CleanSlop vs security-audit-skill: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CleanSlop and security-audit-skill — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CleanSlop
CleanSlop
Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.
Key features
- Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
- System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
- Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
- Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
- Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
- Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
- GitHub App Integration: Scan only the repositories you select, read-only access.
- Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.
Best for
- A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
- A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
- A side-project builder verifies that one user's data cannot be read by another through database policies.
- A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
- A team lead gets a short prioritized list instead of hundreds of linter warnings.
- An indie hacker books an expert triage call to confirm an ambiguous finding.
security-audit-skill
Cloudflare
Open-source coding-agent skill from Cloudflare that runs multi-phase security audits with adversarially verified, machine-readable findings.
Key features
- Six-Phase Audit Workflow: Reconnaissance, coverage-led hunting, candidate validation, structured output, independent verification and reporting, run by isolated agents.
- Coverage Ledger: Maps architecture, trust boundaries and input surfaces into coverage-ledger.json so hunters are assigned systematically and gaps are found by coverage critics.
- Adversarial Validation: Every candidate goes to a fresh verifier that tries to disprove it; the agent checking a finding is never the one that found it.
- Machine-Readable Findings: Confirmed, needs-validation and rejected records in findings.json validated against a JSON schema with zero-dependency Node.js validators.
- Extensive Attack-Class Library: Hunting prompts for memory safety, LLM/prompt injection, web protocol and auth, client-side, supply chain, cloud, RPC, data isolation and local IPC targets.
- Additive Multi-Run Coverage: Subsequent runs reuse prior ledgers and findings to target gaps and revalidate changed source.
- Sandbox-Aware Execution: Requires an OS-enforced sandbox for running target code; without it, leads stay as needs-validation instead of executing untrusted code.
Best for
- Pre-Release Code Audit: Running a full security audit on a repository with a coding agent before shipping.
- Building an Internal Vulnerability Harness: Using the skill as the starting point for a larger automated vulnerability discovery pipeline.
- LLM Application Review: Hunting prompt-injection and tool-misuse issues in LLM-backed services.
- Supply Chain and CI Review: Checking dependencies, release, signing and CI configurations for weaknesses.
- Triage-Ready Reporting: Producing verified, schema-validated findings that security teams can ingest automatically.