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J.A.R.V.I.S. Framework is a checklist-based workflow for auditing AI-assisted code and checking launch and deployment readiness. Its author describes 158 checks, evidence-backed statuses, and human approval gates for sensitive changes. Those are project claims—not proof that the framework independently finds vulnerabilities or improves security outcomes.
What J.A.R.V.I.S. is—and what it is not
Sreegeethesh presents J.A.R.V.I.S. as a “universal audit and diagnostic framework for AI coding agents,” built around trigger phrases and slash-command skills. The project is intended to add repeatable engineering checks to a workflow where an AI agent can produce code quickly.
The README and the author’s article describe a structured checklist framework. The reviewed sources do not establish that it is a standalone vulnerability scanner, that it detects every relevant flaw, or that using it has been shown to improve security outcomes. Its check totals describe the project’s inventory, not measured effectiveness.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsRead Sreegeethesh’s article on DEV Community and view the J.A.R.V.I.S. Framework repository.
#1 Best Overall
What the 158 checks cover
The project reports 158 checks divided among three named protocols. The counts are stated by the author and repository; they should be read as the project’s published inventory, not as an independently validated coverage score.
| Protocol | Project-reported checks | Stated focus |
|---|---|---|
| House Party Protocol | 70 security checks | 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. |
| Drop My Needle | 46 web launch checks | Performance, accessibility, legal requirements, user experience, asset optimization, forms, error pages, and launch configuration. |
| Prepare for Landing | 42 deployment checks | Git hygiene, environment files, dependencies, build readiness, health checks, logging, rollback preparation, operational readiness, and deployment readiness. The README also references the OWASP Top 10. |
The practical distinction is that the framework extends beyond application-security checks: its stated scope also includes preparing a web project for launch and deployment.
How its audit workflow is supposed to work
The project describes four phases: Diagnostic Discovery, Threat Audit, Protocol Remediation, and Secure Reporting. In broad terms, the agent inspects the repository and configuration, evaluates checklist controls and gathers evidence, addresses findings within its authorization, and records results.
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Discovery and audit
Discovery starts with repository and configuration inspection. During the audit, the framework evaluates the controls and collects evidence. Sreegeethesh summarizes the intended standard as “No evidence = No PASS.” The listed statuses are PASS, FAIL, and WAITING_AUTH; the last indicates that a decision or authorization is needed rather than an evidenced pass.
Remediation and reporting
The author says straightforward code issues can be fixed automatically, while higher-impact changes require human approval. The README describes halting for changes involving infrastructure, secrets, or external systems. Findings are intended to persist in .jarvis/STATE.md, so they remain available after an AI session ends.
These are design descriptions from the author and project, not independent verification that every agent integration enforces the boundaries reliably. In practice, the distinction matters: changing a code file is not the same risk as altering production infrastructure, credentials, a database, billing configuration, or an external service. A workflow should make the latter changes visible and require an accountable human decision.
Rank #4
Supported environments and installation model
The article says the framework 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. These are project-stated integrations; the reviewed sources do not provide independent compatibility testing.
The README describes two installation approaches: global installation, for use across projects, and project-level installation, included in a target repository. It provides shell and PowerShell install scripts. Consult the repository’s current README for the applicable commands and setup details, since repository instructions can change.
Best Value
How to evaluate a checklist framework like this
A large checklist can improve consistency only if its controls fit the codebase and the team follows through on evidence and approvals. Before relying on one in a real workflow, consider:
- Coverage and granularity: Do the checks address the languages, frameworks, deployment model, and threat profile of the project?
- Evidence quality: Does each result point to something a reviewer can inspect, rather than merely asserting that a check passed?
- Change authority: Can you distinguish read-only analysis from automatic edits, and are sensitive operations explicitly approval-gated?
- Environment fit: Does the integration work with the coding agent and repository setup your team actually uses?
- Maintenance: Are the checks updated as dependencies, threats, and project requirements change, and do they fit alongside existing security review?
These are evaluation questions, not established advantages of J.A.R.V.I.S. The project sources do not provide comparative results against other tools or evidence of an external audit.
Availability and license
J.A.R.V.I.S. is distributed as software through its public GitHub repository; the README states that the project is MIT-licensed. Refer to the repository’s license file for the license text and the README for the current installation guidance.
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