I'm an infrastructure engineer evaluating cloud migration options. Context: We're moving from on-prem to the cloud for a fintech backend. Output: Compare AWS, GCP, and Azure for scalability, pricing, compliance, and developer tooling. Include citations.

I'm building a real-time collaboration tool. Context: We need low-latency and scalability. Output: Compare top frameworks (e.g., SignalR, Socket.io, WebRTC) with use cases, pros/cons, and current usage by other SaaS companies. Include sources.

Benchmark the top observability tools. Context: We want to move from basic logging to full-stack monitoring. Output: Create a comparison table of features, pricing, integrations for Datadog, New Relic, Prometheus, and OpenTelemetry. Include sources.

I'm researching AI/ML adoption in logistics systems. Context: Our company is considering integrating predictive routing. Output: A 5-paragraph summary on current trends, vendors, and implementation patterns. Include citations and links.

Research best practices for GDPR/CCPA compliance so we can help kick off discussions with our legal team. Context: Our app stores sensitive user data in the EU and US. Output: A compliance checklist with citations, sorted by regulation. Include links to documentation and regulations.

I've drafted a technical design document for [insert project or feature]. Review it for clarity, architectural soundness, and completeness. Highlight any missing considerations or questions reviewers may raise.

I need to document how this internal API works for other developers. Here's the relevant code, schema, and usage examples: [insert materials]. Create clear documentation including endpoints, input/output formats, and expected behavior.

I need to create a runbook for on-call engineers supporting [insert system]. Draft one that includes sections for system overview, common alerts, diagnostic steps, and escalation procedures.

I need to write an onboarding guide for new engineers joining [insert team]. Create a draft with sections for required tools, access setup, codebase overview, and first tasks. Make it suitable for self-service onboarding.

Based on this engineering spec for [insert task or feature], write a JIRA ticket that includes the problem statement, context, goals, acceptance criteria, and technical notes for implementation.

A system in production is intermittently failing, and we're struggling to isolate the root cause. Based on the following logs, metrics, and recent changes: [insert context], help identify the most likely causes and suggest next steps for mitigation.

Our service is experiencing latency and degraded performance during peak usage. Here are metrics, logs, and relevant traces: [insert context]. Help identify the bottlenecks and recommend specific optimizations.

A critical data pipeline failed in yesterday's run. Here are the logs, data volume trends, and error outputs: [insert context]. Analyze what likely went wrong and provide recommendations to prevent recurrence.

We currently use [insert tools] for monitoring [insert service]. Review our observability setup and suggest improvements across metrics, logging, alerting, and dashboards to improve issue detection and debugging.

We're preparing test cases for [insert feature/system]. Brainstorm potential edge cases and failure scenarios that may not be covered by standard testing, including unusual user inputs, system state changes, and concurrency issues.

Analyze this CSV of product usage logs. Context: We want to identify usage trends over time and across user segments. Output: Summary stats + line or bar charts highlighting key trends.

Plot error rates over time from this dataset. Context: It contains application logs from the last month. Output: A time-series chart with callouts for error spikes and a short interpretation.

Analyze this set of performance test results. Context: It compares two versions of our backend service. Output: Side-by-side comparison charts + text summary of improvements or regressions.

Analyze this bug report dataset. Context: Each row includes severity, frequency, and affected users. Output: A prioritized list of top bugs with charts showing frequency vs. severity.

Summarize this user feedback CSV. Context: It includes ratings and open text responses from a recent survey. Output: Key themes, sentiment scores, and charts showing distribution of ratings.