CodeImpact
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CodeImpactfind unused exports in the project"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CodeImpact
Persistent codebase understanding for AI assistants.
npm i codeimpactCodeImpact is an MCP server that indexes your codebase and gives AI assistants like Claude the ability to understand your project's structure, dependencies, and history across sessions. It also includes a Super Agent System — a self-improving multi-agent architecture that detects features, generates knowledge artifacts, learns from mistakes, and continuously improves code quality using the agentskills.io open standard.
What CodeImpact Does
Indexes your code - Extracts functions, classes, imports, and exports using true Tree-sitter AST parsing
Builds a dependency graph - Tracks what files import what, transitively
Super Agent System - Self-improving multi-agent architecture with feature detection, research distillation, and outcome-driven learning
AI Knowledge System - AI assistants create reusable SKILL.md files that persist across sessions
Dead code detection - Finds unused exports and orphan files with confidence scoring
Test impact analysis - Shows which tests to run when you change a file
Blast radius analysis - Risk scoring and critical path detection for any file change
Knowledge gap detection - Identifies uncovered technologies and high-risk areas
Auto-generated skills - Generates starter SKILL.md files from real codebase data (technologies, features, conventions) on first index
Self-learning loop - Records outcomes, diagnoses failures, promotes confirmed patterns to rules; agent failures automatically feed back into skills
Agent lifecycle management - Proposes merge/split/prune of feature agents based on git history
Research distillation - Fetches and distills technology documentation with trust scoring
Cost tracking - Monitors token usage and costs for CodeImpact queries
Detects circular dependencies - Finds import cycles in your codebase
Records decisions - Stores architectural decisions that persist across sessions
Semantic search - Find code by meaning using local embeddings
All processing happens locally on your machine. No cloud services, no telemetry.
Quick Start
# Install globally
npm i -g codeimpact
# Or use with npx (no install)
npx codeimpact init
# Initialize in your project
cd your-project
codeimpact initThis registers your project and configures Claude Desktop, Claude Code, OpenCode, and Cursor automatically. Restart your AI tool and you're ready.
Windows users: If upgrading, close any AI tools using CodeImpact first (or run
taskkill /f /im node.exe) before reinstalling. Windows locks native binaries while they're in use.
Super Agent System
CodeImpact includes a production-grade multi-agent system that automatically detects project features, generates knowledge artifacts, learns from mistakes, and continuously improves. The system generates SKILL.md and AGENT.md files following the agentskills.io open standard — compatible with Claude Code, Cursor, Codex, Gemini CLI, and 27+ other AI agents.
How It Works
Technology detection — scans lockfiles, manifests, and configs to identify your stack
Feature detection — clusters source files into cohesive features using directory structure, import graphs, and CODEOWNERS
Research distillation — fetches and distills technology documentation with trust scoring
Agent generation — creates feature-scoped agents with scope boundaries, allowed tools, and success criteria
Self-learning loop — records outcomes, diagnoses failures using a 15-rule decision table, and promotes confirmed patterns to rules
Lifecycle management — proposes merge/split/prune of agents based on git co-change analysis
Self-Learning Pipeline
Outcome recorded → Diagnose (15-rule decision table)
→ wrong-assumption + fix → Patch SKILL.md immediately
→ wrong-assumption + fix + no SKILL.md → Patch matching knowledge skill's Watch Out
→ outdated-research → Flag for re-research
→ 2+ similar failures → Write lesson to AGENT.md (quarantine)
→ 3+ confirmations → Promote lesson to SKILL.md Rules
→ 90 days unconfirmed → Decay lessonAuto-Generated Skills (New in 0.5.0)
CodeImpact now auto-generates starter skills from real codebase data during knowledge generation. This closes the intelligence loop — review/verify pipelines have rules to check against from day one, instead of operating in a vacuum.
Three skill categories are generated:
Category | Scope | Cap | Source Data |
Technology patterns |
| 5 | Detected technologies + provider research (pitfalls, best practices, import files) |
Feature skills |
| 10 | Feature clusters (3+ files) + risk files + dependency hotspots + decisions |
Project conventions |
| 1 | Languages, test framework, architecture layers, top patterns, risk/change hotspots |
Idempotency guarantees:
All auto-generated skills have
auto_generated: truein frontmatter metadataRe-generation only triggers when the project grows by >20% in file count
User-edited skills (where
auto_generated: truewas removed) are never overwrittenAuto-generated skills are safely overwritten on regeneration
Feedback loop: When an agent records a failure with a fix, the improvement engine patches the matching skill's ## Watch Out section — so the same mistake is caught by review/verify in future sessions.
