external/cc-skills-golang/golang-error-handling/SKILL.md
Idiomatic Golang error handling — creation, wrapping with %w, errors.Is/As, errors.Join, custom error types, sentinel errors, panic/recover, the single handling rule, structured logging with slog, HTTP request logging middleware, and samber/oops for production errors. Built to make logs usable at scale with log aggregation 3rd-party tools. Apply when creating, wrapping, inspecting, or logging errors in Go code. For samber/oops specifics → See `samber/cc-skills-golang@golang-samber-oops` skill; for slog handler ecosystem → See `samber/cc-skills-golang@golang-samber-slog` skill.
npx skillsauth add seikaikyo/dash-skills golang-error-handlingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Persona: You are a Go reliability engineer. You treat every error as an event that must either be handled or propagated with context — silent failures and duplicate logs are equally unacceptable.
Orchestration mode: Use ultracode for auditing error handling across a large codebase — orchestrate the five category sub-agents described in the "Parallelizing Error Handling Audits" section (creation, wrapping, single-handling rule, panic/recover, structured logging) and consolidate their findings.
Modes:
Community default. A company skill that explicitly supersedes
samber/cc-skills-golang@golang-error-handlingskill takes precedence.
This skill guides the creation of robust, idiomatic error handling in Go applications. Follow these principles to write maintainable, debuggable, and production-ready error code.
_fmt.Errorf("{context}: %w", err)%w internally, %v at system boundaries to control error chain exposureerrors.Is for sentinel matching and errors.As/errors.AsType for typed chain inspection instead of direct comparison or bare type assertions. For Go 1.26+, prefer errors.AsType[T](err) when T implements error; use errors.As(err, &target) for Go <1.26 or for non-error interface targets.errors.Join (Go 1.20+) to combine independent errorspanic for expected error conditions — reserve for truly unrecoverable statesslog (Go 1.21+) for structured error logging — not fmt.Println or log.Printfsamber/oops for production errors needing stack traces, user/tenant context, or structured attributesError Creation — How to create errors that tell the story: error messages should be lowercase, no punctuation, and describe what happened without prescribing action. Covers sentinel errors (one-time preallocation for performance), custom error types (for carrying rich context), and the decision table for which to use when.
Error Wrapping and Inspection — Why fmt.Errorf("{context}: %w", err) beats fmt.Errorf("{context}: %v", err) (chains vs concatenation). How to inspect chains with errors.Is, errors.As, and Go 1.26+ errors.AsType for type-safe error handling, and errors.Join for combining independent errors.
Error Handling Patterns and Logging — The single handling rule: errors are either logged OR returned, NEVER both (prevents duplicate logs cluttering aggregators). Panic/recover design, samber/oops for production errors, and slog structured logging integration for APM tools.
When auditing error handling across a large codebase, use up to 5 parallel sub-agents (via the Agent tool) — each targets an independent error category:
errors.New/fmt.Errorf usage, low-cardinality messages, custom types%w vs %v, verify errors.Is/errors.As patterns_)panic usage, verify recovery at goroutine boundariesslog usage at error sites, check for PII in error messagessamber/cc-skills-golang@golang-samber-oops for full samber/oops API, builder patterns, and logger integrationsamber/cc-skills-golang@golang-observability for structured logging setup, log levels, and request logging middlewaresamber/cc-skills-golang@golang-safety for nil interface trap and nil error comparison pitfallssamber/cc-skills-golang@golang-naming for error naming conventions (ErrNotFound, PathError)samber/cc-skills-golang@golang-continuous-integration skill for automated AI-driven code review in CI using these guidelinestools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.