# Debug Issue

> Systematically debug issues using graph-powered code navigation

- Skill: `tirth8205/debug-issue` (Agent Skill)
- Install (CLI): `npx skillmds@latest add tirth8205/debug-issue`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tirth8205/debug-issue/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tirth8205 (https://skillmd.com/u/tirth8205)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/tirth8205/debug-issue

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## Debug Issue

Use the knowledge graph to systematically trace and debug issues.

### Steps

1. Use `semantic_search_nodes_tool` to find code related to the issue.
2. Use `query_graph_tool` with `callers_of` and `callees_of` to trace call chains.
3. Use `get_flow_tool` to see full execution paths through suspected areas.
4. Run `detect_changes_tool` to check if recent changes caused the issue.
5. Use `get_impact_radius_tool` on suspected files to see what else is affected.

### Tips

- Check both callers and callees to understand the full context.
- Look at affected flows to find the entry point that triggers the bug.
- Recent changes are the most common source of new issues.

## Token Efficiency Rules
- Start with `get_minimal_context_tool(task="<your task>")` before other graph tools.
- Use `detail_level="minimal"` on all calls. Only escalate to "standard" when minimal is insufficient.
- Target: complete any review/debug/refactor task in ≤5 tool calls and ≤800 total output tokens.
- Read the implementation and its tests before changing code. The graph narrows scope; it does not replace the source.