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_range according to user intent ("the last day" → 24h, "this week" → 7d); if the user did not specify, use the default 1h and 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 in get_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:

1. Cross-panel correlation: simultaneous movement of multiple curves at the same moment is the most important signal. For example, 14:32 CPU sudden change +312% co-occurring with 14:32 disk latency outlier → likely the same event; merge them into a single problem statement rather than listing them as two.
2. Judge impact in light of metric semantics: rising WAL fsync latency → writes are affected; a rising connection-count trend approaching the limit → capacity risk. Talk about "what this means", not re-reading the numbers.
3. Re-examine the "suspected periodic" flag: spikes flagged as periodic are usually scheduled jobs and generally do not count as anomalies; but if the user is specifically asking "why is it slow at this same time every day", it is in fact the answer.
4. Drill down when necessary: for a key suspicious metric, use query_prometheus to narrow the time window and inspect details (e.g. ±15 minutes around the sudden change, with a smaller step), or query a related metric to validate a hypothesis. Drill down into at most 2-3 of the most critical ones; do not query every suspicious curve.
5. Do not recite the sampled points: the attached "points:" series is evidence for you to see the shape; in the conclusion just cite the key moments and values.

Step 4: Output the analysis report

## <dashboard name> Health Analysis (window 24h)

**Overall conclusion**: one sentence (e.g.: found 1 suspected disk performance event affecting the etcd-2 node; all other metrics normal)

### Anomalies (by severity)
1. **etcd-2 disk performance degradation (starting at 14:30)**
   - Evidence: WAL fsync latency 4ms→89ms (sudden change +312%@14:30, no such phenomenon in the year-over-year window); CPU spiked to 96% at the same moment
   - Impact: write latency will be driven up directly
2. ...

### Correlation analysis
(correlation inference of multiple metrics over time)

### Recommendations
- Actionable next steps (check the disk of the host where etcd-2 resides / iostat / whether there is a snapshot job...)

• When there are no suspicious curves, state clearly "no anomalies found"; do not fabricate problems. Note the coverage (window, skipped panels).
• The output was truncated (very large dashboard) → use panel_ids to re-call key panels in batches, then merge the conclusions at the end.

Boundaries and special cases

• The dashboard has no Prometheus panels at all → the tool returns an error and lists the data source distribution; tell the user directly that this is not supported for now.
• When a variable resolves to no value, the tool notes "matched everything with .*"; the data for a multi-instance dashboard may be excessive, so carry this premise in the conclusion.
• A curve that "existed yesterday but is gone today" (an instance disappeared) is worth a mention in the anomalies — losing an instance is often more serious than metric fluctuation. Note that when the window is >24h, this segment actually compares against the previous period of the same length (e.g. the previous 7 days), so do not phrase it as "it was still there yesterday".
• The user then wants to modify the dashboard (add charts / change thresholds) → load modify-dashboard; to create an alert on an anomalous metric → load create-alert-rule.

Notes

• In the report, always use the moment format returned by the tool (HH:MM); do not convert time zones yourself.
• Keep the precision the tool gives for numbers; the focus is the trend and order of magnitude, not decimal places.
• Suspicious ≠ failure: the detection is statistical, so word the conclusion as "suspected/possible" and make the basis for the judgment clear.