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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.
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 -- mmarcus006/fdd_qc_system
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
11 findings · 19.2s
v0.3.74
1 finding · 15.2s
v0.1.0
2 findings · 7.4s
v0.1.0
0 findings · 7.4s
v1.4.0
None of the entry points this scanner reads were found in this artifact. What each scanner covers
0 findings · 0.3s
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 · 1.1s
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 21, 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 14 findings across every scanner that examined this package.
These findings are in files this artifact installs and are not placeholder values. No false-positive basis can be corroborated for such a finding, so this review never dismisses one; they were not sent to the model and stand at their scanner's severity.
2 DATA_EXFIL_NETWORK_REQUESTS
Files: /repo/enhanced_verification.py, /repo/llm_optimizer.py · reported by cisco-skill-scanner
2 MANIFEST_MISSING_LICENSE
Files: /repo/SKILL.md, /repo/document_management/SKILL.md · reported by cisco-skill-scanner
2 opt.nerlo-rules.nerlo-py-secret-to-network-egress
Files: /repo/enhanced_verification.py, /repo/llm_optimizer.py · reported by nerlo-behavioral
2 SOCIAL_ENG_VAGUE_DESCRIPTION
Files: /repo/SKILL.md, /repo/document_management/SKILL.md · reported by cisco-skill-scanner
1 AAK-SECRET-004
Files: llm_optimizer.py · reported by agent-audit-kit
1 COMMAND_INJECTION_SHELL_TRUE
Files: /repo/advanced_nlp.py · reported by cisco-skill-scanner
1 EXCESSIVE_FILE_COUNT
Files: /repo/. · reported by cisco-skill-scanner
1 HIDDEN_DATA_FILE
Files: /repo/.idx/dev.nix · reported by cisco-skill-scanner
1 MANIFEST_INVALID_NAME
Files: /repo/document_management/SKILL.md · reported by cisco-skill-scanner
1 TOOL_ABUSE_UNDECLARED_NETWORK
Files: /repo/repo/README.md · reported by cisco-skill-scanner
Artifact has credential access, network egress, and shell execution capabilities.
The artifact can access credentials in `llm_optimizer.py` and perform network egress, potentially transmitting sensitive data from `enhanced_verification.py` and `llm_optimizer.py`. It also includes a command injection vulnerability in `advanced_nlp.py` that allows shell execution. Further network requests are observed, indicating data exfiltration capabilities.
Concern level assessed by the review: High. 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.