Nightingale is an open-source monitoring and alerting system that connects to stored metrics and log data, evaluates alert rules, and distributes notifications. Operations teams use it to manage alarms and explore observability data alongside existing data sources and collectors. Catalogue add-ons provide skills for operating Nightingale.
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 ccfos/nightingale --skill host-onboard-diagnosegit clone --depth 1 https://github.com/ccfos/nightingaleWrote 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/ccfos/nightingale/host-onboard-diagnose)<a href="https://agentmods.dev/skills/ccfos/nightingale/host-onboard-diagnose"><img src="https://agentmods.dev/badge/skills/ccfos/nightingale/host-onboard-diagnose/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/ccfos/nightingale/host-onboard-diagnose"><img src="https://agentmods.dev/badge/skills/ccfos/nightingale/host-onboard-diagnose.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00217 | $0.02275 |
| Opus 5 | $0.00109 | $0.01137 |
| Sonnet 5 | $0.00043 | $0.00455 |
| Haiku 4.5 | $0.00022 | $0.00228 |
Grade A, and why
host-onboard-diagnose scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- `curl -s http://<n9e>:17000/api/n9e/self-metrics | grep <ident>`> How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Host Onboarding Failure Diagnosis (host-onboard-diagnose)
Scope
Enter this skill:
- "My newly installed categraf host doesn't show up in Nightingale"
- "The agent is installed and running, but the host doesn't appear in the host list"
- "For this host in the list, the OS / CPU / version are all unknown"
- "I deployed 3 categraf hosts via Helm, but the platform only sees 1"
- "I installed the agent on Windows but it won't register"
- "The host disappeared right after I changed its hostname" (if categraf is still running)
Do NOT enter this skill:
- Was visible before, recently lost contact →
host-health-diagnose - ident duplicate / want to clean up residue after renaming →
host-ident-cleanup(to be built) - Want to change alert rules / mutes →
creation/create-alert-rule - Looking into why an alert didn't fire →
alert-rule-troubleshoot
One-Sentence Principle
A missing host ≠ a single cause. The onboarding pipeline has 5 segments, and each segment getting stuck looks different. Looking at just one segment and telling the user to change config is a common pitfall. Gather evidence first, then localize segment by segment, and finally give fix commands.
The 5 Segments of the Onboarding Pipeline
[1] categraf local process Is it present / is the config correct / is heartbeat.enable on
│
[2] heartbeat report HTTP Can it reach /v1/n9e/heartbeat (network / TLS / BasicAuth)
│
[3] server / edge receive token / version compatibility / hostname duplicate check
│
[4] target table persistence Is this ident in the DB, is the meta in redis
│
[5] Redis + metric stream Can the time-series store find samples for this ident
First Action: Call probe_target_onboard_status
This is the only diagnostic entry tool in this skill; it returns the footprint of all 5 segments in one shot. Always call it first, then decide the next step.
Key fields returned:
in_target_db+target.os+target.agent_version→ evidence for segments 3/4in_redis_beat+redis_meta.hostname+redis_meta.remote_addr→ evidence for segment 4in_prom_target_up+target_up_last+prom_metrics_hit→ evidence for segment 5likely_segment+likely_causes→ diagnosis already aggregated at the tool layer; do not bypass it and re-derive it yourself
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.
- 10d ago First seen · 142 lines · 217 tokens per session scan A 2dc016fe2745
host-onboard-diagnose is a skill published in the GitHub repository ccfos/nightingale (13,282 stars, last pushed yesterday), licensed Apache-2.0. It adds 217 tokens to every session and 2,275 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
cis-aws-database-10.8
Ensure Monitoring and Alerting is Enabled.
datadog
Full-stack observability with Datadog APM, logs, metrics, synthetics, and RUM. Use when implementing monitoring, tracing, alerting, or cost optimization for production systems.
Verification & Quality Assurance
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
phoenix-observability
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
github-ci-fix
Fix failing GitHub CI / Actions checks via fixgithubprci and push to the existing PR head, or fix a branch's failing CI via a linked repair worktree.