Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
source: struct<git: struct<url: string, head: string, branch: string, sha_verified_from: string, previous_up (... 16 chars omitted)
child 0, git: struct<url: string, head: string, branch: string, sha_verified_from: string, previous_upstream: stri (... 3 chars omitted)
child 0, url: string
child 1, head: string
child 2, branch: string
child 3, sha_verified_from: string
child 4, previous_upstream: string
total: int64
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
child 0, aggregate_rule: string
child 1, excluded: string
child 2, reproduce: string
child 3, thresholds_note: string
sbom_written: bool
python: string
schema: string
passed: int64
platform: string
version: string
failed_ids: list<item: null>
child 0, item: null
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
child 0, max_files: int64
...
cation_environment_pinned: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 62, criteria_missing: list<item: null>
child 0, item: null
child 63, criteria_satisfied: int64
child 64, criteria_total: int64
child 65, framework_controls_verified: int64
child 66, ladder: string
child 67, observed_level: int64
child 68, source: string
child 69, trL9_claimed: bool
child 70, trL9_reason: string
child 1, id: string
child 2, ok: bool
child 3, status: string
child 4, title: string
root: string
failed: int64
all_passed: bool
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
e('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'url': Value('string'), 'head': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string'), 'previous_upstream': Value('string')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
source: struct<git: struct<url: string, head: string, branch: string, sha_verified_from: string, previous_up (... 16 chars omitted)
child 0, git: struct<url: string, head: string, branch: string, sha_verified_from: string, previous_upstream: stri (... 3 chars omitted)
child 0, url: string
child 1, head: string
child 2, branch: string
child 3, sha_verified_from: string
child 4, previous_upstream: string
total: int64
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
child 0, aggregate_rule: string
child 1, excluded: string
child 2, reproduce: string
child 3, thresholds_note: string
sbom_written: bool
python: string
schema: string
passed: int64
platform: string
version: string
failed_ids: list<item: null>
child 0, item: null
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
child 0, max_files: int64
...
cation_environment_pinned: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 62, criteria_missing: list<item: null>
child 0, item: null
child 63, criteria_satisfied: int64
child 64, criteria_total: int64
child 65, framework_controls_verified: int64
child 66, ladder: string
child 67, observed_level: int64
child 68, source: string
child 69, trL9_claimed: bool
child 70, trL9_reason: string
child 1, id: string
child 2, ok: bool
child 3, status: string
child 4, title: string
root: string
failed: int64
all_passed: bool
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
e('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'url': Value('string'), 'head': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string'), 'previous_upstream': Value('string')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
ONURBS
DeepCamera — Open-Source AI Camera Skills Platform DeepCamera's open-source skills give your cameras AI — VLM scene analysis, object detection, person re-identification, all running locally with models like Qwen, DeepSeek, SmolVLM, and LLaVA. Built on proven facial recognition, RE-ID, fall detection, and CCTV/NVR surveillance monitoring, the skill catalog extends these machine learning capabilities with modern AI. All inference runs locally for maximum privacy.
Category: SURVEILLANCE_CAMERA · Ecosystem: unknown · Vendored upstream: https://github.com/SharpAI/DeepCamera · Pinned commit: 933dcc7c90226cd1f92dc6ea7cee3b3c94790ad0 · Vendor: Anticloud FZ LLE
Note: this package vendors
https://github.com/SharpAI/DeepCameraas the implementation for theONURBSslot. The vendored repository name differs from the package name; the pin above is the exact commit vendored here.
What This Project Is
DeepCamera — Open-Source AI Camera Skills Platform DeepCamera's open-source skills give your cameras AI — VLM scene analysis, object detection, person re-identification, all running locally with models like Qwen, DeepSeek, SmolVLM, and LLaVA. Built on proven facial recognition, RE-ID, fall detection, and CCTV/NVR surveillance monitoring, the skill catalog extends these machine learning capabilities with modern AI. All inference runs locally for maximum privacy.
This is the upstream project, vendored at the pinned commit above and packaged offline-first. The 12 improvements Anticloud adds live in anticloud/ and are described under Anticloud Overlay below. Every fact in this file traces to a file in this directory.
Installation
Skills are installed by an autonomous LLM deployment agent — not by brittle shell scripts. When you click "Install" in Aegis, a focused mini-agent session reads the skill's SKILL.md manifest and figures out what to do:
- Probe — reads
SKILL.md,requirements.txt, andpackage.jsonto understand what the skill needs - Detect hardware — checks for NVIDIA (CUDA), AMD (ROCm), Apple Silicon (MPS), Intel (OpenVINO), or CPU-only
- Install — runs the right commands (
pip install,npm install, system packages) with the correct backend-specific dependencies - Verify — runs a smoke test to confirm the skill loads before marking it complete
- Determine launch command — figures out the exact
run_commandto start the skill and saves it to the registry
This means community-contributed skills don't need a bespoke installer — the LLM reads the manifest and adapts to whatever hardware you have. If something fails, it reads the error output and tries to fix it autonomously.
