Phantom import
Dependency referenced in code but missing from lockfile and package manifest.
AI Code Integrity for AI-Native Teams
AI code integrity, before it merges.
AI agents write fast but break differently. Shipmoor detect, explain, fix, and govern AI-introduced defects before they hit production.
- CLI
- scan locally, in CI, and agent loops
- Agent Harness
- wraps Codex, Claude, Cursor
- SARIF
- standard security export format
- Console
- full controll governance dashboard
A gate around AI-authored code.
AI coding agents produce code faster than teams can review it. Shipmoor adds a checkpoint, starting with a CLI that scans locally and in agent workflows, then extending into CI gates, PR review signals, SARIF exports, and an enterprise governance console.
See how it works See how it worksFrom scan to merge
From agent output to governed merge
Shipmoor lives where AI-authored code is created - local CLI, agent skills, CI gates. Each finding ships with severity, confidence, and a remediation path.
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Invoke from inside the agent
Drop the Shipmoor skill into Codex, Claude, Cursor, or your own runner.
shipmoor scanruns in-loop on every generated patch. -
Triage, deep-dive, explain
The CLI separates environment noise from real defects, weights findings by severity, and surfaces named failure modes, e,g. phantom imports, clone clusters, docstring inflation, etc
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Ship a graded action plan
Out comes SARIF, JSON, and a human-readable plan: safe patches, ambiguous flags, and the decisions you still need to make - never a destructive auto-fix.
Built for teams shipping fast with AI agents.
Shipmoor turns AI code defect detection into an end-to-end operating layer: a CLI, agent harness, policy gates, PR review signals, SARIF exports, remediation workflows, audit trails, and proof that AI adoption is getting safer over time.
Explore the product surface Explore the product surfaceCLI core
Run local scans, diff checks, baselines, JSON output, SARIF exports, and policy evaluation without waiting on a SaaS install.
Agent harness
Wrap Codex, Claude, Cursor, and other coding agents with checks that catch fake implementations, phantom imports, and brittle generated glue.
Review surface
Findings become PR comments, CI annotations, and reviewer-ready tasks - each with severity, explanation, and safe fix guidance.
Console
Risk dashboards across repos, teams, agents, and policies. Baselines, audit logs, SARIF aggregation, and self-hosted runner support.
Detect
Explain
Fix
Govern
Start at the command line.
The first install path is a CLI developers and agents can run locally, then the same scanner powers CI, PR comments, SARIF reports, and the console.
Harness the agents, not just the repo.
Shipmoor belongs in the loop where code is created: Codex tasks, Claude sessions, Cursor edits, local patches, CI checks, and pull requests.
The category is AI code integrity.
Traditional code quality catches generic defects. Shipmoor focuses on the failure modes that appear when teams accelerate with AI coding tools.
AI code integrity
Agent harness
CLI-first scanning
Fix before merge
Launch with a pilot-friendly model
Start with the CLI and agent harness, prove value on real generated changes, then expand into CI, PR review, governance, and remediation.
Team Pilot
$29 /dev / month
- CLI and Docker scanner
- Agent harness for AI coding tools
- Baseline mode
- JSON and SARIF output
- PR commenter and console
Enterprise
Custom /annual
- Org console and policies
- Codex, Claude, and Cursor workflows
- SSO, RBAC, and audit logs
- Private or self-hosted runners
- Jira, Slack, SARIF, and PR comments
Questions teams ask first FAQs
Short answers for security, platform, and engineering leaders.
The core is the Shipmoor CLI. It runs locally, in agent workflows, and in CI, then emits findings, scores, JSON, and SARIF for the rest of the platform.
A wrapper around AI coding agents like Codex, Claude, and Cursor. It intercepts generated patches so they can be scanned, scored, explained, and fixed before they ever reach a human reviewer.
No. The CLI and CI scanner can run where the code already lives. Teams can upload findings, hashes, severity, and policy results without storing source code in Shipmoor.
Linters catch syntax issues. SAST catches known vulnerability patterns. Shipmoor catches AI-specific failure modes: placeholder logic that passes tests, hallucinated dependencies, disconnected code paths, and generated error handling that handles nothing.
Yes. Baseline mode lets teams track existing issues while blocking only new risk introduced by a pull request.
Safe fixes for clear-cut cases - like adding a missing import or removing a dead code path. For ambiguous findings, Shipmoor explains the risk and defers to the developer. Enterprise trust depends on restraint, not aggressive auto-patching.