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Competitor Comparison

Last checked: June 22, 2026.

This page only uses capabilities confirmed in the official or primary sources linked in Evidence. If a cited source does not clearly document a capability, the table says Not claimed in cited docs instead of treating it as proven absent.

This is not a claim that a tool cannot be extended with plugins, custom rules, or another product in the same vendor platform. It is a conservative comparison of what the cited docs explicitly support.

Skylos At A Glance​

The cited Skylos docs confirm this combination in one CLI:

CapabilitySkylos source-backed scope
Multi-language dead codePython, TypeScript/JavaScript, Java, Go, Kotlin, PHP, Rust, Dart, and C# source scanning with shared finding output. [S1]
Framework-aware dead codeDjango, Flask, FastAPI, Pydantic, Celery, pytest, and related implicit-entrypoint handling for Python. [S2]
Security SASTTaint/data-flow and dangerous-pattern rules for SQL injection, command injection, SSRF, XSS, path traversal, unsafe deserialization, LLM-output sinks, and more. [S3] [S4]
SecretsHardcoded credential detection for AWS, GitHub, Slack, Stripe, and generic secret-looking values. [S5]
Dependency/SCA checks--sca reports known dependency vulnerabilities with package, CVE/advisory ID, reachability, and fixed version. [S6]
CI/CD securityGitHub Actions workflow and action metadata scanning for risky triggers, token scope, unpinned actions, template injection, secrets, OIDC, cache, and artifact policy. [S7]
Quality gateLocal and cloud quality-gate workflows for blocking new issues without requiring all legacy findings to be fixed first. [S8]
AI-code defenseLLM integration discovery and defense checks for prompt boundaries, unsafe output handling, model pinning, logging, cost controls, and OWASP LLM mapping. [S9]
Agent-to-merge workflowAgents or developers write code; Skylos can scan changed lines, comment evidence in GitHub review workflows, and block new issues through a quality gate. [S8] [S12]
Local-first workflowCore static analysis runs locally; cloud upload, dashboard policy, and LLM-assisted workflows are optional. [S10]

No competitor row below is used unless there is a direct source for it.

Why This Comparison Matters​

AI coding is already a merge-risk problem, not just an editor-productivity problem. Sonar's 2026 State of Code Developer Survey reports that developers say 42% of their committed or contributed code is currently AI-generated or significantly AI-assisted, rising to 65% by 2027. The same report says 96% of developers do not fully trust AI-generated code to be functionally correct, and only 48% always check AI-assisted code before committing. [I1]

That makes the practical product shape simple:

Agent writes code
-> scanner comments proof
-> gate blocks bad merges

Skylos is built closest to that workflow: local analysis first, GitHub review comments when teams want PR evidence, and quality gates for blocking new issues instead of forcing teams to clean every legacy finding before adoption. [S8] [S12]

Feature Matrix​

Legend:

