Loading the registry…
Developing beautiful NiceGUI applications with AI
Cursor Rules are .cursorrules / .cursor/rules files that steer Cursor's AI; scanned with the same pipeline.
We found this by crawling public sources and nobody has attested to it. That is how most of the registry gets here, and it is not a mark against the artifact — it only means no authenticated act of publication is on record. An artifact can be the vendor's own and still appear here.
8 critical findings reported by cisco-skill-scanner, 6 of them in the installed artifact. The composite score alone does not reflect this — read the per-scanner reports below.
Cursor Rules are .cursorrules / .cursor/rules files that steer Cursor's AI. Review the scanned source before copying them into your own project.
nerlo install -- nice-vibes
4 of 11 scanners examined this package. The rest do not apply to it — language, ecosystem and packaging decide which analyzers can say anything, and a scanner that cannot examine an artifact reports nothing rather than passing it.
v2.0.11
85 findings · 14.4s
v0.3.74
7 findings · 8.3s
v0.1.0
0 findings · 4.2s
v0.1.0
0 findings · 4.3s
v1.4.0
None of the entry points this scanner reads were found in this artifact. What each scanner covers
0 findings · 0.2s
vv0.3.2
No Go packages were found in this artifact. What each scanner covers
0 findings · 0.1s
v0.1.0
This scanner found nothing in this artifact that it assesses. What each scanner covers
0 findings · 1.6s
No report in this scan: govulncheck, osv-scanner, trivy, trivy_image.
1 scan on record. Every scan's full results are retained immutably for 24 months.
| Completed | Composite | Change | Scanners | Status |
|---|---|---|---|---|
| Aug 20, 2026 | Scan Halted | — | agentshield ·n/a: not applicablecisco-skill-scanner: completeagent-audit-kit: completenerlo-behavioral: completenerlo-install-instruction: completecapslock ·n/a: not applicablenerlo-multi-source ·n/a: not applicable | rejected |
Findings in files this artifact installs stand as reported; a model re-read the rest and said which ones it believes are false positives. This is a second opinion published beside the evidence, not a correction to it: the per-scanner reports above are unchanged, every dismissed finding is still listed there at its original severity, and the score and the Flagged badge are computed from those raw severities alone. Nothing below moved them.
Out of 92 findings across every scanner that examined this package. 3 of 8 findings rated critical by their scanner were among the dismissals.
Not reviewed is not the same as clean. Those 8 findings stand exactly as their scanners reported them, at full severity, as they would have without this review.
Two keys had to turn. The model had to name a reason from a fixed list of six, and a deterministic check of ours — over which files this artifact actually installs — had to independently agree. Anything it could not corroborate is in “not reviewed” above, at full severity.
61 Documentation or example — The match is in documentation or sample code, confirmed by our own check to be outside the installed artifact.
Rules: YARA_prompt_injection_unicode_steganography, FILE_MAGIC_MISMATCH, TOOL_ABUSE_UNDECLARED_NETWORK and 6 more · Files: /repo/samples/cone_spray/animated.gif, /repo/samples/showcase.gif, /repo/docs/mechanics/data_modeling.md and 18 more · reported by cisco-skill-scanner
3 Placeholder value — The matched literal is a placeholder or example — the kind of value a scanner reads as a secret and a human reads as a blank to fill in.
Rules: YARA_credential_harvesting_generic · Files: /repo/nice_vibes/mcp/README.md, /repo/nice_vibes/mcp/SKILL.md · reported by cisco-skill-scanner
1 Agent instruction file — The match is in a file that instructs an AI coding agent — a CLAUDE.md or similar. It is documentation of intent, and our own check confirmed it is not part of what gets installed.
Rules: AAK-AGENT-005 · Files: AGENTS.md · reported by agent-audit-kit
The model could have called any of these a false positive and did not.
4 MANIFEST_MISSING_LICENSE
Files: /repo/SKILL.md, /repo/output/SKILL.md, /repo/nice_vibes/mcp/SKILL.md and 1 more · reported by cisco-skill-scanner
3 COMMAND_INJECTION_OS_SYSTEM
Files: /repo/nice_vibes/mcp/server.py, /repo/scripts/build_samples_gif.py · reported by cisco-skill-scanner
3 DATA_EXFIL_NETWORK_REQUESTS
Files: /repo/nice_vibes/mcp/server.py, /repo/scripts/verify_project_templates.py · reported by cisco-skill-scanner
2 AAK-MCP-SDK-CVE-2026-52869-001
Files: pyproject.toml, poetry.lock · reported by agent-audit-kit
2 RESOURCE_ABUSE_INFINITE_LOOP
Files: /repo/nice_vibes/cli.py · reported by cisco-skill-scanner
1 AAK-DNS-REBIND-002
Files: pyproject.toml · reported by agent-audit-kit
1 AAK-MCP-SSRF-001
Files: scripts/verify_project_templates.py · reported by agent-audit-kit
1 AAK-OAUTH-3P-001
Files: pyproject.toml · reported by agent-audit-kit
1 EXCESSIVE_FILE_COUNT
Files: /repo/. · reported by cisco-skill-scanner
1 HIDDEN_DATA_FILE
Files: /repo/.windsurf/rules/rules.md · reported by cisco-skill-scanner
8 The model called these false positives and our own check did not agree, so the dismissal was refused.
Rules: SECRET_GITHUB_TOKEN, TOOL_ABUSE_UNDECLARED_NETWORK, AAK-MCP-ATTEST-001 and 1 more · Files: /repo/nice_vibes/mcp/README.md, /repo/nice_vibes/mcp/SKILL.md, /repo/nice_vibes/mcp/repo/nice_vibes/mcp/README.md and 4 more · reported by cisco-skill-scanner, agent-audit-kit
Critical findings include shell execution and exposed credentials.
The artifact contains critical findings related to shell execution and credential exposure. Command injection vulnerabilities in `/repo/nice_vibes/mcp/server.py` and `/repo/scripts/build_samples_gif.py` could allow for shell execution. A GitHub token is exposed in `/repo/nice_vibes/mcp/README.md` and `/repo/nice_vibes/mcp/SKILL.md`, which could lead to credential access. High-severity findings also indicate potential infinite loops and a known vulnerability in an SDK dependency.
Concern level assessed by the review: Critical. This is the review's scale, not the registry badge.
Reviewed by gemini-2.5-flash. The model sees the findings, not your code, and cannot change a score.