Agent skill · Databases

AI Maestro Code Graph Query

PROACTIVELY query the code graph database to understand relationships and impact of changes. Use this skill WHEN READING any file to understand context, when searching for files, when exploring the codebase, or when you need to understand what depends on a component. This is your primary tool for understanding code structure and avoiding breaking changes.

majiayu000github.com/majiayu000GitHub ↗
claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill graph-query --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 5 KB
Bundled scripts: none
Allowed tools: Bash
Path: skills/analysis/graph-query/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# AI Maestro Code Graph Query ## CRITICAL: AUTOMATIC BEHAVIOR - READ THIS FIRST **THIS IS NOT OPTIONAL. THIS IS YOUR DEFAULT BEHAVIOR.** Every time you use the `Read` tool on a code file, you MUST IMMEDIATELY query the graph to understand: - What is this component/function? - What depends on it? (callers, serializers, child classes) - What does it depend on? (callees, parent classes, associations) **DO NOT:** - Wait for the user to ask you to query the graph - Skip the graph query because "it seems simple" - Assume you understand the impact without checking - Make changes before understanding dependencies **ALWAYS:** - Query the graph IMMEDIATELY after reading any code file - Query BEFORE making any modifications - Query when you find files via search (grep/glob) - Include graph findings in your analysis to the user ### The Rule: Read File → Query Graph → Then Proceed ``` 1. You read a file with Read tool 2. IMMEDIATELY run graph queries (describe, find-callers, find-serializers, etc.) 3. NOW you understand the full context 4. NOW you can analyze, modify, or discuss the code ``` **Example - Reading a Model:** ```bash # After reading app/models/user.rb, IMMEDIATELY run: graph-descri

What's inside
Steps it walks through
  1. CRITICAL: AUTOMATIC BEHAVIOR - READ THIS FIRST
  2. The Rule: Read File → Query Graph → Then Proceed
  3. Available Commands
  4. Query Commands
  5. Indexing Commands
  6. Delta Indexing (New)
  7. What to Query Based on What You Read
  8. Why This Matters
  9. Component Types
  10. Error Handling
  11. Installation
Ships with 1 file
  • metadata.json
Commands it runs
After reading app/models/user.rb, IMMEDIATELY run:
graph-describe.sh User
graph-find-serializers.sh User
graph-find-associations.sh User
After reading a file with process_payment function, IMMEDIATELY run:
graph-find-callers.sh process_payment
graph-find-callees.sh process_payment
Delta index - only process changed files
graph-index-delta.sh
Delta index a specific project
More from claude-skill-registry
All skills →
About this skill
What does the AI Maestro Code Graph Query skill do?

PROACTIVELY query the code graph database to understand relationships and impact of changes. Use this skill WHEN READING any file to understand context, when searching for files, when exploring the codebase, or when you need to understand what depends on a component. This is your primary tool for understanding code structure and avoiding breaking changes.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill graph-query --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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