Audit every scheduled job you run
Inventories every cron and worker across Render, Kubernetes, AWS, Cloudflare, Fly and Modal, with the last time each one actually succeeded.
I want to do this: List every scheduled job across our clouds and when each one last succeeded. ## 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: Audit every scheduled job you run Steps: 1. Inventory scheduled jobs and workers across every connected provider 2. Pull each one's last runs: succeeded, failed, or simply absent 3. Flag the dead and the flaky: stopped schedules, rising failure rates 4. Check what each broken job feeds, from the context graph 5. Deliver the audit: every job, its state, and which ones need attention today 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.
Background jobs fail by going quiet.
Crons live on four platforms, and none of them page when a job just stops being scheduled. The billing export that died in June gets discovered when finance asks in September.
- No single list of what's scheduled where
- Jobs that fail silently by never running at all
- "When did this last work?" requiring provider archaeology
One prompt, this much work. Every step on your real data.
- 1 Inventory scheduled jobs and workers across every connected provider
- 2 Pull each one's last runs: succeeded, failed, or simply absent
- 3 Flag the dead and the flaky: stopped schedules, rising failure rates
- 4 Check what each broken job feeds, from the context graph
- 5 Deliver the audit: every job, its state, and which ones need attention today
Every job accounted for
One list across all providers with real last-success times, and the dead ones flagged with what they were feeding. September's surprise gets caught in June.
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.” 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.” The weekly read 
“Summarise what our telemetry says about last week: regressions, improvements, anything odd.” 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.” 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.” The weekly read “Summarise what our telemetry says about last week: regressions, improvements, anything odd.” 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.”