semvec/pypi-packages huggingface dataset:
dhub-cli 0.6.7
The AI Skill Manager for Data Science Agents
Requires Python: >=3.11

# dhub-cli: The AI Skill Manager for Data Science Agents

**Decision Hub** is a CLI-first registry for publishing, discovering, and installing *Skills* — modular packages of code and prompts that AI coding agents (Claude, Cursor, Codex, Gemini, OpenCode) can use.

## Why Decision Hub?

**Agents that extend themselves.** Install Decision Hub as a skill into any supported agent, and the agent can discover new skills in natural language — then install and use them mid-conversation without human intervention.

**Publish from anywhere.** Point `dhub publish` at a local directory or a GitHub repo URL and every valid `SKILL.md` inside is discovered, versioned, and published.

**Private skills for your team.** Skills can be scoped to your GitHub organization so proprietary tooling stays internal.

**Install once, use everywhere.** A single `dhub install` symlinks a skill into every agent's skill directory — Claude, Cursor, Codex, Gemini, OpenCode. No duplication, no per-agent setup.

**Security gauntlet.** Every publish is scanned for dangerous patterns. Skills get a trust grade (A/B/C/F) before they reach the registry.

**Automated evals in sandboxes.** Skills ship with eval cases that run on publish in isolated sandboxes, scored by an LLM judge.

**Executable skills with SKILL.md.** Builds on the [Agent Skills spec](https://agentskills.io/specification) with `runtime` and `evals` blocks — skills are runnable programs, not just static prompts.

## Installation

```bash
# Via uv (recommended)
uv tool install dhub-cli

# Via pipx
pipx install dhub-cli
```

## Quick Start

```bash
# 1. Authenticate via GitHub
dhub login

# 2. Search for skills using natural language
dhub ask "analyze A/B test results"

# 3. Install a skill for all your agents
dhub install pymc-labs/causalpy

# 4. Scaffold a new skill
dhub init my-new-skill

# 5. Publish it under your namespace
# (Run this inside the skill directory)
dhub publish .
```

## Supported Agents

Skills are installed as symlinks into each agent's skill directory, making them immediately available:

- **Claude:** `~/.claude/skills`
- **Cursor:** `~/.cursor/skills`
- **Gemini:** `~/.gemini/skills`
- **OpenCode:** `~/.config/opencode/skills`

## Documentation

For full documentation on creating skills, the `SKILL.md` format, and running your own registry server, see the [main repository](https://github.com/lfiaschi/decision-hub).
hwcomponents 1.0.95
Hardware Component Area, Energy, Latency, and Leak Power Models
Requires Python: >=3.12

# HWComponents
The HWComponents (Hardware Components) package, part of the
[CiMLoop](https://github.com/mit-emze/cimloop) project, provides an interface for the
estimation of area, energy, latency, and leak power of hardware components in hardware
architectures. Key features in HWComponents include:

[Information about the package is available on the hwcomponents website](https://accelergy-project.github.io/hwcomponents/).

## Citing HWComponents

If you use this package in your work, please cite the CiMLoop project:

```bibtex
@INPROCEEDINGS{cimloop,
  author={Andrulis, Tanner and Emer, Joel S. and Sze, Vivienne},
  booktitle={2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
  title={CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool},
  year={2024},
  volume={},
  number={},
  pages={10-23},
  keywords={Performance evaluation;Accuracy;Computational modeling;Computer architecture;Artificial neural networks;In-memory computing;Data models;Compute-In-Memory;Processing-In-Memory;Analog;Deep Neural Networks;Systems;Hardware;Modeling;Open-Source},
  doi={10.1109/ISPASS61541.2024.00012}
}
```
hwcomponents-library 1.0.46
A library of hardware components for energy estimation.
Requires Python: >=3.12
Keywords: hardware, components, energy, estimation

# HWComponents-Library
HWComponents-Library contains a library of components from published works. It is
intended to be used to rapidly model prior works and to provide a common set of
components for comparison.

These models are for use with the HWComponents package, found at
https://accelergy-project.github.io/hwcomponents/.

## Installation

Install from PyPI:

```bash
pip install hwcomponents-library

# Check that the installation is successful
hwc --list | grep adder
```

## Contributing: Adding or Updating Numbers from Your Work
We would be happy to update these models given a pull request. Please see
"Creating Library Entries" and format your entries to match the existing
entries. If you have any questions, we would be happy to help.

Note that we will only accept entries that are published or backed by public
data. Citations are required for all entries.

## Citation

If you use this library in your work, please cite the following:

```bibtex
@inproceedings{cimloop,
  author={Andrulis, Tanner and Emer, Joel S. and Sze, Vivienne},
  booktitle={2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}, 
  title={{CiMLoop}: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool}, 
  year={2024},
  volume={},
  number={},
  pages={10-23},
  keywords={Compute-In-Memory;Processing-In-Memory;Analog;Deep Neural Networks;Systems;Hardware;Modeling;Open-Source},
  doi={10.1109/ISPASS61541.2024.00012}}
}
```