By demon24ru
Persistent incremental knowledge graph for token-efficient, context-aware code reviews with Claude Code. Analyzes codebase structure, detects dead code, traces data flow, and assesses blast radius of changes across multiple repositories.
Deep structural analysis of codebase using communities, flows, wiki, and embedding search. Understand architecture, execution paths, and module boundaries without reading files.
Plan and track implementation work using the Task DAG system. Create structured task trees linked to real code, enforce single-pipeline discipline, and generate handoff context for coders.
Build or update the code review knowledge graph. Run this first to initialize, or let hooks keep it updated automatically.
Safe, graph-powered refactoring with dead code detection, rename preview, workspace audit, and SCIP export. Always preview before applying.
Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
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Stop burning tokens. Start reviewing smarter.
AI coding tools re-read your entire codebase on every task. code-review-graph fixes that. It builds a structural map of your code with Tree-sitter, tracks changes incrementally, and gives your AI assistant precise context via MCP so it reads only what matters.
pip install code-review-graph # or: pipx install code-review-graph
code-review-graph install # auto-detects and configures all supported platforms
code-review-graph build # parse your codebase
One command sets up everything. install detects which AI coding tools you have, writes the correct MCP configuration for each one, and injects graph-aware instructions into your platform rules. It auto-detects whether you installed via uvx or pip/pipx and generates the right config. Restart your editor/tool after installing.
To target a specific platform:
code-review-graph install --platform cursor # configure only Cursor
code-review-graph install --platform claude-code # configure only Claude Code
Requires Python 3.10+. For the best experience, install uv (the MCP config will use uvx if available, otherwise falls back to the code-review-graph command directly).
Then open your project and ask your AI assistant:
Build the code review graph for this project
The initial build takes ~10 seconds for a 500-file project. After that, the graph updates automatically on every file edit and git commit.
Your repository is parsed into an AST with Tree-sitter, stored as a graph of nodes (functions, classes, imports) and edges (calls, inheritance, test coverage), then queried at review time to compute the minimal set of files your AI assistant needs to read.
When a file changes, the graph traces every caller, dependent, and test that could be affected. This is the "blast radius" of the change. Your AI reads only these files instead of scanning the whole project.
On every git commit or file save, a hook fires. The graph diffs changed files, finds their dependents via SHA-256 hash checks, and re-parses only what changed. A 2,900-file project re-indexes in under 2 seconds.
npx claudepluginhub demon24ru/code-review-graph --plugin code-review-graphPermanent coding companion for Claude Code — survives any update. MCP-based terminal pet with ASCII art, stats, reactions, and personality.
Comprehensive skill pack with 66 specialized skills for full-stack developers: 12 language experts (Python, TypeScript, Go, Rust, C++, Swift, Kotlin, C#, PHP, Java, SQL, JavaScript), 10 backend frameworks, 6 frontend/mobile, plus infrastructure, DevOps, security, and testing. Features progressive disclosure architecture for 50% faster loading.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Intelligent prompt optimization: injects the right context at the right moment so Claude lands a better first output. Clarifies vague prompts with research-based questions, plus targeted nudges for approach selection, plan readability, workflow routing, background execution, subagent routing, output readability, user-decision questions, and plan-mode assessment
v9.44.1 — Patch release for Gemini environment/version detection and qwen auth gating. Run /octo:setup.
Harness-native ECC operator layer - 67 agents, 271 skills, 92 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses