export [path]` - Export analytics to file - `--brief` - Show summary only ## References - @$AIWG_ROOT/agentic/code/addons/ralph/schemas/reflection-memory.json - Reflection schema - @$AIWG_ROOT/agentic/code/addons/ralph/schemas/debug-memory.yaml - Debug memory schema - @$AIWG_ROOT/agentic/code/addons/ralph/docs/reflection-memory-guide.md - Guide
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