SiftrCode V2
SiftrCode V2 • TypeSafe SystemOne Live Pilot Certified

Context optimization, outcome-aware

SiftrCode discovers repository context, protects edit targets, and synthesizes interface skeletons — eliminating 90% of token bloat while boosting coding agent accuracy.

$ npx siftrcode context "task prompt"
See Interactive Comparison
−90%
Token Reduction
AST skeletons eliminate internal function bodies
100%
Plan Invariance
Bit-for-bit identical baseline vs shadow decision plans
175ms
Median Latency
Verified on 25 real tasks via TypeSafe SystemOne
0 Egress
Local & Secure
Zero raw source or secrets leave your machine

See the Difference: Raw Dump vs SiftrCode

Select a task to compare how an AI agent experiences a raw repository dump versus a SiftrCode outcome-aware bundle.

Without SiftrCode
94,200 Tokens
Context Bloat & Agent Confusion

Agent is handed entire repositories or full uncompressed files. Critical edit target is buried inside 18 files of irrelevant boilerplate.

Injected Files:
Cost per turn: $0.283 100% Uncompressed
OPTIMIZED
SiftrCode V2 Bundle
3,030 Tokens −96.8%
EDIT TARGET (FULL BODY PROTECTED) 100% Fidelity
src/webhooks/signature_verifier.ts
Preserved with complete implementation so agent edits succeed without hallucinations.
DEPENDENCIES (AST SKELETON SYNTHESIS) Excised Bodies
14 Unrelated Files Suppressed Zero context pollution
Cost per turn: $0.009 Save $0.274 / turn

AST Interface Skeletonizer Demo

See how function bodies are excised across 4 languages while preserving type interfaces.

RAW IMPLEMENTATION 240 tokens
...
SIFTRCODE AST SKELETON 38 tokens (−84%)
...

How SiftrCode Optimizes Agent Context

From full repository scan to compressed context plan in under 100 milliseconds.

01

Multi-Channel Recall

Extracts AST symbols, traverses the repository call graph, and analyzes git co-change history to find candidate files related to the task prompt or stack trace.

Exact + Lexical + Graph + Git
02

Outcome-Aware Scoring

Evaluates candidates against point-in-time features to distinguish exact edit targets from supporting dependencies. Verified against production TypeSafe SystemOne JEV signals.

Edit Target vs Dependency
03

Submodular Budgeting

Linear programming solver allocates each file into its optimal resolution (FULL, BODY, SKELETON, SIGNATURE, OMIT) under strict token caps with zero hallucination.

Linear Optimization Solver
Empirical Validation

Live TypeSafe Pilot Results

Target Repositories: Express • FastAPI • SiftrCode
Tasks Evaluated
25 / 25
100% Success Rate (0 Failures)
Decision Plan Invariance
100.0%
Normalized cryptographic equality
Remote Latency (P50)
175ms
P95: 294ms • 0 retries required
Ground-Truth Correlation
r = 0.513
Strong root cause identification
Lineage Coverage: 9,018 / 9,018 decision records referentially joined with 0 orphans.
Read Full Architecture →

Get Started in 30 Seconds

Works seamlessly with Claude Code CLI, Cursor, Windsurf, or as a standalone CLI tool.

Register SiftrCode as a native Model Context Protocol (MCP) server for Claude Code CLI:

claude mcp add siftrcode -- npx siftrcode mcp

Claude Code will automatically invoke siftr_context before executing edits.

Stop Wasting 90% of Your Agent Tokens

Drop SiftrCode into your agent loop today. Compatible with Claude Code, Cursor, Windsurf, and custom autonomous agents.