Persona: You are a Go engineer who treats errors as structured data. Every error carries enough context — domain, attributes, trace — for an on-call engineer to diagnose the problem without asking the developer.
Fetches golang-samber-oops from samber/cc-skills-golang and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/golang-samber-oops
Restart Cursor to activate golang-samber-oops. Access via /golang-samber-oops in your agent's command palette.
⚠
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Persona: You are a Go engineer who treats errors as structured data. Every error carries enough context — domain, attributes, trace — for an on-call engineer to diagnose the problem without asking the developer.
samber/oops Structured Error Handling
samber/oops is a drop-in replacement for Go's standard error handling that adds structured context, stack traces, error codes, public messages, and panic recovery. Variable data goes in .With() attributes (not the message string), so APM tools (Datadog, Loki, Sentry) can group errors properly. Unlike the stdlib approach (adding slog attributes at the log site), oops attributes travel with the error through the call stack.
Why use samber/oops
Standard Go errors lack context — you see connection failed but not which user triggered it, what query was running, or the full call stack. samber/oops provides:
Structured context — key-value attributes on any error
Stack traces — automatic call stack capture
Error codes — machine-readable identifiers
Public messages — user-safe messages separate from technical details
Low-cardinality messages — variable data in .With() attributes, not the message string, so APM tools group errors properly
This skill is not exhaustive. Please refer to library documentation and code examples for more information. Context7 can help as a discoverability platform.
Core pattern: Error builder chain
All oops errors use a fluent builder pattern:
err := oops.In("user-service").// domain/featureTags("database","postgres").// categorizationCode("network_failure").// machine-readable identifierUser("user-123","email","[email protected]").// user contextWith("query", query).// custom attributesErrorf("failed to fetch user: %s","timeout")
Terminal methods:
.Errorf(format, args...) — create a new error
.Wrap(err) — wrap an existing error
.Wrapf(err, format, args...) — wrap with a message
.Join(err1, err2, ...) — combine multiple errors
.Recover(fn) / .Recoverf(fn, format, args...) — convert panic to error
Error builder methods
Methods
Use case
.With("key", value)
Add custom key-value attribute (lazy func() any values supported)
.WithContext(ctx, "key1", "key2")
Extract values from Go context into attributes (lazy values supported)
.In("domain")
Set the feature/service/domain
.Tags("auth", "sql")
Add categorization tags (query with err.HasTag("tag"))
.Code("iam_authz_missing_permission")
Set machine-readable error identifier/slug
.Public("Could not fetch user.")
Set user-safe message (separate from technical details)
Add span ID representing a unit of work/operation (default: ULID)
.Time(t)
Override error timestamp (default: time.Now())
.Since(t)
Set duration based on time since t (exposed via err.Duration())
.Duration(d)
Set explicit error duration
.Request(req, includeBody)
Attach *http.Request (optionally including body)
.Response(res, includeBody)
Attach *http.Response (optionally including body)
oops.FromContext(ctx)
Start from an OopsErrorBuilder stored in a Go context
Common scenarios
Database/repository layer
func(r *UserRepository)FetchUser(id string)(*User,error){ query :="SELECT * FROM users WHERE id = $1" row, err := r.db.Query(query, id)if err !=nil{returnnil, oops.In("user-repository").Tags("database","postgres").With("query", query).With("user_id", id).Wrapf(err,"failed to fetch user from database")}// ...}
HTTP handler layer
func(h *Handler)CreateUser(w http.ResponseWriter, r *http.Request){ userID :=getUserID(r) err := h.service.CreateUser(r.Context(), userID)if err !=nil{return oops.In("http-handler").Tags("endpoint","/users").Request(r,false).User(userID).Wrapf(err,"create user failed")} w.WriteHeader(http.StatusCreated)}
// ✓ Good — Wrap returns nil if err is nilreturn oops.Wrapf(err,"operation failed")// ✗ Bad — unnecessary nil checkif err !=nil{return oops.Wrapf(err,"operation failed")}returnnil
DO: Add context at each layer
Each architectural layer SHOULD add context via Wrap/Wrapf — at least once per package boundary (not necessarily at every function call).
// ✓ Good — each layer adds relevant contextfuncController()error{return oops.In("controller").Trace(traceID).Wrapf(Service(),"user request failed")}funcService()error{return oops.In("service").With("op","create_user").Wrapf(Repository(),"db operation failed")}funcRepository
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
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate 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
