Get the week's story from the telemetry
Reads the week across Datadog, Axiom, Better Stack, Sentry and Cloudflare: what regressed, what improved, what got strange.
I want to do this: Summarise what our telemetry says about last week: regressions, improvements, anything odd. ## Setup (skip if Polylane is already set up) Read and follow https://polylane.com/auth.md for non-interactive signup and setup. Start by checking whether I am already signed in; reuse my account and workspace. If the CLI is missing, bootstrap it without starting the interactive wizard: curl -fsSL 'https://polylane.com/setup?ref=prompts' | bash -s -- --install-only Then follow the guide through email verification, workspace selection, source connections, and MCP authentication. Ask me for an email code or OAuth consent only when needed. Verify each step; report pending setup instead of claiming success from installation alone. ## How to work Over MCP: searchTools lists what this workspace exposes, with each tool's schema; call it first. runTool runs one tool, runCode chains several in one call and returns just the answer. search and execute cover the full Polylane REST API: threads, issues, investigations, autofixes, memories. From the terminal: the polylane CLI wraps the same API, with structured output and non-interactive flags everywhere. Reads always work. Write tools appear only if I have opted in, and every write is screened. ## Task: Get the week's story from the telemetry Steps: 1. Read the week's series across every connected provider 2. Split the movement: regressions, improvements, and the genuinely odd 3. Attach likely causes from the week's change records where they line up 4. Note what deserves a check that doesn't have one yet 5. Deliver the weekly read, each item with its series and evidence Ground every claim in data you actually pulled: the query, the log line, the change record. If the data is inconclusive, say so. Ask me before anything that writes.
The only telemetry review is the incident review.
Between pages, nobody reads the signals. Slow regressions compound, wins go unnoticed, and the weird blip from Tuesday gets remembered only when it recurs as an outage.
- Trends reviewed only after they become incidents
- Improvements nobody noticed or credited
- That odd Tuesday spike, never explained
One prompt, this much work. Every step on your real data.
- 1 Read the week's series across every connected provider
- 2 Split the movement: regressions, improvements, and the genuinely odd
- 3 Attach likely causes from the week's change records where they line up
- 4 Note what deserves a check that doesn't have one yet
- 5 Deliver the weekly read, each item with its series and evidence
A week of signals in five minutes
The story your telemetry was telling all week, read and summarised with causes attached. Regressions get caught as trends; the odd blip gets explained on Tuesday.
The quiet failure 
“Find things that look wrong but never alerted: error spikes, dead crons, growing queues.” Know the account 
“Map our AWS account: what's running, what depends on what, and what's critical?” Risky config 
“Find the risky configuration: public buckets, missing alarms, single points of failure.” The cron audit “List every scheduled job across our clouds and when each one last succeeded.” Close the gaps 
“Which critical services have no logs or traces? Show the gaps and write the fixes.” Read my dashboards 
“Go through our dashboards and tell me what's drifting from normal this week.” Query to check “Write the query for checkout error rate by region over 24 hours, and save it as a check.” Coverage gaps “Which routes swallow errors or log nothing? Show the gaps and the fixes.” Watch this deploy “We're deploying checkout-api at 5pm. Watch the metrics after and flag anything that moves.” Onboard me “I'm new here. Walk me through the architecture: the critical path, the data stores, what talks to what.” Instrument the launch “payments-v2 ships next week. What instrumentation should it have before launch? Draft it.”
“Find things that look wrong but never alerted: error spikes, dead crons, growing queues.” Know the account “Map our AWS account: what's running, what depends on what, and what's critical?” Risky config “Find the risky configuration: public buckets, missing alarms, single points of failure.” The cron audit “List every scheduled job across our clouds and when each one last succeeded.” Close the gaps 
“Which critical services have no logs or traces? Show the gaps and write the fixes.” Read my dashboards “Go through our dashboards and tell me what's drifting from normal this week.” Query to check “Write the query for checkout error rate by region over 24 hours, and save it as a check.” Coverage gaps “Which routes swallow errors or log nothing? Show the gaps and the fixes.” Watch this deploy “We're deploying checkout-api at 5pm. Watch the metrics after and flag anything that moves.” Onboard me “I'm new here. Walk me through the architecture: the critical path, the data stores, what talks to what.” Instrument the launch “payments-v2 ships next week. What instrumentation should it have before launch? Draft it.”