Filesystem Context

Filesystem Context is an AI skill for building context-aware tools that understand and leverage file system structure, file relationships, and project conventions. It covers directory traversal, file type detection, project structure inference, dependency mapping, and context extraction that enable AI agents to navigate and understand codebases effectively.

What Is This?

Overview

Filesystem Context provides structured approaches to extracting meaningful context from file system layouts. It handles traversing directory trees while respecting ignore patterns from gitignore and similar files, detecting file types and programming languages through extensions and content analysis, inferring project structure conventions such as src, test, and config directories, mapping dependencies between files through import and require statement parsing, extracting summary context from file headers, docstrings, and exported symbols, and building navigable project maps that orient agents within unfamiliar codebases.

Who Should Use This

This skill serves AI tool developers building code-aware assistants, IDE plugin engineers creating project navigation features, DevOps teams building automated codebase analysis tools, and platform teams constructing development environment intelligence.

Why Use It?

Problems It Solves

AI agents without file system awareness cannot determine which files are relevant to a task. Flat file listings do not convey the architectural significance of directory organization. Without dependency mapping, agents miss related files that need coordinated changes. Ignoring gitignore patterns wastes processing on generated, vendored, or binary files.

Core Highlights

Smart traversal respects ignore patterns to focus on source files that matter. Structure inference identifies project conventions without explicit configuration. Dependency mapping reveals file relationships through import analysis. Context extraction summarizes file purpose from headers and exports.

How to Use It?

Basic Usage

from pathlib import Path
from dataclasses import dataclass, field

@dataclass
class FileInfo:
    path: str
    language: str = ""
    size: int = 0
    imports: list = field(default_factory=list)

LANG_MAP = {
    ".py": "python", ".js": "javascript",
    ".ts": "typescript", ".go": "go",
    ".java": "java", ".rs": "rust",
    ".rb": "ruby", ".cpp": "cpp"
}

class FilesystemScanner:
    def __init__(self, root, ignore=None):
        self.root = Path(root)
        self.ignore = ignore or {
            "node_modules", ".git", "__pycache__",
            "vendor", "dist", "build"
        }

    def scan(self):
        files = []
        for path in self.root.rglob("*"):
            if any(p in path.parts for p in self.ignore):
                continue
            if path.is_file():
                lang = LANG_MAP.get(path.suffix, "")
                files.append(FileInfo(
                    path=str(path.relative_to(self.root)),
                    language=lang,
                    size=path.stat().st_size
                ))
        return files

    def structure_summary(self, files):
        dirs = set()
        for f in files:
            parts = Path(f.path).parts
            if len(parts) > 1:
                dirs.add(parts[0])
        return sorted(dirs)

Real-World Examples

import re

class ProjectAnalyzer:
    def __init__(self, root):
        self.scanner = FilesystemScanner(root)
        self.files = self.scanner.scan()

    def detect_framework(self):
        filenames = {Path(f.path).name for f in self.files}
        if "package.json" in filenames:
            if "next.config.js" in filenames:
                return "Next.js"
            return "Node.js"
        if "pyproject.toml" in filenames:
            return "Python"
        if "go.mod" in filenames:
            return "Go"
        return "unknown"

    def extract_imports(self, file_info):
        path = Path(self.scanner.root) / file_info.path
        if not path.exists():
            return []
        content = path.read_text(errors="ignore")
        imports = []
        if file_info.language == "python":
            for m in re.finditer(
                r'^(?:from|import)\s+([\w.]+)', content, re.M
            ):
                imports.append(m.group(1))
        elif file_info.language in ("javascript", "typescript"):
            for m in re.finditer(
                r'(?:import|require)\(?.+["\']([\w./]+)', content
            ):
                imports.append(m.group(1))
        file_info.imports = imports
        return imports

    def dependency_map(self):
        deps = {}
        for f in self.files:
            if f.language:
                self.extract_imports(f)
                deps[f.path] = f.imports
        return deps

    def context_summary(self):
        framework = self.detect_framework()
        langs = {}
        for f in self.files:
            if f.language:
                langs[f.language] = langs.get(f.language, 0) + 1
        return {
            "framework": framework,
            "file_count": len(self.files),
            "languages": langs,
            "top_dirs": self.scanner.structure_summary(self.files)
        }

Advanced Tips

Parse gitignore files to dynamically build ignore patterns rather than hardcoding directory names. Cache scan results and invalidate based on file modification timestamps for repeated analysis. Use import dependency graphs to identify which files need review when a specific module changes.

When to Use It?

Use Cases

Use Filesystem Context when building AI coding assistants that need project awareness, when creating tools that analyze codebase structure for documentation, when mapping file dependencies for impact analysis during refactoring, or when orienting agents in unfamiliar repositories.

Related Topics

Abstract syntax tree parsing, gitignore pattern matching, project convention detection, code navigation tooling, and dependency graph visualization complement filesystem context.

Important Notes

Requirements

File system read access to the target project directory. Language-specific import parsing rules for dependency extraction. Ignore pattern configuration for filtering non-source files.

Usage Recommendations

Do: respect gitignore patterns to avoid processing generated and vendored files. Cache scan results for projects that are analyzed repeatedly. Use dependency maps to identify related files when making changes to a specific module.

Don't: traverse into node_modules, vendor, or other dependency directories that inflate scan results. Parse binary files for import statements, which produces garbage output. Assume directory naming conventions without verifying against actual project structure.

Limitations

Import parsing with regex misses dynamic imports and computed require paths. Project structure inference relies on common conventions that may not match all codebases. Large monorepos with thousands of files may require incremental scanning for performance.