Code Graph RAG vs SkillSpector: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Code Graph RAG and SkillSpector — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
C
Code Graph RAG
vitali87
Multi-language monorepo RAG: Tree-sitter parses your codebase into a Memgraph knowledge graph so you can query, edit, and refactor in plain English.
Key features
- Multi-Language Graph Ingest: Tree-sitter parses Python, TypeScript, TSX, JavaScript, Rust, Go, Java, C, C++, C#, PHP, Lua, and Dart into a single language-agnostic Memgraph schema.
- Natural-Language Cypher: The interactive CLI turns plain-English questions into Cypher queries and answers grounded in the real code structure, not vector-only guesses.
- AST-Based Surgical Editing: The agent edits code through structural patches with a diff preview before any change is applied.
- Structural Search & Replace: ast-grep is exposed as an agent tool, so you match and rewrite by AST pattern across the whole codebase instead of regex.
- Pluggable ast-grep Tier: Add a new language from a single YAML pattern file — Ruby was added this way with Module/Function/Class nodes plus import edges.
- Data-Flow Tracing: FLOWS_TO taint edges follow values through assignments, function calls, and I/O sinks across C, Java, C#, and Go.
- Dead-Code Detection: Walk call and reference edges from entry points to find functions and modules nothing reaches.
- Shared Graph Across Projects: Index many repos into one shared graph and query across them; a `clean` subcommand resets from scratch with confirmation.
Best for
- Monorepo Q&A: Ask 'where is refund logic in this monorepo?' and get grounded answers from a graph of the real code, not stale docs.
- AI-Assisted Refactoring: Rename or restructure APIs across languages with AST patches and a diff preview before commit.
- Cross-Language Data-Flow Audits: Trace a value through assignments and function calls to see where sensitive data ends up.
- Dead-Code Cleanup: Find unreachable functions and modules by walking call edges from entry points.
- Codebase Onboarding: Give a new engineer or agent a queryable graph they can explore in natural language.
- Structural Migrations: Use ast-grep to rewrite deprecated patterns (imports, error handling, config lookups) across a polyglot codebase.
S
SkillSpector
NVIDIA
SkillSpector is NVIDIA's open-source security scanner that detects vulnerabilities, malicious patterns, and policy risks in AI agent skills.
Key features
- Vulnerability Pattern Detection: Covers 64 vulnerability patterns across 16 categories including prompt injection, data exfiltration, and privilege escalation.
- Flexible Inputs: Accepts Git repositories, URLs, zip files, directories, and single files for scanning.
- Fast Static Checks: Runs rapid static analysis by default to flag risky instructions, hidden metadata, and overbroad permissions.
- Optional LLM Semantic Analysis: Adds intent-comparison analysis powered by an LLM for issues that need deeper reasoning.
- Supply-Chain & MCP Coverage: Detects supply-chain attacks, memory poisoning, tool misuse, trigger abuse, and MCP-specific risks.
- Taint Tracking & YARA Signatures: Uses taint tracking and YARA signatures to catch dangerous code paths.
Best for
- Pre-Install Skill Vetting: Scan an agent skill before installation to decide whether it is safe to use.
- Marketplace Review: Automate risk scanning inside a skill publishing or catalog pipeline.
- Security Audits: Audit existing agent skills for prompt injection and data exfiltration risks.
- Supply-Chain Defense: Detect malicious or over-permissioned skills introduced through third-party sources.