Agent Workspace Structure
your-project/
├── .code-impact/ # Agent workspace (auto-generated)
│ ├── config.yaml # Configuration
│ ├── index.json # Manifest with tech, features, outcomes
│ ├── project/ # Project-wide knowledge
│ │ ├── SKILL.md # Technology stack + conventions
│ │ ├── CONVENTIONS.md # Coding standards
│ │ ├── ARCHITECTURE.md # System architecture
│ │ └── AGENT.md # Super agent (coordinator)
│ ├── research/ # Distilled tech documentation
│ │ └── express@4.21.0.md # With trust scores + structured sections
│ └── features/ # Feature-specific knowledge
│ └── auth/
│ ├── SKILL.md # Feature rules, pitfalls, research refs
│ └── AGENT.md # Scope, tools, lessons learned
├── AGENTS.md # Top-level agent directory (auto-generated)
├── knowledge/ # Legacy knowledge workspace
│ └── skills/ # AI-created SKILL.md files
├── .codeimpact/
│ └── codeimpact.db # SQLite database
└── src/CLI Commands for Agents
codeimpact agents init # Initialize agent workspace
codeimpact agents generate # Run full pipeline (detect → research → generate)
codeimpact agents status # Show agent system health + lifecycle proposals
codeimpact agents research # Refresh technology research
codeimpact agents migrate # Migrate knowledge/ → .code-impact/Skill Format (agentskills.io SKILL.md)
---
name: better-sqlite3-patterns
description: Synchronous database patterns. Use when working with database queries.
version: 1.0
metadata:
scope: technology
created_by: ai
---
# better-sqlite3 Patterns
## When to Use
When modifying database queries, adding tables, or working with any file
that imports from src/storage/database.ts.
## Rules
- ALL database access goes through database.ts — never import better-sqlite3 directly.
- Use db.prepare().all() for SELECT, db.prepare().run() for INSERT/UPDATE/DELETE.
## Pitfalls
- db.exec() returns nothing. If you use it for SELECT, you get undefined.
- better-sqlite3 is synchronous. Do NOT wrap in async/await.
## Verification
- npx tsc --noEmit passes with no type errors on database code.MCP Tools
Tool | What It Does |
| Project overview + knowledge gaps + lifecycle proposals |
| List/get agents, skills, research; validate scope; record outcomes; telemetry dashboard |
| Create/improve skills, report outcomes, list signals |
| Semantic search across code and knowledge |
| Check code against learned patterns and skill rules |
| Pre-commit quality checks using skill verification rules |
| Proactive intelligence — conflict detection, deja vu, session resurrection |
| Impact analysis with skill-aware recommendations |
Key Features
Dead Code Detection
Find unused exports and orphan files in your codebase:
codeimpact deadcode
codeimpact deadcode --json --threshold 80Output:
Dead Code Report:
- 4,230 lines of unused code detected
- 12 files with zero imports
- 23 functions never called
Safe to delete: 89% confidenceTest Impact Analysis
Know exactly which tests to run when you change a file:
codeimpact test-impact
codeimpact test-impact --changed src/auth/login.ts
codeimpact test-impact --branch mainOutput:
Analyzing impact of src/auth/login.ts...
Files affected: 12
Tests to run: 8 (instead of 234)
Estimated time: 2m (instead of 28m)
Time saved: 26 minutesBlast Radius Analysis
Understand the risk of changing any file:
codeimpact impact src/core/engine.ts
codeimpact impact src/auth/session.ts --depth 5 --jsonOutput:
File: src/auth/session.ts
Risk Score: 78/100 (HIGH)
Direct dependents: 8 files
Transitive dependents: 34 files
Critical paths affected: src/api/checkout.ts, src/billing/payments.ts
Recommendation: Senior review requiredUsage Dashboard
Track token usage and costs for CodeImpact queries:
codeimpact stats
codeimpact stats --period week
codeimpact stats --period all --jsonOutput:
This Month:
- Queries: 1,247
- Tokens used: 892K
- Estimated cost: $5.35Supported AI Tools
Tool | Setup |
Claude Desktop |
|
Claude Code (CLI) | |
Cursor |
|
OpenCode | |
Any MCP Client | Manual config |
Any tool (HTTP) |
|
All tools share the same data - switch between them freely.
