Context Builder: The Ultimate Tool for Generating LLM-Optimized Codebase Context

What is Context Builder?

Context Builder is an agentic skill designed to generate a single, structured
markdown file from any codebase directory. The output is meticulously
optimized for Large Language Model (LLM) consumption, featuring relevance-
based file ordering, AST-aware code signatures, automatic token budgeting, and
smart defaults that make it incredibly powerful for developers, researchers,
and AI agents.

Core Features and Capabilities

LLM-Optimized Output

The tool creates markdown files specifically structured for LLM consumption.
Files are sorted by relevance (configuration and documentation first, then
source code, tests, and build files), making it easy for AI models to
understand the codebase structure and priorities.

AST-Aware Code Signatures

One of the most powerful features is the ability to extract Abstract Syntax
Tree (AST) signatures from code. Instead of including entire source files,
Context Builder can extract function signatures, class definitions, and method
declarations, dramatically reducing token usage while preserving structural
understanding. This is particularly useful when working with token-limited
models.

Smart Token Budgeting

The tool includes intelligent token counting and budgeting features. You can
set maximum token limits (e.g., 100,000 tokens for most models or 200,000 for
larger models like Gemini), and Context Builder will automatically include
files in relevance order until the budget is exhausted. It also provides token
count previews before generation, helping you make informed decisions about
filtering and optimization strategies.

Security and Path Scoping

Security is a top priority with Context Builder. The tool implements strict
path scoping rules to prevent accidental exposure of sensitive information.
Agents must always use absolute paths and target explicit project directories
rather than home directories, system paths, or credential stores. The tool
automatically excludes sensitive directories like .git/, node_modules/, and
various cache directories, respects .gitignore rules when present, and detects
binary files to skip them.

Best Practices for Secure Usage

Always write output to project-local paths like docs/ or /tmp/, never to
shared or public locations. Review the output before sharing, as it may
contain embedded API keys, secrets, or credentials from source files. Use
.gitignore patterns to exclude sensitive files when possible.

Installation and Setup

Context Builder requires the Rust toolchain and builds from source with
cryptographic verification via crates.io. Install it with:

cargo install context-builder --features tree-sitter-all

Pre-built binaries with SHA256 checksums are also available from GitHub
Releases. Verify your installation with:

context-builder --version

Core Workflows

Quick Context Generation

For a complete project overview, use:

context-builder -d /path/to/project -y -o context.md

The -y flag skips confirmation prompts, which is recommended for agent
workflows when you have explicitly scoped the path.

Scoped Context with File Filtering

To focus on specific file types:

context-builder -d /path/to/project -f rs,toml -i docs,assets -y -o context.md

This includes only Rust and TOML files while excluding docs and assets
directories.

AST Signatures Mode

For minimal token usage:

context-builder -d /path/to/project --signatures -f rs,ts,py -y -o signatures.md

This replaces full file content with extracted signatures, typically reducing
tokens by 80-90%.

Budget-Constrained Context

To work within token limits:

context-builder -d /path/to/project --max-tokens 100000 -y -o context.md

Files are included in relevance order until the budget is exhausted, with
automatic warnings if output exceeds 128K tokens.

Advanced Features

Incremental Diffs

Context Builder supports incremental updates through diff caching. First,
ensure context-builder.toml exists with timestamped_output = true and
auto_diff = true. Then run the tool twice: first for a baseline snapshot, then
after code changes for diff annotations. Use --diff-only for minimal output
containing only changes.

Structural Summary

Combine signatures with structural summaries using --structure to append
counts like "6 functions, 2 structs, 1 impl block." Pair with --visibility
public to show only public API surface.

Smart Defaults

Context Builder includes intelligent defaults that require no configuration:
automatic exclusion of heavy directories like node_modules, dist, build,
pycache, .venv, vendor, and 12 more; self-exclusion of output files and
cache directories; automatic .gitignore respect when .git directories exist;
binary file detection via UTF-8 sniffing; and intelligent file ordering.

CLI Reference

Essential Flags

-d : Input directory (always use absolute paths for reliability) -o : Output path (write to project/docs/ or /tmp/) -f : Filter by extension (comma-separated: -f rs,toml,md) -i : Ignore directories/files (comma-separated: -i tests,docs,assets) --max-tokens : Token budget cap (100000 for most models, 200000 for Gemini) --token-count: Dry-run token estimate -y: Skip all prompts (use only with explicit, scoped project paths) --preview: Show file tree only --diff-only: Output only diffs --signatures: AST signature extraction (requires tree-sitter-all feature) --structure: Structural summary --visibility : Filter by visibility (all/default or public) --truncate : Truncation strategy (smart/AST-aware or simple) --init: Create config file --clear-cache: Reset diff cache

Practical Recipes

Deep Code Review

Generate focused context and feed it to an LLM for architecture review, bug
hunting, and performance analysis:

context-builder -d /path/to/project -f rs,toml --max-tokens 120000 -y -o docs/deep_think_context.md

Then attach docs/deep_think_context.md and ask for comprehensive analysis.

API Surface Review

Extract only public signatures for API documentation:

context-builder -d /path/to/project --signatures --visibility public -f rs -y -o docs/api_surface.md

This typically provides 80-90% token reduction while preserving public API
understanding.

Compare Two Versions

Generate context for both versions and feed them to an LLM for comparative
analysis:

context-builder -d ./v1 -f py -y -o /tmp/v1_context.md

context-builder -d ./v2 -f py -y -o /tmp/v2_context.md

Monorepo Slice

Focus on a specific package within a monorepo:

context-builder -d /path/to/monorepo/packages/core -f ts,tsx -i tests,mocks -y -o core_context.md

Supported Languages and Extensions

Context Builder supports 8 languages for AST signature extraction: Rust (.rs),
JavaScript (.js/.jsx), TypeScript (.ts/.tsx), Python (.py), Go (.go), Java
(.java), C (.c), and C++ (.cpp). The tool automatically detects and processes
these file types based on their extensions.

Token Efficiency and Optimization

The tool is designed for maximum token efficiency. Full source files typically
consume 15K+ tokens each, while AST signatures reduce this to around 4K tokens
per file. The relevance-based ordering ensures that the most important files
(configuration, documentation, entry points) are included first, maximizing
the value of limited token budgets.

Cross-Project Research

Context Builder excels at cross-project research by allowing quick packaging
of dependency source for analysis. This is invaluable for understanding third-
party libraries, comparing implementations across projects, or conducting
comprehensive codebase studies.

Conclusion

Context Builder represents a significant advancement in how we interact with
codebases through AI and LLM systems. By providing structured, optimized, and
secure context generation, it enables deeper code analysis, faster onboarding,
and more effective AI-assisted development workflows. Whether you're
conducting deep code reviews, generating API documentation, or performing
cross-project research, Context Builder provides the tools and features needed
to work efficiently with large codebases in the age of AI.

Skill can be found at:
builder/SKILL.md>