  • Yes means the cited docs confirm the capability.
  • Limited means the cited docs confirm a narrower or product-specific form.
  • Separate means it exists in the vendor platform, but not as the same scanner.
  • Not claimed in cited docs means the reviewed official source did not clearly claim it.
CapabilitySkylosGitHub CodeQL / GitHub code scanningSonarQubeSemgrepVultureSnyk CodeRuff
Primary scopeLocal-first static analysis CLI for dead code, security, secrets, dependencies, quality, CI config, and AI-code defense.Code scanning for security vulnerabilities and coding errors; CodeQL is GitHub's code analysis engine. [G1]Automated code review and static analysis across many languages. [Q1]SAST, SCA, and secrets detection through CE and the AppSec Platform. [M1]Python dead-code finder. [V1]Developer-first SAST; Snyk platform also has open-source dependency scanning. [Y1] [Y2]Python linter and formatter. [R1]
Dead or unused codeYes: multi-language dead code plus confidence and framework-aware handling. [S1] [S2]Not claimed in cited docs.Limited: unused-code rules are documented, such as JavaScript unused local variables/functions. [Q2]Not claimed in cited docs as a dead-code removal workflow.Yes: Python unused code and unreachable code with confidence scores. [V1]Limited: Snyk Code says its engine can find coding issues including dead code. [Y1]Limited: unused imports and unused variables. [R2] [R3]
Framework-aware dead codeYes: documented framework entrypoint handling. [S2]Not claimed in cited docs.Not claimed in cited docs.Not claimed in cited docs.Limited: --ignore-decorators and whitelists can suppress framework false positives. [V1]Not claimed in cited docs.Not claimed in cited docs.
Security SASTYes: taint/data-flow and dangerous-pattern rules. [S3] [S4]Yes: code scanning with CodeQL finds security vulnerabilities and errors. [G1]Yes: rules include vulnerabilities and security hotspots. [Q3]Yes: Semgrep Code is SAST for first-party code. [M2]Not claimed in cited docs.Yes: Snyk Code is SAST and documents data-flow analysis. [Y1]Not claimed in cited docs.
Dependency/SCA/CVEYes: --sca dependency vulnerability output with reachability and fix version. [S6]Separate: Dependabot alerts are GitHub's dependency vulnerability feature. [G4]Yes, through SonarQube Advanced Security SCA add-on. [Q4]Yes: Semgrep Supply Chain detects vulnerable open-source dependencies. [M3]Not claimed in cited docs.Yes: snyk test checks open-source packages and dependencies. [Y2]Not claimed in cited docs.
SecretsYes: built-in secret scanning. [S5]Separate: GitHub secret scanning is a platform feature separate from CodeQL. [G3]Yes: Secrets is a documented SonarQube language/scope area. [Q5]Yes: Semgrep describes secrets detection as one of its scan tools. [M1]Not claimed in cited docs.Limited: hardcoded secret rules run during Snyk Code SAST, but Snyk says standalone secret scanning is via third-party tools. [Y1]Not claimed in cited docs.
GitHub Actions workflow securityYes: scans workflows and local action metadata. [S7]Yes: CodeQL's supported-language table includes GitHub Actions workflow and action metadata files. [G2]Yes: supported-language table includes GitHub Actions. [Q1]Limited: Semgrep SARIF docs show GitHub Actions security rule output, but this page does not treat that as full workflow-security coverage. [M5]Not claimed in cited docs.Not claimed in cited docs.Not claimed in cited docs.
Custom rulesYes: YAML rules and paid-plan Python rules. [S11]Yes: custom CodeQL queries and packs. [G5]Yes: rule management and quality profiles are documented. [Q3]Yes: custom YAML rules are first-class. [M4]Limited: config, whitelists, ignore names, and ignore decorators. [V1]Not claimed in cited docs.Limited: linter configuration and rule selection, not custom security rules in cited docs. [R1]
SARIFYes: Skylos docs describe SARIF output as part of shared finding output. [S1]Yes: GitHub code scanning accepts SARIF uploads and CodeQL can upload results. [G6]Limited: SonarQube imports SARIF as external issues. [Q6]Yes: JSON and SARIF output fields are documented. [M5]Not claimed in cited docs.Not claimed in cited docs.Not claimed in cited docs.
Local workflowYes: core analysis runs locally without an API key. [S10]Limited: CodeQL CLI is documented, while GitHub code scanning, Dependabot, and secret scanning are platform features. [G1] [G4]Limited: scanner runs locally but reports to SonarQube Server/Cloud. [Q7]Yes for local scans; platform features require Semgrep AppSec Platform. [M1]Yes: local CLI. [V1]Limited: CLI exists; no-upload requires Snyk Code Local Engine. [Y1]Yes: local CLI tooling. [R1]
AI-code controlsYes: LLM integration discovery and defense checks. [S9]Limited: cited GitHub sources here cover code scanning, Dependabot, and secret scanning, not LLM-integration defense.Yes: AI Code Assurance is documented for projects containing AI-generated code. [Q8]Yes: AI-powered detection, Multimodal, and Guardian are documented. [M2] [M6]Not claimed in cited docs.Not claimed in cited docs.Not claimed in cited docs.

Practical Takeaways​

If you are using GitHub CodeQL​

Keep it for deep security analysis in GitHub. Skylos adds a local-first pass that also checks dead code, secrets, SCA, quality gates, AI-code patterns, and GitHub Actions workflow risk in the same CLI. CodeQL's cited language support is strong for C/C++, C#, Go, Java/Kotlin, JavaScript/TypeScript, Python, Ruby, Rust, Swift, and GitHub Actions. Skylos' cited language support adds dead-code-oriented coverage for PHP, Dart, C#, Shell security, deployment configuration, and the Skylos-specific AI-code rules. [S1] [G2]

If you are using SonarQube​

SonarQube is the broadest platform comparison here: it has many languages, quality gates, security rules, secrets, AI Code Assurance, and SCA through Advanced Security. Skylos is smaller and more local-first, with a narrower focus on running one CLI for dead code, security, secrets, SCA, quality, CI/CD workflow risk, and AI-code guardrails before a PR is merged.

If you are using Semgrep​

Semgrep is strong for custom security rules, SAST, SCA, secrets, CI workflows, and AI-assisted security review. Skylos' cited advantage is not "more Semgrep"; it is the combined local workflow for framework-aware dead code, security, dependency reachability, quality, GitHub Actions risk, and LLM integration defense without configuring separate tools for each category.

If you are using Vulture​

Vulture is focused and useful for Python dead code. Its own README documents the core tradeoff: Python's dynamic nature can make static analyzers miss dead code, and code called implicitly can be reported as unused. Skylos is designed for that exact false-positive problem with framework and confidence handling, then adds non-dead-code checks Vulture does not claim in its cited README.

If you are using Snyk Code​

Snyk Code is a SAST product with data-flow analysis, CI/IDE workflows, and documented coding issue detection including dead code. Skylos' cited difference is the single local CLI surface that combines dead-code cleanup, SAST, secrets, SCA, CI workflow analysis, quality gates, and AI-code checks.

If you are using Ruff​

Ruff is fast Python linting and formatting. It can catch unused imports and unused variables, but the cited Ruff docs do not position it as a SAST, SCA, secrets, CI workflow security, or AI-code defense tool. Skylos can sit beside Ruff when you want dead-code reachability, security, and governance checks beyond linting.

Evidence​

Skylos Sources​

Industry Sources​

Competitor Sources​