What You Can Ask
Once CodeImpact is running, your AI assistant can:
Understand dependencies:
"What files depend on src/auth/login.ts?"
"Show me the import chain for this module"Analyze impact:
"If I change this file, what else might break?"
"What's the blast radius of changing this module?"
"What tests cover this function?"Build knowledge:
"Check memory_status for knowledge gaps"
"Create a skill for the authentication patterns I just implemented"
"What skills exist for the database layer?"Agent system:
"List all agents and their scopes"
"Validate if auth-agent can modify src/billing/pay.ts"
"Show the telemetry dashboard for this week"
"What lifecycle proposals exist?"Find dead code:
"Are there any unused exports in this project?"
"Which files have no dependents?"Find code:
"Find all authentication-related code"
"Where is the user validation logic?"Check for issues:
"Are there any circular dependencies?"
"What decisions have we made about authentication?"How It Works
CodeImpact watches your project and maintains:
Symbol index - Functions, classes, imports, exports via Tree-sitter AST
Dependency graph - File-to-file import relationships (transitive)
Agent workspace - Auto-generated SKILL.md, AGENT.md, and research files
Auto-generated skills - Starter skills from detected technologies, feature clusters, and project conventions
Self-learning loop - Outcome recording, diagnosis, lesson writing, promotion, and skill patching
Research cache - Distilled technology docs with trust scoring and staleness tracking
Decision log - Architectural decisions that persist across sessions
Embeddings - Semantic search using MiniLM-L6 locally
Telemetry - Generation, learning, and research event tracking (local SQLite)
Lifecycle proposals - Merge/split/prune suggestions from git co-change analysis
Token usage - Query tracking for cost analysis
When your AI assistant asks a question, CodeImpact provides the relevant context. When the AI completes work, the self-learning loop captures what happened — successes confirm existing knowledge, failures trigger diagnosis and improvement.
Language Support
Language | What's Extracted |
TypeScript/JavaScript | Functions, classes, imports, exports |
Python | Functions, classes, imports |
Go | Functions, structs, imports |
Rust | Functions, structs, imports |
Java | Classes, methods, imports |
Parsing is powered by Tree-sitter WASM, providing true Abstract Syntax Tree (AST) understanding rather than fragile regex matching. This ensures 100% accurate symbol extraction, boundary detection, and method signatures across all supported languages.
CLI Commands
# Setup
codeimpact init # Set up project + configure AI tools
codeimpact serve # Start HTTP API server
# Analysis
codeimpact deadcode # Find unused exports and dead code
codeimpact test-impact # Find which tests to run for changes
codeimpact impact <file> # Analyze blast radius of a file
codeimpact stats # Show token usage and costs
codeimpact reindex # Force reindex after git operations
# Agent System
codeimpact agents init # Initialize agent workspace
codeimpact agents generate # Detect → research → generate → learn
codeimpact agents status # Health, outcomes, lifecycle proposals
codeimpact agents research # Refresh technology research
codeimpact agents migrate # Migrate knowledge/ → .code-impact/
# Project Management
codeimpact projects list # List registered projects
codeimpact projects add . # Add current directory
codeimpact projects switch # Switch active project
codeimpact export # Export decisions to ADR files
codeimpact help # Show helpCLI Options
--project, -p <path> # Path to the project directory
--json # Output as JSON
--threshold <percent> # Minimum confidence % (for deadcode)
--changed <file> # Specify changed file(s) (for test-impact)
--git-diff # Use git diff to detect changes
--branch <name> # Compare to branch (e.g., main)
--depth <n> # Max dependency depth (default: 3)
--period <type> # Time period: day, week, month, allHTTP API
For tools that don't support MCP, CodeImpact provides a REST API:
codeimpact serve --project /path/to/project --port 3333Endpoints:
Method | Endpoint | Description |
GET |
| Project stats |
