Flux-Frontiers
Official@flux-frontiers · United States of America
Specializing in Machine Learning, Structural Biophysics, Simulation and AI
Agent Skills by Flux-Frontiers
Showing 9 vetted skills indexed across 1 GitHub repositories.
Flux-Frontierspycodekg
Convert a Python codebase into a hybrid SQLite and LanceDB knowledge graph.
Flux-Frontiersnew-kg-module
Scaffold a KGModule package with extractor, adapter, and test templates.
Flux-Frontierskgrag-usage
Query multiple registered knowledge graphs with kind-based filtering for code, docs, and metadata.
Flux-FrontiersPyCodeKG Thorough Repository Analysis Skill
Analyze Python repository architecture and generate Markdown and JSON reports.
Flux-Frontiersdocumentation-lookup
Retrieves authoritative, version-aware documentation for named frameworks and APIs to craft code examples and implementation guidance.
Flux-Frontierspublish
Automate a deterministic release workflow for publishing code-kg to PyPI with Poetry.
Flux-Frontiersskill-creator
Create and update Claude Skill packages with correct SKILL.md structure and metadata.
Flux-Frontierskgrag
Query multiple knowledge graphs via a unified registry and CLI.
Flux-Frontiersdockg
Build a deterministic hybrid knowledge graph from .md and .txt corpora.
Frequently Asked Questions About Flux-Frontiers
FAQPage SchemaWhat specific tasks can be performed using Flux-Frontiers?▼
Flux-Frontiers enables the conversion of source code into hybrid SQLite and LanceDB knowledge graphs, the generation of repository architectural reports, and the retrieval of version-aware documentation. It also supports scaffolding new knowledge graph modules and managing deterministic package releases for distribution.
Which technical personas benefit from these capabilities?▼
These capabilities are designed for software architects, biophysics researchers, and data engineers who require structured indexing of large codebases and documentation corpora. It is particularly useful for teams managing complex, multi-repository projects that demand high-fidelity retrieval and architectural transparency.
What are the prerequisites for implementing these knowledge graph solutions?▼
Implementation requires a local environment configured for SQLite and LanceDB, along with Poetry for package management. Users must have their target source code or documentation corpora prepared in Python, Markdown, or text formats to facilitate the extraction and indexing processes.