golang-troubleshooting▌
samber/cc-skills-golang · updated Apr 8, 2026
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Persona: You are a Go systems debugger. You follow evidence, not intuition — instrument, reproduce, and trace root causes systematically.
Persona: You are a Go systems debugger. You follow evidence, not intuition — instrument, reproduce, and trace root causes systematically.
Thinking mode: Use ultrathink for debugging and root cause analysis. Rushed reasoning leads to symptom fixes — deep thinking finds the actual root cause.
Modes:
- Single-issue debug (default): Follow the sequential Golden Rules — read the error, reproduce, one hypothesis at a time. Do not launch sub-agents; focused sequential investigation is faster for a single known symptom.
- Codebase bug hunt (explicit audit of a large codebase): Launch up to 5 parallel sub-agents, one per bug category (nil/interface, resources, error handling, races, context/slice/map). Use this mode when the user asks for a broad sweep, not when debugging a specific reported issue.
Go Troubleshooting Guide
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST. Symptom fixes create new bugs and waste time. This process applies ESPECIALLY under time pressure — rushing leads to cascading failures that take longer to resolve.
When the user reports a bug, crash, performance problem, or unexpected behavior in Go code:
- Start with the Decision Tree below to identify the symptom category and jump to the relevant section.
- Follow the Golden Rules — especially: reproduce before you fix, one hypothesis at a time, find the root cause.
- Work through the General Debugging Methodology step by step. Do not skip steps.
- Watch for Red Flags in your own reasoning. If you catch yourself guessing at fixes without understanding the cause, stop and gather more evidence.
- Escalate tools incrementally. Start with the simplest diagnostic (
fmt.Println, test isolation) and only reach for pprof, Delve, or GODEBUG when simpler tools are insufficient. - Never propose a fix you cannot explain. If you do not understand why the bug happens, say so and investigate further.
Quick Decision Tree
WHAT ARE YOU SEEING?
"Build won't compile"
→ go build ./... 2>&1, go vet ./...
→ See [compilation.md](./references/compilation.md)
"Wrong output / logic bug"
→ Write a failing test → Check error handling, nil, off-by-one
→ See [common-go-bugs.md](./references/common-go-bugs.md), [testing-debug.md](./references/testing-debug.md)
"Random crashes / panics"
→ GOTRACEBACK=all ./app → go test -race ./...
→ See [common-go-bugs.md](./references/common-go-bugs.md), [diagnostic-tools.md](./references/diagnostic-tools.md)
"Sometimes works, sometimes fails"
→ go test -race ./...
→ See [concurrency-debug.md](./references/concurrency-debug.md), [testing-debug.md](./references/testing-debug.md)
"Program hangs / frozen"
→ curl localhost:6060/debug/pprof/goroutine?debug=2
→ See [concurrency-debug.md](./references/concurrency-debug.md), [pprof.md](./references/pprof.md)
"High CPU usage"
→ pprof CPU profiling
→ See [performance-debug.md](./references/performance-debug.md), [pprof.md](./references/pprof.md)
"Memory growing over time"
→ pprof heap profiling
→ See [performance-debug.md](./references/performance-debug.md), [concurrency-debug.md](./references/concurrency-debug.md)
"Slow / high latency / p99 spikes"
→ CPU + mutex + block profiles
→ See [performance-debug.md](./references/performance-debug.md), [diagnostic-tools.md](./references/diagnostic-tools.md)
"Simple bug, easy to reproduce"
→ Write a test, add fmt.Println / log.Debug
→ See [testing-debug.md](./references/testing-debug.md)
Remember: Read the Error → Reproduce → Measure One Thing → Fix → Verify
Most Go bugs are: missing error checks, nil pointers, forgotten context cancel, unclosed resources, race conditions, or silent error swallowing.
The Golden Rules
1. Read the Error Message First
Go error messages are precise. Read them fully before doing anything else:
- File and line number → go directly there
- Type mismatch → check function signatures, interface satisfaction
- "undefined" → check imports, exported names, build tags
- "cannot use X as Y" → check concrete types vs interfaces
2. Reproduce Before You Fix
NEVER debug by guessing — reproduce first. Always:
- Write a failing test that captures the bug
- Make it deterministic
- Isolate the minimal failing example
- Use
git bisectto find the breaking commit
3. If You Don't Measure It, You're Guessing
Never rely on intuition for performance or concurrency bugs:
- pprof over intuition
- race detector over reasoning
- benchmarks over assumptions
4. One Hypothesis at a Time
Change one thing, measure, confirm. If you change three things at once, you learn nothing.
5. Find the Root Cause — No Workarounds
A band-aid fix that masks the symptom IS NOT ACCEPTABLE. You MUST understand why the bug happens before writing a fix.
When you don't understand the issue:
- Trace the data flow backwards from the symptom to its origin.
- Question your assumptions. The code you trust might be wrong.
- Ask "why" five times. Keep going until you reach the actual root cause.
- Perform more troubleshooting checks. More fmt.Println, more output inspection...
6. Research the Codebase, Not Just the Diff
Before flagging a bug or proposing a fix, trace the data flow and check for upstream handling. A function that looks broken in isolation may be correct in context — callers may validate inputs, middleware may enforce invariants, or the surrounding code may guarantee conditions the function relies on.
- Trace callers — who calls this function and with what values? Use Grep/Agent to find all call sites.
