Free tools Windows power users keep installed
One-click scans. No signup required.
J.A.R.V.I.S. Framework is a checklist-based workflow for auditing AI-assisted projects and checking launch and deployment readiness. Its author reports 158 checks across those areas, with a central rule: “No evidence = No PASS.” That is a useful organizing principle, but the published check count is not proof that the framework finds every vulnerability or improves security outcomes.
What J.A.R.V.I.S. is—and what it is not
Sreegeethesh describes J.A.R.V.I.S. as a “universal audit and diagnostic framework for AI coding agents.” The project is distributed as software through a public repository, not as a standalone physical product. Its README describes a collection of trigger phrases and slash-command skills intended to structure checks inside supported coding-agent environments.
As an Amazon Associate I earn from qualifying purchases.
The distinction matters: the project sources describe a checklist framework, not an independently validated vulnerability scanner. They do not report an external audit, measured detection rates, or evidence that using the framework improves security outcomes. Its inventory can help make review tasks explicit, but the number of checks alone does not establish their depth or effectiveness.
What the 158 checks cover
The author and project README report 158 checks in three groups. These are project-published inventory counts; they are not outcome statistics.
#1 Best Overall
| Group | Project-reported checks | Examples of stated coverage |
|---|---|---|
| Security: House Party Protocol | 70 | Secrets, authentication and authorization, SQL injection, cross-site scripting (XSS), server-side request forgery (SSRF), insecure direct object references (IDOR), cross-site request forgery (CSRF), cross-origin resource sharing (CORS), secure cookies, AI security, and CI/CD security. |
| Web launch: Drop My Needle | 46 | Performance, accessibility, legal requirements, user experience, asset optimization, forms, error pages, and launch configuration. |
| Deployment: Prepare for Landing | 42 | Git hygiene, environment files, dependencies, build readiness, health checks, logging, rollback preparation, and operational readiness. The README also mentions deployment readiness and the OWASP Top 10. |
The counts and coverage labels come from the project README and Sreegeethesh’s DEV Community article. They show the framework’s stated scope, not that each control is comprehensive or that every relevant risk is covered.
How the workflow is meant to work
The project describes four stages, from inspecting a repository to reporting findings. Its stated sequence is:
- Diagnostic Discovery: inspect the repository and configuration to understand the project.
- Threat Audit: evaluate checklist controls and collect supporting evidence.
- Protocol Remediation: address findings within the framework’s stated authorization boundary.
- Secure Reporting: record findings and their status, including in
.jarvis/STATE.mdso they can persist beyond an AI session.
The article lists three statuses: PASS, FAIL, and WAITING_AUTH. In the author’s formulation, “No evidence = No PASS”: a check should not be marked as passed without evidence. A WAITING_AUTH state signals that a person must make or approve a decision rather than letting the agent proceed as if permission had been granted.
Recommended Free Tools
Why the approval boundary matters
An AI coding agent may be able to edit code, configuration, or connected services. The article says the framework is intended to allow safe code fixes automatically while requiring human approval for high-impact changes. The README says it halts for changes involving infrastructure, secrets, or external systems. These are the author’s and project’s design claims; the available sources do not independently verify how consistently those gates are enforced.
The boundary is meaningful because a seemingly small change can affect production infrastructure, credentials, a database, billing configuration, or an external service. A review process should make clear what the agent may change on its own, what evidence supports a proposed action, and where a human must authorize the next step. A status label is only useful if the team can inspect the evidence and understand the action being held.
Supported environments and installation options
The article says J.A.R.V.I.S. is designed for Antigravity, OpenCode, Claude Code, Cursor, Windsurf, and Cline / Roo Code. The README lists Antigravity IDE, OpenCode, Claude Code, Cursor, Windsurf, and Cline. The project describes both global installation, for availability across projects, and project-level installation, for inclusion in a target repository. It provides shell and PowerShell installation scripts.
Those are integrations claimed by the project, not independently tested compatibility results. Check the current repository README for the installation instructions and supported options before adopting it; repository details can change.
How to assess whether it fits your team
Rather than treating a published checklist total as a security score, evaluate the framework against the way your team actually builds and ships software:
- Coverage and granularity: Are the listed checks specific enough to guide review of your stack and threat model?
- Evidence: Does each status link to evidence that a developer or reviewer can inspect later?
- Remediation permissions: Can you distinguish read-only inspection, automatic code changes, and actions that require approval?
- Environment fit: Does the integration work with the coding tools and repository conventions your team uses?
- Maintenance: Are the checks updated and compatible with your existing security review and release process?
These are evaluation questions, not established advantages of J.A.R.V.I.S. The project states that it is MIT-licensed; consult the repository’s license file for the applicable license text.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