Usage
The easiest way to run DeepCamera's AI skills. Aegis connects everything — cameras, models, skills, and you.
- 📷 Connect cameras in seconds — add RTSP/ONVIF cameras, webcams, or iPhone cameras for a quick test
- 🤖 Built-in local LLM & VLM — llama-server included, no separate setup needed
- 📦 One-click skill deployment — install skills from the catalog with AI-assisted troubleshooting
- 🔽 One-click HuggingFace downloads — browse and run Qwen, DeepSeek, SmolVLM, LLaVA, MiniCPM-V
- 📊 Find the best VLM for your machine — benchmark models on your own hardware with HomeSec-Bench
- 💬 Talk to your guard — via Telegram, Discord, or Slack. Ask what happened, tell it what to watch for, get AI-reasoned answers with footage.
Anticloud Overlay
The 12 improvements live under anticloud/. Run the check suite from this directory:
python anticloud/bench/runner.py --cwd anticloud
Benchmark Results
| Result | Status | Evidence |
|---|---|---|
| 01_loc_files Code size and file count | PASS | {"ceilings": {"max_files": 120, "max_lines": 20000, "min_lines": 2000}, "code_li |
| 02_licence Licence posture (A/B/C policy) | PASS | {"a_licences": ["Apache-2.0", "BSD-2-Clause", "BSD-3-Clause", "ISC", "MIT", "MPL |
| 03_dependency_scan Dependency scan (hash-pinned lock) | PASS | {"command": "anticloud-ref deps-verify requirements.lock", "declared_in_pyprojec |
| 04_sbom_cyclonedx SBOM (CycloneDX 1.5) | PASS | {"command": "python -c "import json;d=json.load(open('sbom.cdx.json'));print(d[ |
| 05_git_health Git health | PASS | {"branch": "main", "command": "git status --porcelain && git log --oneline -1 && |
| 06_owasp_llm_top10 OWASP Top 10 for LLM Applications | PASS | {"authority": "OWASP Foundation", "command": "python tools/run_bench.py --only o |
| 07_owasp_top10 OWASP Top 10 (2021) | PASS | {"authority": "OWASP Foundation", "command": "python tools/run_bench.py --only o |
| 08_soc2_type2 SOC 2 Type II readiness | PASS | {"authority": "AICPA", "command": "python tools/run_bench.py --only soc2", "cont |
| 09_nist_ai_rmf NIST AI Risk Management Framework | PASS | {"authority": "NIST AI RMF", "command": "python tools/run_bench.py --only nist", |
| 10_nist_sp_800_53 NIST SP 800-53 Rev. 5 | PASS | {"authority": "NIST", "command": "python tools/run_bench.py --only nist", "contr |
| 11_nist_csf NIST Cybersecurity Framework 2.0 | PASS | {"authority": "NIST", "command": "python tools/run_bench.py --only nist", "contr |
| 12_fedramp FedRAMP Rev. 5 | PASS | {"authority": "FedRAMP", "command": "python tools/run_bench.py --only fedramp", |
| 13_pci_dss PCI DSS v4.0.1 | PASS | {"authority": "PCI SSC", "command": "python tools/run_bench.py --only pci", "con |
| 14_iso_27001 ISO/IEC 27001:2022 | PASS | {"authority": "ISO/IEC", "command": "python tools/run_bench.py --only iso", "con |
| 15_mitre_attack MITRE ATT&CK v16 | PASS | {"authority": "MITRE", "command": "python tools/run_bench.py --only mitre", "con |
| 16_ml_trl ML Technology Readiness Level 8 | PASS | {"claimed_level": 8, "command": "python tools/run_bench.py --only 16 && cat docs |
Suite: 16/16 passed · platform win32 · python 3.12.10 · method {'aggregate_rule': 'all_passed == AND over benchmarks[*].ok; a FAIL forces the aggregate FAIL', 'excluded': 'no AI/LLM-latency check is included or required', 'reproduce': 'python tools/run_bench.py && python -m pytest -q', 'thresholds_note': 'no threshold is relaxed by the runner; ceilings live in runner.py and are asserted independently in tests/test_project_metadata.py'}
Licence
The upstream licence is preserved in LICENSE and THIRD_PARTY_LICENSES/. The Anticloud overlay is licensed under Anticommons 0.1.0 (ANTICOMMONS_LICENSE.md).
Verification
SHA3-256 of BENCH.json:
9a3a519b6d5404ef079d5997d657934bd855cb27b812fdc83ba8494f745dc566
Recompute the hash above and compare — any drift means the package has been tampered with and the seal is invalid. This is a self-assessment; no external audit has been performed and a SOC 2 / NIST independent audit is committed post-funding.
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