How do I install the Debug Issue skill?

Run npx skillmds@latest add krishnakanthb13/debug-issue in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Debug Issue safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Debug Issue?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Debug Issue free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Debug Issue?

krishnakanthb13 (@krishnakanthb13) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Debug Issue by krishnakanthb13 — Agent Skill

Name: Debug Issue
Author: krishnakanthb13

File contents

Systematic Debugging (The Iron Law)

Core Principle: NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST. Symptom patching is failure.

Inputs

Issue Description: What is broken?
Observed Behavior: What is happening instead?
Stack Trace/Logs: (Optional) Error output.

Tooling Strategy

Use grep_search to find error messages in code.
Use view_file to inspect logic.
Use run_command to run reproduction scripts.

Workflow

1. Isolation & Symptom Analysis

Deviation: Clearly define Expected vs Observed.
Error Messages: Read the ENTIRE stack trace. Do not skim.
Environment: Dev vs Prod? Recent changes?

2. Reproduction (The MRE)

Steps: List the exact steps to fail.
Determinism: Is it 100% consistent or flaky?
MRE: Create a Minimal Reproducible Example (strip away everything else).

3. Root Cause Investigation (The "Why")

Trace Data Flow: Follow the bad value backwards. Where did it originate?
Instrumentation: "Measure, Don't Guess." Add logs at boundaries.
Hypothesis: Formulate: "I think X fails because Y."

4. Implementation (The Fix)

The 3-Strike Rule: If you have tried 3 fixes and they failed, STOP.
- Observation: You likely have an architecture problem, not a bug.
- Action: Question the fundamental pattern.
Single Change: Apply ONE fix. Test it.

5. Verification & Regression

Verify: Does the MRE pass now?
Regression: Did anything else break? Run enhance_ui or standard tests.

Rationalization Busting

If you catch yourself saying these, STOP and return to Step 1:

"I'll just try this quickly..."
"It's probably X..."
"I don't know why this works but it does."

Advanced Debugging Strategies

Binary Search: If a long script or a large commit history fails, cut it in half. Test which half fails. Repeat.
Targeted Instrumentation: Don't just console.log("here"). Log state: console.log({ user: req.user, authHeader: req.headers.authorization }).
Form Hypotheses & Controlled Experiments: Before changing code, write down what you think is wrong and what single change will prove it.
Rubber Ducking / Write it Down: Explain the data flow step-by-step out loud (or in the prompt). The assumption gap often reveals itself.

---name: debug-issuedescription: A strict, scientific debugging protocol (The "Iron Law"). No fixes allowed without root cause investigation.---# Systematic Debugging (The Iron Law)**Core Principle**: NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST.Symptom patching is failure.## Inputs* **Issue Description**: What is broken?* **Observed Behavior**: What is happening instead?* **Stack Trace/Logs**: (Optional) Error output.## Tooling Strategy* Use `grep_search` to find error messages in code.* Use `view_file` to inspect logic.* Use `run_command` to run reproduction scripts.## Workflow### 1. Isolation & Symptom Analysis* **Deviation**: Clearly define `Expected` vs `Observed`.* **Error Messages**: Read the ENTIRE stack trace. Do not skim.* **Environment**: Dev vs Prod? Recent changes?### 2. Reproduction (The MRE)* **Steps**: List the exact steps to fail.* **Determinism**: Is it 100% consistent or flaky?* **MRE**: Create a Minimal Reproducible Example (strip away everything else).### 3. Root Cause Investigation (The "Why")* **Trace Data Flow**: Follow the bad value backwards. Where did it originate?* **Instrumentation**: "Measure, Don't Guess." Add logs at boundaries.* **Hypothesis**: Formulate: "I think X fails because Y."### 4. Implementation (The Fix)* **The 3-Strike Rule**: If you have tried 3 fixes and they failed, STOP. * *Observation*: You likely have an architecture problem, not a bug. * *Action*: Question the fundamental pattern.* **Single Change**: Apply ONE fix. Test it.### 5. Verification & Regression* **Verify**: Does the MRE pass now?* **Regression**: Did anything else break? Run `enhance_ui` or standard tests.## Rationalization BustingIf you catch yourself saying these, STOP and return to Step 1:* "I'll just try this quickly..."* "It's probably X..."* "I don't know why this works but it does."## Advanced Debugging Strategies- **Binary Search**: If a long script or a large commit history fails, cut it in half. Test which half fails. Repeat.- **Targeted Instrumentation**: Don't just `console.log("here")`. Log state: `console.log({ user: req.user, authHeader: req.headers.authorization })`.- **Form Hypotheses & Controlled Experiments**: Before changing code, write down what you think is wrong and what single change will prove it.- **Rubber Ducking / Write it Down**: Explain the data flow step-by-step out loud (or in the prompt). The assumption gap often reveals itself.

Frequently asked questions

Debug Issue