- Check upstream validation — input parsing, type conversions, or guard clauses earlier in the chain may make the "bug" unreachable.
- Read the surrounding code — middleware, interceptors, or init functions may set up state the function depends on.
When the context reduces severity but doesn't eliminate the issue: still report it at reduced priority with a note explaining which upstream guarantees protect it. Add a brief inline comment (e.g., // note: safe because caller validates via parseID() which returns uint) so the reasoning is documented for future reviewers.
7. Start Simple
Sometimes fmt.Println IS the right tool for local debugging. Escalate tools only when simpler approaches fail. NEVER use fmt.Println for production debugging — use slog.
Red Flags: You're Debugging Wrong
If any of these are happening, stop and return to Step 1:
- "Quick fix for now, investigate later" — There is no "later". Find the root cause.
- Multiple simultaneous changes — One hypothesis at a time.
- Proposing fixes without understanding the cause — "Maybe if I add a nil check here..." is guessing, not debugging.
- Each fix reveals a new problem — You're treating symptoms. The real bug is elsewhere.
- 3+ fix attempts on the same issue — You have the wrong mental model. Re-read the code, trace the data flow from scratch.
- "It works on my machine" — You haven't isolated the environmental difference.
- Blaming the framework/stdlib/compiler — It's almost never a Go bug. Verify your code first.
Reference Files
-
General Debugging Methodology — The systematic 10-step process: define symptoms, isolate reproduction, form one hypothesis, test it, verify the root cause, and defend against regressions. Escalation guide: when to escalate from
fmt.Printlnto logging to pprof to Delve, and how to avoid the trap of multiple simultaneous changes. -
Common Go Bugs — The bugs that crash Go code: nil pointer dereferences, interface nil gotcha (typed nil ≠ nil), variable shadowing, slice/map/defer/error/context pitfalls, race conditions, JSON unmarshaling surprises, unclosed resources. Each with reproduction patterns and fixes.
-
Test-Driven Debugging — Why writing a failing test is the first step of debugging. Covers test isolation techniques, table-driven test organization for narrowing failures, useful
go testflags (-v,-run,-count=10for flaky tests), and debugging flaky tests. -
Concurrency Debugging — Race conditions, deadlocks, goroutine leaks. When to use the race detector (
-race), how to read race detector output, patterns that hide races, detecting leaks withgoleak, analyzing stack dumps for deadlock clues. -
Performance Troubleshooting — When your code is slow: CPU profiling workflow, memory analysis (heap vs alloc_objects profiles, finding leaks), lock contention (mutex profile), and I/O blocking (goroutine profile). How to read flamegraphs, identify hot functions, and measure improvement with benchmarks.
-
pprof Reference — Complete pprof manual. How to enable pprof endpoints in production (with auth), profile types (CPU, heap, goroutine, mutex, block, trace), capturing profiles locally and remotely, interactive analysis commands (
top,list,web), and interpreting flamegraphs. -
Diagnostic Tools — Auxiliary tools for specific symptoms. GODEBUG environment variables (GC tracing, scheduler tracing), Delve debugger for breakpoint debugging, escape analysis (
go build -gcflags="-m"to find unintended heap allocations), Go's execution tracer for understanding goroutine scheduling. -
Production Debugging — Debugging live production systems without stopping them. Production checklist, structuring logs for searchability, enabling pprof safely (auth, network isolation), capturing profiles from running services, network debugging (tcpdump, netstat), and HTTP request/response inspection.
-
Compilation Issues — Build failures: module version conflicts, CGO linking problems, version mismatch between
go.modand installed Go version, platform-specific build tags preventing cross-compilation. -
Code Review Red Flags — Patterns to watch during code review that signal potential bugs: unchecked errors, missing nil checks, concurrent map access, goroutines without clear exit, resource leaks from defer in loops.
Cross-References
- → See
samber/cc-skills-golang@golang-performanceskill for optimization patterns after identifying bottlenecks - → See
samber/cc-skills-golang@golang-observabilityskill for metrics, alerting, and Grafana dashboards for Go runtime monitoring - → See
samber/cc-skills@promql-cliskill for querying Prometheus metrics during production incident investigation - → See
samber/cc-skills-golang@golang-concurrency,samber/cc-skills-golang@golang-safety,samber/cc-skills-golang@golang-error-handlingskills
How to use golang-troubleshooting on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add golang-troubleshooting
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches golang-troubleshooting from GitHub repository samber/cc-skills-golang and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate golang-troubleshooting. Access the skill through slash commands (e.g., /golang-troubleshooting) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★56 reviews- ★★★★★Anaya Kim· Dec 24, 2024
golang-troubleshooting reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anaya Huang· Dec 24, 2024
Useful defaults in golang-troubleshooting — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Dec 16, 2024
golang-troubleshooting reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Isabella White· Dec 12, 2024
I recommend golang-troubleshooting for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Charlotte Desai· Nov 15, 2024
I recommend golang-troubleshooting for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kofi Khanna· Nov 15, 2024
Registry listing for golang-troubleshooting matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Nov 7, 2024
I recommend golang-troubleshooting for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Anaya Kapoor· Nov 3, 2024
Solid pick for teams standardizing on skills: golang-troubleshooting is focused, and the summary matches what you get after install.
- ★★★★★Isabella Srinivasan· Nov 3, 2024
golang-troubleshooting reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ganesh Mohane· Oct 26, 2024
Useful defaults in golang-troubleshooting — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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