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Systematic debugging methodologies for hard bugs. Covers rubber ducking, code tracing, hypothesis testing, logging strategies. Use when stuck on bugs that resist quick fixes.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Structure Claude prompts for clarity and better results using roles, explicit instructions, context, positive framing, and strategic organization. Use when crafting prompts for complex tasks, long documents, tool workflows, or code generation.
Pause agent execution to have the human validate decisions, choose between options, answer freetext, or comment on a document — via the `hl` CLI. Every interaction is a kickoff that returns a job handle immediately; collect the human's answer later with `hl job result`. Use for material design decisions, approval gates, picks between meaningful alternatives, and markdown doc review. Not for trivial yes/no confirmations the agent should decide itself.
Validate technical designs against requirements for coverage, feasibility, and architectural soundness. Use after creating a design.
Validate implementation plans against requirements and design. Use after creating a plan to ensure full coverage and no ambiguities.
Validate requirements documents for EARS compliance, completeness, and consistency. Use after creating requirements.
Design CLIs for humans and LLM agents — subcommand shape, output streams, exit codes, JSON modes, TTY-aware color, structured errors, mutation safety. Use when building or refactoring a CLI, adding machine-readable output, writing --help, deciding stdout vs stderr, or making a tool agent-friendly.
| name | debugging |
| description | Systematic debugging methodologies for hard bugs. Covers rubber ducking, code tracing, hypothesis testing, logging strategies. Use when stuck on bugs that resist quick fixes. |
| user-invocable | true |
Quote code snippets and explain what each chunk should do vs actually does. Don't assume—verbalize the logic. Discrepancies reveal bugs.
Trace data from entry point to failure. At each transformation: what goes in, what comes out? Mark where expectations diverge.
Failure-prone boundaries: async, serialization, type coercion, null propagation, state mutations.
Don't change code hoping it helps—that creates noise.
Log at decision points and async boundaries, not everywhere.
Workflow:
For complex/unfamiliar code sections acting as a blackbox:
When stuck: Spawn 2-3 senior-advisor agents in parallel with different perspectives (pragmatist, architect, skeptic).
Avoid biasing agents: Pass them relevant file paths and the observed behavior, but not your hypotheses or assumptions. Let them form independent conclusions.
If no, keep investigating.
