Generate and manage Software Bill of Materials (SBOMs) for the OpenShell project. Covers SBOM generation with Syft, license resolution via public registries, and CSV export for compliance review. Trigger keywords - SBOM, sbom, bill of materials, license audit, license resolution, generate sbom, sbom csv, dependency license, supply chain, license scan.
npx skills add https://github.com/NVIDIA/OpenShell --skill sbom
Generate CycloneDX SBOMs, resolve missing licenses, and export to CSV for compliance review.
The OpenShell SBOM tooling produces CycloneDX JSON SBOMs using Syft, resolves missing or hash-based licenses by querying public registries (crates.io, npm, PyPI), and exports the results to CSV for stakeholder review.
SBOMs are release artifacts only -- they are generated on demand and not committed to the repository. Output lands in deploy/sbom/output/ (gitignored).
mise install has been run (installs Syft and other tools)mise run sbom
This single command chains three stages:
sbom:generate): Syft scans the workspace source tree and produces a CycloneDX JSON SBOMsbom:resolve): Public registry APIs fill in missing or hash-based licenses in the JSONsbom:csv): JSON SBOMs are converted to CSV for reviewOutput directory: deploy/sbom/output/
After running, the user can find:
deploy/sbom/output/*.cdx.json -- full CycloneDX SBOMsdeploy/sbom/output/*.csv -- CSV exports ready for spreadsheet reviewRun stages independently when debugging or iterating:
mise run sbom:generate # Generate JSON SBOMs only (requires Syft)
mise run sbom:resolve # Resolve licenses in existing JSONs (queries APIs)
mise run sbom:csv # Convert existing JSONs to CSV
mise run sbom:check
Reports unresolved licenses without failing. Intended for PR CI as a non-blocking advisory check. Requires that SBOMs have already been generated (mise run sbom:generate).
The Python scripts accept explicit file paths, so they can process SBOMs from any source (e.g., NVIDIA nSpect pipeline output):
uv run python deploy/sbom/resolve_licenses.py /path/to/external-sbom.json
uv run python deploy/sbom/sbom_to_csv.py /path/to/external-sbom.json
The resolver queries these public registries:
| Registry | Package URL prefix | Method |
|----------|-------------------|--------|
| crates.io | pkg:cargo/* | REST API |
| npm | pkg:npm/* | Registry API |
| PyPI | pkg:pypi/* | JSON API |
| Go modules | pkg:golang/* | Known license map (no API) |
| Debian/Ubuntu | pkg:deb/* | Known license map |
Components from private registries (e.g., @openclaw/* npm packages) are not resolved and will appear in the "unresolved" report.
| Pattern | Description |
|---------|-------------|
| deploy/sbom/output/openshell-source-{version}.cdx.json | CycloneDX JSON SBOM |
| deploy/sbom/output/openshell-source-{version}.csv | CSV export (name, version, type, purl, licenses, bom-ref) |
| File | Purpose |
|------|---------|
| deploy/sbom/resolve_licenses.py | License resolution script |
| deploy/sbom/sbom_to_csv.py | JSON-to-CSV converter |
| tasks/sbom.toml | Mise task definitions |
| mise.toml | Syft tool definition (under [tools]) |
| Task | Command |
|------|---------|
| Full pipeline | mise run sbom |
| Generate only | mise run sbom:generate |
| Resolve licenses | mise run sbom:resolve |
| Export CSV | mise run sbom:csv |
| CI license check | mise run sbom:check |
| Process external SBOM | uv run python deploy/sbom/resolve_licenses.py <file> |
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
Retrieve and display GitHub Copilot usage metrics for organizations and enterprises using the GitHub CLI and REST API.
Socratic mentoring for junior developers and AI newcomers. Guides through questions, never answers. Triggers: "help me understand", "explain this code", "I''m stuck", "Im stuck", "I''m confused", "Im confused", "I don''t understand", "I dont understand", "can you teach me", "teach me", "mentor me", "guide me", "what does this error mean", "why doesn''t this work", "why does not this work", "I''m a beginner", "Im a beginner", "I''m learning", "Im learning", "I''m new to this", "Im new to this", "walk me through", "how does this work", "what''s wrong with my code", "what''s wrong", "can you break this down", "ELI5", "step by step", "where do I start", "what am I missing", "newbie here", "junior dev", "first time using", "how do I", "what is", "is this right", "not sure", "need help", "struggling", "show me", "help me debug", "best practice", "too complex", "overwhelmed", "lost", "debug this", "/socratic", "/hint", "/concept", "/pseudocode". Progressive clue systems, teaching techniques, and success metrics.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.
Take nvidia/sbom from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.