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Skill
debug
Original text · EnglishStructured debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.
Republished in full under its license, with attribution. The source is authoritative.
name: debug
description: Structured debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.
argument-hint: "<error message or problem description>"
/debug
> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
Run a structured debugging session to find and fix issues systematically.
Usage
/debug $ARGUMENTS
How It Works
┌─────────────────────────────────────────────────────────────────┐
│ DEBUG │
├─────────────────────────────────────────────────────────────────┤
│ Step 1: REPRODUCE │
│ ✓ Understand the expected vs. actual behavior │
│ ✓ Identify exact reproduction steps │
│ ✓ Determine scope (when did it start? who is affected?) │
│ │
│ Step 2: ISOLATE │
│ ✓ Narrow down the component, service, or code path │
│ ✓ Check recent changes (deploys, config changes, dependencies) │
│ ✓ Review logs and error messages │
│ │
│ Step 3: DIAGNOSE │
│ ✓ Form hypotheses and test them │
│ ✓ Trace the code path │
│ ✓ Identify root cause (not just symptoms) │
│ │
│ Step 4: FIX │
│ ✓ Propose a fix with explanation │
│ ✓ Consider side effects and edge cases │
│ ✓ Suggest tests to prevent regression │
└─────────────────────────────────────────────────────────────────┘
What I Need From You
Tell me about the problem. Any of these help:
Error message or stack trace
Steps to reproduce
What changed recently
Logs or screenshots
Expected vs. actual behavior
Output
## Debug Report: [Issue Summary]
### Reproduction
- **Expected**: [What should happen]
- **Actual**: [What happens instead]
- **Steps**: [How to reproduce]
### Root Cause
[Explanation of why the bug occurs]
### Fix
[Code changes or configuration fixes needed]
### Prevention
- [Test to add]
- [Guard to put in place]
If Connectors Available
If ~~monitoring is connected:
Pull logs, error rates, and metrics around the time of the issue
Show recent deploys and config changes that may correlate
If ~~source control is connected:
Identify recent commits and PRs that touched affected code paths
Check if the issue correlates with a specific change
If ~~project tracker is connected:
Search for related bug reports or known issues
Create a ticket for the fix once identified
Tips
Share error messages exactly — Don't paraphrase. The exact text matters.
Mention what changed — Recent deploys, dependency updates, and config changes are top suspects.
Include context — "This works in staging but not prod" or "Only affects large payloads" narrows things fast.
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`,…
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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.