Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jmagly/aiwg/ralph-analyticsnpx skills add jmagly/aiwg --skill ralph-analyticsgit clone --depth 1 https://github.com/jmagly/aiwgWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jmagly/aiwg/ralph-analytics)<a href="https://agentmods.dev/skills/jmagly/aiwg/ralph-analytics"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/ralph-analytics.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.00400 |
| Opus 5 | $0.00007 | $0.00200 |
| Sonnet 5 | $0.00003 | $0.00080 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
ralph-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Al Analytics Command
Display aggregate analytics and metrics from agent loop execution history.
Instructions
When invoked, analyze agent loop data and present metrics:
-
Scan Loop History
- Load all loop records from
.aiwg/ralph/ - Load reflections from
.aiwg/ralph/reflections/ - Load debug memory from
.aiwg/ralph/debug-memory/
- Load all loop records from
-
Calculate Metrics
- Success rate: % of loops that completed successfully
- Average iterations: Mean iterations to completion
- Reflection reuse rate: % of reflections applied in subsequent loops
- Stuck loop rate: % of loops that hit stuck detection
- Escalation rate: % requiring human intervention
-
Pattern Analysis
- Most common failure types
- Most effective fix patterns
- Average time per iteration
- Quality trajectory per loop
-
Display Dashboard
- Summary metrics table
- Trend indicators (improving/stable/degrading)
- Recommendations for improvement
Arguments
--since [date]- Analyze loops from date (default: all)--loop [id]- Analyze specific loop--export [path]- Export analytics to file--brief- Show summary only
References
- @$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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 53 lines · 14 tokens per session scan A 844cd4b8edbe
ralph-analytics is a skill published in the GitHub repository jmagly/aiwg (209 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 400 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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