audit-packs-core
audit-packs-core contains the foundational models, normalization logic, git diff parser, and source-to-sink data-flow primitives for the audit-packs ecosystem.
⚠️ IMPORTANT: This is a sub-package of the
audit-packscompliance mapping toolkit. It is NOT designed to be run as a standalone CLI tool. If you are looking for the main CLI and GitHub Action scanner execution engine, please install and refer to audit-packs.
📦 Installation
You can install this package via pip if you are writing custom extensions or building on top of the audit-packs data structures:
pip install audit-packs-core
🛠️ API Surface & Modules
| Module | Key API Exports | Description |
|---|---|---|
audit_packs_core.models |
Finding, ControlFinding, ControlStatus, AdjudicationResult |
Core compliance structures and type schemas. |
audit_packs_core.normalize |
sarif_to_findings(), extract_rule_confidences() |
Standardizes engine-specific SARIF JSON outputs into standard Finding instances. |
audit_packs_core.diff |
parse_unified_diff() |
Compares line numbers between refs to filter PR-changed lines. |
audit_packs_core.dataflow |
extract_data_flows(), flow_confidence() |
Tracks source-to-sink data flow dependencies (Python/YAML/HCL). |
📦 Ecosystem Architecture
audit-packs is built as a modular ecosystem consisting of five Python packages:
| Package | PyPI Link | Role | Standalone? |
|---|---|---|---|
audit-packs |
pypi | Main CLI & Action entrypoint | Yes |
audit-packs-core |
pypi | Primitives, diff parsing, normalization | No |
audit-packs-mapping |
pypi | Compliance pack loader & OSCAL exporter | No |
audit-packs-evidence |
pypi | Evidence collectors & heuristic agents | No |
audit-packs-ai |
pypi | LLM consensus & confidence scoring | No |
🔗 Related Resources
- Main GitHub Repository: https://github.com/prakharsingh/audit-packs
- Documentation & Setup: docs/SETUP.md
- Issue Tracker: https://github.com/prakharsingh/audit-packs/issues
📄 License
This library is licensed under the Apache-2.0 License. See the LICENSE file in the main repository for details.
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