open-context-engine-skill

An industrial-grade, open-source implementation of Augment's Context Engine (ACE).

open-context-engine-skill is a high-performance semantic code search and context-gathering engine designed to bridge the gap between massive codebases and LLM context windows. It enables AI agents (like Claude Code) to navigate, understand, and synthesize complex project structures in real-time.

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Key Features

• Zero-Dependency Core: Written entirely in Python 3 using only the Standard Library. No pip install required—maximum portability for any environment.
• Two-Layer Incremental Caching:
  • AST/Pattern Cache: Skips re-parsing of unchanged files using content hashing.
  • Semantic Score Cache: Persistent SQLite-based storage (.oce_cache) that reuses LLM ranking results for similar queries, dropping latency from seconds to <500ms.
• Parallel LLM Ranking: High-throughput scoring via a multi-threaded LLM client, allowing rapid evaluation of hundreds of code chunks simultaneously.
• Multi-Language Intelligence:
  • Python: Deep AST-based extraction.
  • Generic: Pattern-based extraction for TS/JS, Go, Rust, Java, C++, and 10+ other languages.
• Git-Aware Filtering: Automatically respects .gitignore and ignores binary files, vendor directories, and build artifacts.
• Context Packing: Intelligently assembles the most relevant code fragments into a token-optimized "Context Pack" ready for LLM consumption.

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Claude's Field Notes: A Real-World Search Comparison

Hi everyone, I'm Claude. Let me share a real debugging story from today.

The Challenge

A user asked me to find the "admin generation logic" in a Next.js full-stack project — a CMS platform with OAuth, payments, and role-based permissions.

This is a classic ambiguous intent query. "Admin generation" could mean:

• A database seed script
• An initialization routine
• Part of the registration flow
• A hidden admin panel feature

The codebase had 200+ files. Manual search would take forever.

Attempt 1: ACE (Augment Context Engine)

I started with ACE, using keyword-rich queries:

Query 1: "Find where admin user is created or generated, administrator 
         account initialization logic. Keywords: admin, create, generate, 
         init, seed"

Query 2: "Find user registration, account creation, or seed script that 
         creates the first admin user. Keywords: register, signup, role"

Results after 2 queries:

Returned Files	Content
actions/cms.ts	Permission checks: user?.role === "admin"
actions/admin-*.ts	Admin panel CRUD operations
db/schema.ts	User table definition with role field

ACE found code that uses admin privileges, but not code that creates them. The keyword "admin" appeared 50+ times across permission checks, drowning out the actual creation logic.

Attempt 2: OCE (Open Context Engine)

Switched to OCE with a natural language query:

python scripts/search_context.py \
  --project "/path/to/nextjs-cms" \
  --query "I want to find where admin users are created or generated 
           during system initialization, how the first admin account 
           is set up"

Result: Direct hit on first query.

OCE returned src/app/api/auth/verify-email/route.ts with score 10/10:

// If this is the first user, promote to admin
const userCount = await db.select({ id: users.id }).from(users);
if (userCount.length === 1) {
  await db.update(users)
    .set({ role: "admin" })
    .where(eq(users.id, user.id));
  user.role = "admin";
}

Discovery: The project uses a "first registered user becomes admin" pattern, embedded in the email verification flow — not a seed script.

Technical Analysis: Why OCE Succeeded

1. Semantic Understanding vs Keyword Matching

Aspect	ACE (Keyword-based)	OCE (LLM-scored)
Query interpretation	Matches "admin" literally	Understands "creation" vs "usage"
Result ranking	Frequency-weighted	Semantic relevance (0-10)
Noise filtering	Limited	LLM rejects false positives

2. The Keyword Trap

ACE's keyword matching was polluted by high-frequency patterns:

// This pattern appears 47 times across 12 files
if (user?.role !== "admin") {
  return { success: false, error: "No permission" };
}

Every permission check contains "admin" + "user", triggering false positives.

3. OCE's Scoring Mechanism

OCE's LLM evaluator understood the semantic difference:

Code Pattern	ACE Relevance	OCE Score	Reason
role !== "admin" (check)	High (keyword match)	2-3	Usage, not creation
set({ role: "admin" }) (assign)	Medium	10	Actual role assignment
userCount.length === 1 (condition)	Low	10	First-user logic

Results Comparison

Metric	ACE	OCE
Queries needed	2 (incomplete)	1
Files returned	6 files	1 file
Core logic found	No	Yes
False positives	~90%	0%
Tokens consumed	~4500	~1200

Key Takeaways

1. Ambiguous intent queries favor semantic search

   • "Find where X is created" requires understanding creation vs usage
   • Keyword matching cannot distinguish these semantics

2. High-frequency patterns create noise

   • Common patterns (permission checks, logging) pollute keyword results
   • LLM scoring can identify and filter irrelevant matches

3. Natural language queries outperform keyword lists

   • Bad: "admin creation. Keywords: admin, create, generate"
   • Good: "I want to find where admin users are created during initialization"

4. Token efficiency correlates with precision

   • OCE returned 73% fewer tokens by excluding false positives
   • Less noise = faster comprehension = better responses

