Loading the registry…
Causal inference engine for deep learning training dynamics
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.
4 critical findings reported by cisco-skill-scanner, 3 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 -- neuraldbg
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
73 findings · 23.3s
v0.3.74
10 findings · 20.8s
v0.1.0
2 findings · 4.2s
v0.1.0
5 findings · 4.2s
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 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 90 findings across every scanner that examined this package. None of the 4 findings rated critical by their scanner were dismissed.
Not reviewed is not the same as clean. Those 40 findings stand exactly as their scanners reported them, at full severity, as they would have without this review.
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.
8 DATA_EXFIL_NETWORK_REQUESTS
Files: /repo/scrape_paper_archs.py, /repo/scripts/check_prs.py, /repo/scripts/find_bugs.py and 1 more · reported by cisco-skill-scanner
7 HIDDEN_DATA_FILE
Files: /repo/.dvcignore, /repo/.githooks/post-merge, /repo/.githooks/post-checkout and 4 more · reported by cisco-skill-scanner
5 MANIFEST_MISSING_LICENSE
Files: /repo/SKILL.md, /repo/arxiv/SKILL.md, /repo/benchmark_public/SKILL.md and 2 more · reported by cisco-skill-scanner
5 opt.nerlo-rules.nerlo-install-run-bundled-artifact
Files: /repo/rules/rule_07_08_09_10_11_12_13_15_16_17.md, /repo/rules/rule_39_41_44_45_54_55_60_61_62_63_74_76.md, /repo/rules/rule_87_ownership_intelligence.md · reported by nerlo-install-instruction
5 SOCIAL_ENG_VAGUE_DESCRIPTION
Files: /repo/SKILL.md, /repo/arxiv/SKILL.md, /repo/benchmark_public/SKILL.md and 2 more · reported by cisco-skill-scanner
3 BINARY_FILE_DETECTED
Files: /repo/sweep_final.txt, /repo/sweep_output.txt, /repo/sweep_v2.txt · reported by cisco-skill-scanner
3 UNANALYZABLE_BINARY
Files: /repo/sweep_final.txt, /repo/sweep_v2.txt, /repo/sweep_output.txt · reported by cisco-skill-scanner
2 COMMAND_INJECTION_EVAL
Files: /repo/mcp_server.py, /repo/e2e_pipeline.py · reported by cisco-skill-scanner
2 FILE_MAGIC_MISMATCH
Files: /repo/benchmark_public/__init__.py · reported by cisco-skill-scanner
1 AAK-AGENT-005
Files: .cursorrules · reported by agent-audit-kit
1 AAK-METIS-SCORING-SINK-001
Files: neuraldbg/rl_detector.py · reported by agent-audit-kit
1 AAK-SUPPLY-004
Files: pyproject.toml · reported by agent-audit-kit
1 COMMAND_INJECTION_SHELL_TRUE
Files: /repo/scripts/ci_agent.py · reported by cisco-skill-scanner
1 DATA_EXFIL_JS_NETWORK
Files: /repo/dashboard/app.js · reported by cisco-skill-scanner
1 DATA_EXFIL_SOCKET_CONNECT
Files: /repo/scripts/live_status.py · reported by cisco-skill-scanner
1 EXCESSIVE_FILE_COUNT
Files: /repo/. · reported by cisco-skill-scanner
1 MANIFEST_INVALID_NAME
Files: /repo/benchmark_public/SKILL.md · reported by cisco-skill-scanner
1 opt.nerlo-rules.nerlo-py-dynamic-exec
Files: /repo/scripts/ci_agent.py · reported by nerlo-behavioral
1 opt.nerlo-rules.nerlo-py-secret-to-network-egress
Files: /repo/infrastructure/scripts/linear_sync.py · reported by nerlo-behavioral
40 The model's answer could not be read.
Rules: COMMAND_INJECTION_EVAL, AAK-GHA-IMMUTABLE-001, AAK-AGENT-005 and 5 more · Files: /repo/examples/repro_pytorch_173334.py, .github/workflows/codacy.yml, .github/workflows/publish.yml and 19 more · reported by cisco-skill-scanner, agent-audit-kit
Critical risks: shell execution and data egress
The artifact contains critical findings related to shell execution via `eval` in `e2e_pipeline.py`, `repro_pytorch_173334.py`, and `mcp_server.py`, and `shell=True` in `ci_agent.py`. It also exhibits critical network egress capabilities from `live_status.py` and high-severity credential egress from `linear_sync.py`. Dynamic code execution is present in `ci_agent.py`, and multiple medium-severity network requests are observed across the codebase.
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.