Code Context Finder

Overview

Find and surface relevant context while coding by combining knowledge graph search with code relationship analysis. Uses smart detection to identify when additional context would be helpful, then retrieves:

• Knowledge graph entities: Prior decisions, project context, related concepts

• Code relationships: Dependencies, imports, function calls, class hierarchies

When to Use (Smart Detection)

This skill activates automatically when detecting:

Trigger What to Search

Opening unfamiliar file Knowledge graph for file/module context, code for imports/dependencies

Working on new feature Prior decisions, related concepts, similar implementations

Debugging errors Related issues, error patterns, affected components

Refactoring code Dependent files, callers/callees, test coverage

Making architectural decisions Past ADRs, related design docs, established patterns

Touching config/infra files Related deployments, environment notes, past issues

For detection triggers reference, load references/detection_triggers.md .

Core Workflow

1. Detect Context Need

Identify triggers that suggest context would help:

Signals to watch:

• New/unfamiliar file opened
• Error messages mentioning unknown components
• Questions about "why" or "how" something works
• Changes to shared/core modules
• Architectural or design discussions

2. Search Knowledge Graph

Use MCP memory tools to find relevant entities:

Search for related context

mcp__memory__search_nodes(query="<topic>")

Open specific entities if known

mcp__memory__open_nodes(names=["entity1", "entity2"])

View relationships

mcp__memory__read_graph()

Search strategies:

• Module/file names → project context

• Error types → past issues, solutions

• Feature names → prior decisions, rationale

• People names → ownership, expertise

3. Analyze Code Relationships

Find code-level context:

Find what imports this module

grep -r "from module import" --include=".py" grep -r "import module" --include=".py"

Find function callers

grep -r "function_name(" --include="*.py"

Find class usages

grep -r "ClassName" --include="*.py"

Find test coverage

find . -name "test.py" -exec grep -l "module_name" {} ;

For common search patterns, load references/search_patterns.md .

4. Synthesize Context

Present findings concisely:

Context Found

Knowledge Graph:

• [Entity]: Relevant observation
• [Decision]: Prior architectural choice

Code Relationships:

• Imported by: file1.py, file2.py
• Depends on: module_a, module_b
• Tests: test_module.py (5 tests)

Suggested Actions:

• Review [entity] before modifying
• Consider impact on [dependent files]

Quick Reference

Knowledge Graph Queries

Intent Query Pattern

Find project context search_nodes("project-name")

Find prior decisions search_nodes("decision") or search_nodes("<feature>")

Find related concepts search_nodes("<concept>")

Find people/owners search_nodes("<person-name>")

Browse all read_graph()

Code Relationship Queries

Intent Command

Find importers grep -r "from X import|import X"

Find callers grep -r "function("

Find implementations grep -r "def function|class Class"

Find tests find -name "test" -exec grep -l "X"

Find configs grep -r "X" *.json *.yaml *.toml

Integration with Coding Workflow

Before Making Changes

• Check knowledge graph for context on module/feature

• Find all files that import/depend on target

• Locate relevant tests

• Review prior decisions if architectural

After Making Changes

• Update knowledge graph if significant decision made

• Note new patterns or learnings

• Add observations to existing entities

When Debugging

• Search knowledge graph for similar errors

• Find all code paths to affected component

• Check for related issues/decisions

• Document solution if novel

Resources

references/

• detection_triggers.md

• Detailed trigger patterns for smart detection

• search_patterns.md

• Common search patterns for code relationships

scripts/

• find_code_relationships.py
• Analyze imports, dependencies, and call graphs