code-query

Query codebase structure through knowledge graphs before falling back to deterministic repo mapping.

3|Updated May 2, 2026
One-click install
npx skills add https://github.com/JuanTrujilloDev/agent-config-template --skill code-query-juantrujillodev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-query
Source: https://github.com/JuanTrujilloDev/agent-config-template/tree/main/plugin/skills/code-query
Command: npx skills add https://github.com/JuanTrujilloDev/agent-config-template --skill code-query-juantrujillodev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Answering structural questions about a codebase—what depends on X, how A connects to B, what a subsystem does—by grepping and reading raw files burns context and time. This Skill routes structural questions to a code knowledge graph first and reserves file reads for the files that actually matter. ## Core Features & Use Cases - Graph-first querying: Detects and queries a codebase knowledge graph (e.g. graphify via CLI or MCP tools like query_graph, shortest_path, get_node) for impact analysis, path queries, and concept explanation. - Deterministic fallback: Builds a cheap structural repo map from git ls-files, symbol listings, and import edges when no graph exists. - Workflow integration: Grounds /spec decomposition, /feature impact analysis, and /fix debugging in real dependency data while preserving Read Before You Write discipline. - Use Case: Before editing a shared module, ask the graph what depends on it, verify INFERRED edges by reading the flagged callers, then make the change with full impact awareness. ## Quick Start Ask the agent to use the code-query skill to find what depends on the module you are about to change before editing it.

Frequently Asked Questions about code-query

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find what depends on a function or module before changing it?▼

Query a code knowledge graph for impact analysis first, then read the files it flags. If no graph exists, build a quick map from import and require lines to see who uses the module.

When should I use a code knowledge graph instead of grep?▼

Use grep for known symbol or string lookups. Use the graph for structural questions like what depends on X, how two components connect, or what a subsystem does, where reading raw files would waste context.

How do I explore an unfamiliar codebase efficiently?▼

Start with graph communities or a deterministic repo map: group git ls-files by directory, list symbols with ctags or grep patterns, and trace import edges. Then read only the files the structure identifies as relevant.

Can I trust inferred edges in a code knowledge graph?▼

Treat INFERRED edges as leads, not facts. They come from reference resolution rather than explicit source, so verify each one by reading the implicated code before acting on it.

What if no knowledge graph exists for my repository?▼

Build one with graphify, or use the fallback: a skeleton from git ls-files, a symbol surface from ctags or grep, and import-line edges. This gives a lower-fidelity answer to which files matter at minimal cost.