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 analyze-dashboardgit 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/analyze-dashboard)<a href="https://agentmods.dev/skills/ccfos/nightingale/analyze-dashboard"><img src="https://agentmods.dev/badge/skills/ccfos/nightingale/analyze-dashboard.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 | $0.00088 | $0.01578 |
| Opus 5 | $0.00044 | $0.00789 |
| Sonnet 5 | $0.00018 | $0.00316 |
| Haiku 4.5 | $0.00009 | $0.00158 |
Grade A, and why
analyze-dashboard 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 7d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Nightingale (N9E) Analyze Dashboard
Help the user analyze the data health of an existing dashboard over a given time window, and produce an anomaly list with recommendations.
The core tool is get_dashboard_data: it performs data fetching and statistical pre-screening on the server side (four deterministic detections — MAD outliers, sudden changes, trends, year-over-year comparison — plus periodicity denoising), and returns layered results. Year-over-year baseline: for a window ≤24h it compares against the same period yesterday; for a longer window it compares against the previous period of the same length (the digest header states the shift amount — describe it accordingly). The detection is already done; your job is attribution, correlation, and explanation, not re-scanning point by point.
Step 1: Locate the dashboard
- The context already carries a
dashboard_id(injected by the frontend from/dashboards/<id>) → use it directly. - The user pasted a
/dashboards/<id>link → take the id. - Only a name was given → match by name with
list_dashboards(query="..."); if there are multiple candidates or no match, list them and ask the user — do not guess.
Step 2: Call get_dashboard_data
get_dashboard_data(id=<id>, time_range="24h")
- Pass
time_rangeaccording to user intent ("the last day" →24h, "this week" →7d); if the user did not specify, use the default1hand note the analysis window in the conclusion. - The user constrained host/cluster, etc. ("look at the host web01") → pass it via
vars:vars={"ident":["web01"]}(the variable name follows the variable definition inget_dashboard_detail(include_config=true); if unsure, check it first). - One call is enough. Do not first call get_dashboard_detail and then run query_prometheus over every panel at full scale — that is what the tool has already done for you.
Step 3: Interpret the pre-screening results (your core value)
The tool returns layered results: ⚠ suspicious curves (features + sampled points) / ✓ normal summary / skipped list (including a flat-line category — curves with a constant value are counted separately; if a curve is flat but at a different level than yesterday, it goes into the suspicious section as a year-over-year anomaly, e.g. qps stuck at 0). What you should do:
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.
- 7d ago First seen · 87 lines · 88 tokens per session scan A b2bb7d2efe83
analyze-dashboard is a skill published in the GitHub repository ccfos/nightingale (13,280 stars, last pushed 2d ago), licensed Apache-2.0. It adds 88 tokens to every session and 1,578 once invoked, about $0.0004 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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