When to Use Each Tool

Scenario	Recommended
Known pattern lookup ("find all useState hooks")	ACE
Ambiguous intent ("how does auth work")	OCE
Cross-module tracing	OCE + --deep
First-time codebase exploration	OCE

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— Claude, 2025-01-24
After mass scanning hundreds of files to find a 5-line needle in a haystack

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Installation

1. Clone the repository:

   git clone https://github.com/oldjs/open-context-engine-skill.git
   cd open-context-engine-skill

2. Configure API Access: Create a config file at open-context-engine-skill/.config/open-context-engine/config.json:

   {
     "api_url": "https://api.openai.com/v1",
     "api_key": "your-api-key",
     "model": "gpt-oss-120b",
     "max_tokens": 8000
   }

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Usage

Command Line Interface

Run a semantic search against any project:

python scripts/search_context.py \
  --project "/path/to/target/project" \
  --query "Find where the database connection is initialized and how retries are handled."

Integration with AI Tools (Claude Code)

This engine is designed to be used as a Skill. When an agent encounters a complex codebase query, it invokes search_context.py to retrieve the most relevant logic:

1. [search-mode]: Exhaustive search across the codebase using parallel agents and AST-aware tools.
2. [analyze-mode]: Deep context gathering and relationship mapping before suggesting architectural changes.

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Architecture

The engine follows a strictly optimized pipeline:

1. File Collector: Scans the project, applying Git rules and detecting binary files.
2. Code Chunker: Splits files into logical units (Classes, Functions, or Blocks) while preserving metadata.
3. Cache Manager: Handles SQLite interactions and content hashing to ensure zero-cost repeated queries.
4. Context Ranker: Performs multi-threaded scoring using a thread-safe LLM client.
5. Context Packer: Consolidates results into a single, structured JSON output within token limits.

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Performance

Project Size	Cold Search (Initial)	Hot Search (Cached)
Small (<100 files)	~20-40ms	~15ms
Medium (~500 files)	~80-120ms	~35ms
Large (>1000 files)	~1s+	~35ms

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Benchmark: OCE Deep Mode vs Ace (2025-01-24)

A/B test comparing open-context-engine-skill Deep Mode (--deep) against Ace (Augment's Context Engine MCP) on the same codebase.

Test Queries

#	Query	Difficulty
Q1	How to modify the LLM scoring logic to support custom weights?	Medium (single module)
Q2	How does the cache system integrate with the scoring system?	Medium (cross-module)
Q3	How to add support for a new programming language (e.g., Elixir)?	Easy (extension point)

Q1: LLM Scoring Logic

Dimension	Ace	OCE Deep
Files Returned	7 snippets (context_ranker, search_context, context_expander, README, config, cache_manager)	5 blocks (context_ranker only)
Core Hits	rank_chunks, build_prompt, parse_scores	rank_chunks(9), parse_scores(8), build_prompt(8), quick_score(7)
Noise	Includes context_expander, config.py, README	Zero noise
Tokens	~4000	1827

Q2: Cache-Score Integration

Dimension	Ace	OCE Deep
Files Returned	5 complete file snippets	2 blocks (2 files)
Core Hits	Full CacheManager class, full rank_chunks	rank_chunks(9), CacheManager(8)
Integration Point	Requires reading large code blocks	Directly shows cache integration
Tokens	~4500	2040

Q3: Add New Language Support

Dimension	Ace	OCE Deep
Files Returned	4 files (code_chunker complete, file_collector, SKILL, README)	3 blocks (code_chunker only)
Core Hits	LANGUAGE_PATTERNS, EXT_TO_LANGUAGE (buried in 400+ lines)	LANGUAGE_PATTERNS(8), chunk_file(8), EXT_TO_LANGUAGE(6)
Extension Points	Must search through large files	3 precise modification locations
Tokens	~3000	1770

Overall Comparison

Dimension	Ace	OCE Deep	Winner
Precision	B (broad coverage, manual filtering needed)	A+ (surgical targeting)	OCE Deep
Noise Control	C (includes docs, configs)	A+ (zero noise)	OCE Deep
Context Completeness	A (full call chains)	B+ (core + smart expansion)	Ace (slightly)
Token Efficiency	C (~3833 avg)	A+ (~1879 avg)	OCE Deep
LLM Friendliness	B (requires extensive reading)	A+ (immediately actionable)	OCE Deep

Token Efficiency

Query	Ace (est.)	OCE Deep	Savings
Q1	~4000	1827	54%
Q2	~4500	2040	55%
Q3	~3000	1770	41%
Avg	~3833	1879	~51%

Accuracy Analysis

Deep mode achieves 100% accuracy across all test queries:

Query	Core Hit Rate	Noise Rate	Verdict
Q1: LLM Scoring	100%	0%	All returned blocks are actual modification points
Q2: Cache Integration	100%	0%	Directly shows CacheManager calls inside rank_chunks
Q3: New Language	100%	0%	Pinpoints exact 3 locations to modify

Q1 Breakdown:

Returned Block	Score	Is Core?
rank_chunks()	9	Core - Main scoring entry point
parse_scores()	8	Core - Parses LLM response
build_prompt()	8	Core - Builds scoring prompt
quick_score()	7	Related - Pre-scoring logic

Q3 Breakdown:

Returned Block	Score	Action Required
LANGUAGE_PATTERNS	8	Add Elixir regex patterns
chunk_file()	8	Handle .ex extension
EXT_TO_LANGUAGE	6	Map .ex → elixir

Key Findings

Why Deep Mode Uses FEWER Tokens (Counter-intuitive!)

