Configurable Source Context Packager: Pre-Agentic Code Ingestion

[!NOTE]

Executive Overview

Attribute	Details
Status	🛠️ Foundation Tooling (Pre-Agentic LLM Era)
What It Is	Configurable CLI utility that serializes complete multi-file repositories into token-optimized, structured single documents for large-context LLMs.
Core Problem & Solution	Early 1M/2M context models (Claude 3, Gemini 1.5) lacked native multi-file editing harnesses. This packager uses AST awareness, .packignore rules, and token budgeting to feed coherent codebases to models without clutter.
Tech Stack	Node.js, Bash, AST Tree Parsers, Tokenizer Estimators
Key Highlights	Prioritizes type declarations & schemas over large implementation bodies, automated secret scrubbing, attention-optimized formatting.

Before official agentic harnesses (such as the Model Context Protocol, IDE agent sidecars, or CLI tool loops) were released by research labs, Andrea engineered custom source code context packagers to unlock deep, repository-wide reasoning from large-context LLMs.

1. The Token Budgeting & Priority Engine

Dumping entire repositories verbatim into an LLM degrades reasoning quality and exhausts token limits. The packager introduced an intelligent priority matrix:

[ Source Code Repository ]
           │
           ▼
[ .packignore & Security Scrub ] ──► ( Strips secrets, .env, build output, binary blobs )
           │
           ▼
[ Tier 1: Directory Tree & Types ] ──► ( Always Included: Folder structure, index.d.ts, schemas )
           │
           ▼
[ Tier 2: Public Interfaces & APIs ] ──► ( Always Included: Exported signatures, routers, routes )
           │
           ▼
[ Tier 3: Core Business Logic ] ────► ( Budget Permitting: Algorithm files, state stores )
           │
           ▼
[ Tier 4: Implementation Details ] ──► ( Stripped/Stubbed if Token Limit Exceeded )

• AST-Aware Signature Trimming: When token budgets were constrained, the tool stripped internal function bodies, preserving type signatures and JSDoc comments so the model retained structural understanding of the entire codebase.
• Hierarchical Tree Generation: Prefixed the bundle with a visual ASCII directory tree annotated with file sizes and purposes.

2. Secret Redaction & Leak Prevention

• Automated Credential Scrubbing: Applied strict regex gates to strip API keys, OAuth tokens, private IP addresses, and private machine hostnames before emitting the bundle.
• Binary Exclusion: Automatically blocked images, compiled bytecode, database files, and minified vendor bundles.

3. Attention-Optimized Formatting

• Fenced Code Blocks with Relative Paths: Structured file blocks with exact relative file paths and line number markers, allowing models to cite exact file locations in their generated patches.
• Standardized Prompt Envelopes: Framed the codebase with clear instruction preambles that instructed models on expected diff formats and coding standards.