# Production Debugging

> Investigate live or production-like .NET failures using logs, traces, metrics, dumps, and configuration evidence. Use for production diagnosis; do not mutate production state or deploy fixes without explicit authorization.

- Skill: `dprakash2101/production-debugging` (Agent Skill)
- Install (CLI): `npx skillmds@latest add dprakash2101/production-debugging`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dprakash2101/production-debugging/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: dprakash2101 (https://skillmd.com/u/dprakash2101)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/dprakash2101/production-debugging

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# .NET production debugging

Start with an incident timeline, impact, affected versions/instances/tenants, correlation identifiers, and recent deployments/configuration changes. Protect customer data and credentials in every query and output.

Form falsifiable hypotheses and request the smallest evidence needed: structured logs, distributed traces, relevant metrics, health/dependency signals, configuration provenance, or a narrowly captured dump/profile. Correlate by time and request/message identity; distinguish cause from downstream noise.

For high CPU, memory, thread-pool starvation, deadlock, or crash analysis, capture diagnostics with production-safe tooling and bounded duration/size. Account for collection overhead and retention/security rules. Never attach a debugger, restart, scale, change flags, clear queues/caches, or capture sensitive dumps without explicit authorization.

Conclude with verified facts, root cause or ranked hypotheses, evidence gaps, safe mitigation/fix options, and a validation/monitoring plan. Keep production diagnosis separate from authorization to change production.