The Scenario
Your post-incident review is scheduled for 11 AM Thursday. It's Wednesday evening, and you're putting together the report — the one that's supposed to show the timeline of what broke, when, and how bad.
The data is all in Grafana. Seven days of API error rates and latency for the service that went down. It's sitting there in two panels that your team has been watching all week.
What you're tempted to do is the obvious thing:
The bad version:
- Navigate into the API Error Rate panel, switch to table view, set the time range to the incident window, export the CSV, open it separately, and paste the values into your Sheet — then repeat the whole process for the Latency panel.
- Discover that Grafana exported the timestamps in epoch milliseconds, not a readable format, and spend fifteen minutes converting them in a separate column.
- Realize the column headers exported with the internal metric name (
http_request_duration_seconds_p99) instead of the panel title, and rename them manually before the report is shareable.
You are supposed to be writing the actual post-mortem analysis. Instead you're doing timestamp arithmetic and renaming columns at 9 PM the night before the review.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, connects to Grafana through its built-in integration, and pulls panel data directly — without you touching the Grafana UI or a CSV file.
Query the panel named 'API Error Rate' from our public Grafana dashboard (UID: abc123) for the last 7 days and paste the time-series values into columns A and B with headers Timestamp and Error Rate.
What You Get
- Column A: timestamps in ISO 8601 format (readable, not epoch milliseconds)
- Column B: error rate values as decimals, one row per data point
- Row 1: headers ("Timestamp", "Error Rate") written before the data
- If the panel returns no data for part of the time range, the gap is represented with blank rows rather than silently skipped
What If the Data Is Not Quite Ready
The dashboard has multiple panels and you need all of them
Pull data from the panels named 'API Error Rate', 'P99 Latency', 'Request Volume', and 'Service Uptime' from our Grafana dashboard (UID: abc123) for the incident window from 2026-05-06T14:00 to 2026-05-06T18:30 UTC and write each panel's data into a separate tab named after the panel title.
The time range needs to match a specific incident window, not "last 7 days"
Query the 'API Error Rate' panel from Grafana dashboard UID abc123 for the time range 2026-05-06T14:00:00Z to 2026-05-06T18:30:00Z and paste timestamp and value pairs into columns A and B starting at row 3. Leave rows 1 and 2 for the report header I already have there.
The panel data uses internal metric names and you need human-readable headers
Pull data from the panel named 'API Error Rate' on Grafana dashboard UID abc123 for the last 7 days. The metric in the panel is labeled 'http_requests_total{status="5xx"}' — write that into column B but set the header in row 1 to 'Error Count' instead of the raw metric name.
Clean up the data, flag anomalies, and write the narrative summary in one shot
Pull the 'API Error Rate' and 'P99 Latency' panels from Grafana dashboard UID abc123 for the 7 days ending 2026-05-06. Write the data into columns A–D (Timestamp, Error Rate, Timestamp, P99ms). Then in column F, flag any row where Error Rate exceeds 0.05 with "SPIKE" and where P99ms exceeds 800 with "LATENCY". Finally write a two-sentence summary of the incident pattern into cell F1.
One prompt. Cleanup, flagging, and the executive summary — all in the same ask.
Try It
Open your post-incident report Sheet, paste in the Grafana dashboard UID and the incident time range, and ask SheetXAI to pull the panels you need for the timeline. Get the 7-day free trial of SheetXAI — the Grafana integration is built in. Once you've got the panel data working, the Grafana instance health audit spoke shows how to document your infrastructure status in the same workflow. See also the Grafana hub overview for all the methods compared.