GitHub CLI extension to list and delete GitHub Actions artifacts based on new retention policy
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Updated
Feb 25, 2025 - Shell
GitHub CLI extension to list and delete GitHub Actions artifacts based on new retention policy
AlgoSoft Artifact — GitHub Action to upload, download or delete build artifacts in your own artifact service
A GitHub action for uploading build artifacts to Buildstash.
GitHub Actions in der Praxis: Matrix-Builds, Caching und Artefakte – schlanke Vorlage für zuverlässige CI/CD-Pipelines.
Language-agnostic, LLM-assisted development system treating AI as a compiler generating structured plan artifacts. Enforces TDD, requires human approval at irreversible boundaries, maintains complete audit trails. CLI, TUI, and Web interfaces included.
Deterministic replay-integrity validation for compressed operational agent traces.
Public proof publication surface for Verifrax: canonical proof artifacts, disclosures, and verification references.
MCP-native desktop library for AI-generated artifacts. Your model lists, reads, writes and re-renders them directly in a local-first workspace that keeps everything rendering, versioned and editable.
Your private publishing plane — self-hosted, internal-first HTML/file publishing with a REST API
Preflight artifact names and producer-consumer ordering in GitHub Actions.
Unofficial build artifacts for gromacs/gromacs
This repository contains all the assignments I worked on as a part of the seminar for the course "Medical Visualization" from my Master's degree.
🧬 Master Artifacts Test Repo– Status: Active(Private R&D) — internal prototype for model-driven development, tracking and refining downstream project artifacts across iterative cycles 🧠🗂️🔬
Local Rust evidence-boundary utility for manifesting artifacts with hashes, scope, limitations, and review notes.
DeepSeek Harness plugin: let agents publish HTML pages and Markdown documents as shareable artifacts on GitHub Pages
Publish once, version forever, share with links you control.
Provide a lightweight C++ string utility focused on efficiency, small memory footprint, and predictable behavior for embedded and performance-critical uses.
An end-to-end machine learning pipeline for wholesale customer channel prediction, including data ingestion, preprocessing, model training, artifact saving, and inference.
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