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 skills add RelationalAI/rai-agent-skills --skill rai-healthgit clone --depth 1 https://github.com/RelationalAI/rai-agent-skillsWrote 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/relationalai/rai-agent-skills/rai-health)<a href="https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-health"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-health/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-health"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-health.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00061 | $0.05395 |
| Opus 5 | $0.00030 | $0.02697 |
| Sonnet 5 | $0.00012 | $0.01079 |
| Haiku 4.5 | $0.00006 | $0.00539 |
Grade A, and why
rai-health 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Summary
What: A process skill for diagnosing RAI operational health across four domains — reasoner performance (memory/CPU/demand), failed transactions, CDC / data-stream health, and CDC engine management — each with decision tables and remediation actions.
When to use:
- Reasoner is slow, stuck, or queuing; need to check memory, CPU, or demand metrics
- Observability views need setup, role grants, or dashboard/alerting work
- A transaction was aborted;
get_transaction_problems,get_own_transaction_problems, orget_load_errorsmust be called - Batch processing has failed and load errors need inspection
- A CDC task is suspended, a data stream is quarantined, or
resume_cdcis needed - CDC engine needs resizing (
alter_cdc_engine_size) or force-deletion
When NOT to use:
- Writing PyRel models or query logic → see
rai-pyrel; authentication or initial setup → seerai-setup - Managing solver optimization problems → see
rai-prescriptive-problem(formulation, solver selection) andrai-prescriptive-results(execution, diagnostics)
Overview (process steps):
- Verify observability is set up (events view registered and healthy)
- Query the three metric views: memory, CPU, demand
- Apply threshold-based decision rules and prescribe the exact remediation action
- Diagnose a failed transaction (get_transaction, get_load_errors, owner-restriction pitfall)
- Diagnose CDC / data stream health (errors, batches, quarantine recovery, resume_cdc)
- Manage the CDC engine (alter_cdc_engine_size, force delete, cdc_status)
Navigation: Steps 1–3 cover reasoner health only. For CDC/stream issues go directly to Step 5. For transaction failures go directly to Step 4. For CDC engine sizing or force-delete go directly to Step 6.
Quick Reference
The Three Metric Views (all in OBSERVABILITY_PREVIEW)
| View | Key Column | Healthy Signal |
|---|---|---|
logic_reasoner__memory_utilization |
MEMORY_UTILIZATION (0.0–1.0) |
< 0.80 on most runs |
logic_reasoner__cpu_utilization |
CPU_UTILIZATION (0.0–1.0) |
< 0.85 sustained; < 0.95 peak |
logic_reasoner__demand |
DEMAND (0.0+) |
≤ 1.0 (> 1.0 = queuing) |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 500 lines · 61 tokens per session scan A 77fecee76156
rai-health is a skill published in the GitHub repository RelationalAI/rai-agent-skills (4 stars, last pushed 2d ago), licensed Apache-2.0. It adds 61 tokens to every session and 5,395 once invoked, about $0.0003 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 2026-08-31.
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