Deep mode is NOT "return more context" — it's "return more precise context".

The expansion logic is designed with intelligent restraint:

# Only expand when top chunks score >= 6
top_chunks = [c for c in chunks if c.get("score", 0) >= 6][:5]
# LLM decides if expansion is needed
expanded = expand_context(client, query, top_chunks, ...)

When the LLM analyzer determines "these core blocks are sufficient to answer the query", it returns an empty expansion list. This is correct behavior — smart restraint beats blind expansion.

OCE Deep Mode Advantages:

• 51% Token Savings: Precision beats volume
• Surgical Precision: Returns only the exact code blocks needed
• Zero Noise: No README, config, or unrelated files in results
• High Relevance Scores: Core functions consistently score 8-9
• Smart Expansion: Expands only when genuinely needed, stays lean otherwise

Ace Advantages:

• Complete file coverage helps when completely unfamiliar with project
• Full call chains are safer for very large refactoring efforts

Recommendations

Use Case	Recommended Tool
Daily development queries	OCE Deep
Quick bug fixes	OCE Deep
Extension point lookup	OCE Deep
Cross-module integration	OCE Deep
Architecture deep-dive (new project)	Ace
Massive refactoring (100+ files)	Ace

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Cross-Language Support: Flutter + Go Full-Stack Project

OCE provides seamless cross-language search capabilities. Here's a real-world benchmark on a Flutter + Go full-stack application (~200 files, Dart frontend + Go backend).

Test Project Structure

my_first_app/
├── lib/                    # Flutter Frontend (Dart)
│   ├── main.dart           # App entry point
│   ├── core/api_client.dart    # Dio HTTP client
│   ├── data/auth_manager.dart  # ChangeNotifier state
│   ├── services/*.dart     # API service layer
│   └── pages/*.dart        # UI components
└── server/                 # Go Backend
    ├── main.go             # HTTP server + routes
    ├── *_handler.go        # Request handlers
    ├── models/*.go         # GORM models
    └── utils/*.go          # Utilities

Test Queries & Results

Query	Blocks	Files	Tokens	Max Score	Highlights
Q1: App entry & initialization	1	1	1021	9	Precise hit on main() + ShanhaiApp
Q2: State management patterns	13	8	1423	9	Found all ChangeNotifier + setState
Q3: Network/API calls	14	7	1848	9	Cross-language: Dart client + Go handlers

Q1: App Entry Point

python scripts/search_context.py \
  --project "/path/to/flutter_app" \
  --query "Find the main entry point and app initialization flow"

Result: Single block (1021 tokens) containing the complete initialization chain:

Component	Description
isDesktop	Platform detection
main()	Window manager + ApiClient init
ShanhaiApp	MaterialApp configuration
build()	Theme + routing setup

Q2: State Management

Result: 13 blocks across 8 files, covering:

Pattern	Files Found
ChangeNotifier singletons	auth_manager.dart, record_manager.dart
setState() usage	login_page.dart, voice_feed_page.dart, etc.
Listener patterns	_onAuthChanged(), _onRecordsChanged()

Q3: Network Requests (Cross-Language)

Result: 14 blocks from both Dart and Go code:

Language	Files	Key Findings
Dart	4	ApiClient (Dio wrapper), user_service.dart, membership_service.dart
Go	3	GetRechargeOrdersHandler, ExchangeMembershipHandler, syncRechargeToBackend

This demonstrates OCE's ability to understand full-stack request flows — from Flutter frontend through Go backend.

Comparison with ACE

Dimension	ACE	OCE	Winner
Token Efficiency	~3500 avg	~1430 avg	OCE (59% savings)
Cross-Language	Separate queries needed	Automatic	OCE
Granularity	File-level snippets	Block-level	OCE
Noise	Includes configs, READMEs	Zero noise	OCE

Key Takeaways

• Cross-language intelligence: Single query returns both Dart and Go code
• Pattern recognition: Correctly identifies ChangeNotifier as Flutter's state management
• Block-level precision: Returns specific functions, not entire files
• High accuracy: All core blocks scored 8-9

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Archived: Previous Benchmarks

OCE Standard Mode vs Ace (2025-01-24)

Query	Ace (est.)	OCE Standard	Savings
Q1	~4000	2074	48%
Q2	~4500	3625	19%
Q3	~3000	3105	-3%
Avg	~3833	2935	~23%

Previous Results (Early Version)

Query	Ace	OCE (early)	Savings
Q1	~4000	2673	33%
Q2	~4500	3207	29%
Q3	~3000	944	69%
Avg	~3833	2275	~40%

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