GET |
| Semantic code search |
GET |
| File dependencies |
GET |
| Impact analysis |
GET |
| Find circular deps |
GET |
| List decisions |
POST |
| Record a decision |
GET |
| Get file symbols |
Example:
curl "http://localhost:3333/impact?file=src/auth/login.ts"Manual Configuration
If codeimpact init doesn't work for your setup, add to your Claude Desktop config:
{
"mcpServers": {
"codeimpact": {
"command": "npx",
"args": ["-y", "codeimpact", "--project", "/path/to/your/project"]
}
}
}Config locations:
Windows:
%APPDATA%\Claude\claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonLinux:
~/.config/claude/claude_desktop_config.json
Data Storage
Project data is stored locally in each project:
your-project/
├── .codeimpact/
│ ├── codeimpact.db # SQLite database (index, outcomes, telemetry)
│ ├── tier1.json # Hot context cache
│ └── feature-context.json # Session tracking
├── .code-impact/ # Agent workspace (auto-generated)
│ ├── config.yaml # Agent system configuration
│ ├── index.json # Technologies, features, outcomes, lifecycle
│ ├── project/ # Project-wide SKILL/CONVENTIONS/ARCHITECTURE/AGENT
│ ├── research/ # Distilled tech docs with trust scores
│ └── features/ # Per-feature SKILL + AGENT files
├── knowledge/ # Knowledge workspace
│ ├── skills/ # SKILL.md files (auto-generated + AI-created)
│ │ ├── technology/ # Tech pattern skills (auto-generated)
│ │ ├── features/ # Feature-scoped skills (auto-generated)
│ │ └── core/ # Project conventions (auto-generated)
│ ├── docs/ # Generated documentation
│ └── index.json # Knowledge manifest (includes autoGeneration tracking)
├── src/
└── ...Each project has its own isolated data — no cross-contamination between projects. The .code-impact/ agent workspace is the primary knowledge store; knowledge/ is supported for backward compatibility and can be migrated via codeimpact agents migrate.
Global registry for project listing:
~/.codeimpact/
└── registry.json # Project listPrivacy
All data stays on your machine
No cloud services
No telemetry
Works offline
Development
git clone https://github.com/abhisavakar/codeimpact.git
cd codeimpact
npm install
npm run build
npm test # 180+ tests including 10 eval scenariosArchitecture
src/core/agents/ # Super Agent System (12 modules)
├── orchestrator.ts # Pipeline: init → detect → research → generate → learn
├── tech-detector.ts # Lockfile + manifest scanning
├── feature-detector.ts # Cohesion clustering + CODEOWNERS
├── research-engine.ts # Fetch + distill + trust scoring
├── generator.ts # SKILL.md / CONVENTIONS.md / ARCHITECTURE.md
├── agent-generator.ts # AGENT.md + AGENTS.md shim
├── improvement-engine.ts # Self-learning: diagnose → learn → promote → decay + knowledge skill patching
├── diagnosis-table.ts # 15-rule declarative error classification
├── outcome-storage.ts # SQLite agent_outcomes table
├── lifecycle.ts # Merge/split/prune proposals
├── telemetry.ts # Event emission + dashboard queries
├── marker-writer.ts # Content-hash dedup + token budgets
├── token-budget.ts # Section-priority truncation
├── migration.ts # knowledge/ → .code-impact/ migration
├── research-trust.ts # 7-signal trust scoring
├── remote-provider.ts # GitHub/GitLab/Bitbucket abstraction
├── co-change-analyzer.ts # Git log analysis for feature coupling
└── workspace.ts # Directory management + config
src/core/knowledge/ # Knowledge Layer
├── orchestrator.ts # Knowledge generation pipeline (intel → providers → features → auto-skills → evolution)
├── skill-auto-generator.ts # Auto-generates starter skills from codebase data (NEW in 0.5.0)
├── skill-generator.ts # SKILL.md rendering + writing
├── skill-reader.ts # Read/search skills across all scopes
├── skill-evolution.ts # Evolve existing skills from usage data
├── intelligence-collector.ts # Collect project intelligence (techs, risks, patterns)
├── provider-research.ts # Technology documentation research
├── platform-sync.ts # Sync rules to IDE platform files
├── doc-sync.ts # Sync architecture/component/changelog docs
└── workspace.ts # Knowledge workspace paths + manifest I/OAuthor: Abhishek Arun Savakar - savakar.com
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