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- debugging strategies
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debugging strategies
Systematic root cause analysis and evidence-based debugging plans for complex software failures.
$8
debugging strategies
Example session with this skill installed
I have a Node.js API that occasionally throws a 500 error on the /users/update endpoint. Logs show a 'ConnectionTimeoutError' from the database, but it only happens under high load.
- Read your context and instructions
- Compiled the debugging strategies
I have analyzed the reported database timeout issue. Below is the systematic debugging plan to isolate the root cause.
Debugging Strategy: DB Connection Timeout
| Phase | Activity | Details |
|---|---|---|
| Evidence | Current Logs | ConnectionTimeoutError observed on /users/update. |
| Evidence | Missing Info | Connection pool settings, DB CPU metrics, network latency logs. |
| Hypothesis 1 | Pool Exhaustion | The connection pool is too small for the peak concurrent requests. |
| Hypothesis 2 | Long-running Query | An unindexed update query is locking the table, blocking other connections. |
| Hypothesis 3 | Resource Leak | Database connections are not being released back to the pool in error blocks. |
Isolation Steps
Load Test Reproduction: Run a k6 script targeting the endpoint with 50, 100, and 500 VUs to see if the error rate scales with load.
2. Pool Monitoring: Log pool.waitingCount and pool.totalCount during the load test.
3. Query Profiling: Enable the slow query log on the database to identify if /users/update queries exceed 200ms.
Fix Validation Plan
- Metric Baseline: Establish current failure rate (e.g., 5% at 200 req/s).
- Load Comparison: Apply fix (e.g., pool increase or indexing) and re-run the k6 script under identical load.
- Leak Check: Monitor long-term connection counts to ensure stability over 24 hours.
Next steps
- Provide the current database connection pool configuration (e.g.,
max,min,idleTimeout). - Check the database server metrics for CPU or Disk I/O spikes coinciding with the logs.
- Share the repository's database client initialization code.
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What you get
About this skill
The problem
Debugging often descends into "shotgun debugging" where developers change code blindly, hoping to fix the issue. This waste of time lacks a systematic way to isolate root causes, resulting in fragile fixes that don't address the underlying failure.
What it does
- Generates structured reproduction plans to confirm bugs before attempting fixes.
- Analyzes stack traces, logs, and environment details to build an evidence register.
- Formulates and prioritizes testable hypotheses to isolate specific failure points.
- Defines clear validation steps and post-fix checks to prevent regressions.
- Identifies missing diagnostic information instead of making assumptions about system state.
Why this beats prompting it yourself
General-purpose LLMs often jump straight to suggesting code fixes that might not apply to your specific environment. This skill enforces a strict evidence-first protocol, ensuring you don't waste cycles on unverified assumptions. It provides a formal framework for root cause analysis that stays focused on your logs and stack traces.
Use cases
- Isolating intermittent race conditions or memory leaks in production logs.
- Analyzing complex stack traces from crash reports to find the entry point of failure.
- Developing a step-by-step plan to reproduce a regression found in a specific environment.
- Performing a post-mortem root cause analysis after a critical system incident.
Known limitations
Requires logs, stack traces, or specific reproduction steps to be effective. It cannot execute commands or modify production systems directly.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
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- 2
Unzip into your skills folder
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- 3
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