semvec/pypi-packages huggingface dataset:
ppecli 0.1.0
Python Ping with Emoji

# ppecli (py-ping-emoji)

`cping` – a cross‑platform CLI to **ping any host/IP and get an emoji latency badge**.

```bash
cping 8.8.8.8              # 🟢 Fast: 11 ms
cping google.com           # 🟢 Fast: 7 ms
cping --http google.com    # 🟡 Medium: 120 ms
```

> ICMP by default · HTTP latency with `--http` · Works on macOS, Linux, Windows

---

## 🚀 Installation

```bash
pip install ppecli
```

Creates the command **`cping`** in your PATH.

---

## 🌟 Usage

```bash
cping <host|ip> [--http]
```

| Example                    | What happens                           |
| -------------------------- | -------------------------------------- |
| `cping 8.8.8.8`            | ICMP‑ping → emoji & RTT                |
| `cping google.com`         | ICMP‑ping of resolved IP               |
| `cping https://github.com` | Same, scheme stripped automatically    |
| `cping --http example.com` | Full HTTPS request latency measurement |

Exit code is **0** on success, **1** on error — handy for CI scripts.

---

## 🛠️ Features

* **Emoji output** — 🟢 <100 ms · 🟡 100‑500 ms · 🔴 >500 ms or no reply
* **Auto‑scheme handling** — give `google.com`, `https://google.com`, or raw IP
* **Cross‑platform** — macOS, Linux, Windows (`ping` flags auto‑detected)
* **HTTP mode** — measure real web latency with TLS handshake (`--http`)
* **Tiny footprint** — stdlib + `requests` only

---

## 📦 Library API

```python
from ppecli import icmp_ping, http_ping, emoji_latency

ms = icmp_ping("8.8.8.8")
print(emoji_latency(ms))
```

---

## 📝 License

MIT — see [LICENSE](https://github.com/froas-dev/ppe-cli/tree/master) for details.

---

Made with ❤️ by [Froas](https://github.com/Froas)
jsonstruct-cli 0.1.22
Format deeply nested JSON and decode JWTs
Requires Python: >=3.7

# jsonstruct
A CLI tool to pretty-print JSON and decode JWTs. Supports JSON or Python-style input.

## Install
```bash
pip install .
```

## Usage
```bash
jsonstruct --file input.json --decode-jwt
jsonstruct --json '{"token": "..."}'
jsonstruct --version
```
nfactorial 0.1.30
A distributed task queue for building reliable multi-agent systems.
Requires Python: >=3.9

# nFactorial

**Build distributed agents that spawn more distributed sub-agents.**

nFactorial is a distributed task queue for building reliable multi-agent systems. It makes the following trivial to implement:

* **Agent Reliability**: Automatic retries, backoff strategies, and recovery of dropped tasks from crashed workers.
* **In-flight Task Management**: Cancel, steer, and monitor running tasks. 
* **Spawning Sub Agents**: Having an agent spawn multiple sub agents and wait for their completion before continuing.
* **Deferred Tools**: Pause the agent while it waits for long running tools to complete externally or wait for user approval before continuing.
* **Observability**: Built-in metrics dashboard and comprehensive logging

![Dashboard](https://raw.githubusercontent.com/ricardo-agz/nfactorial/main/docs/static/img/dashboard.png)


## Installation

```bash
pip install nfactorial
```

## Quick Start

```python
from factorial import Agent, Orchestrator, AgentWorkerConfig, gpt_41


def get_weather(location: str) -> str:
    return f"The weather in {location} is sunny and 72°F"


agent = Agent(
    instructions="You help users get weather information.",
    model=gpt_41,
    tools=[get_weather],
)

# Create orchestrator
orchestrator = Orchestrator(
    redis_host="localhost",
    redis_port=6379,
    redis_db=0,
    redis_max_connections=50,
)
orchestrator.register_runner(
    agent=agent, agent_worker_config=AgentWorkerConfig(workers=1)
)

# Run the system
if __name__ == "__main__":
    orchestrator.run()

```


## Usage Examples:

### IDE Coding Agent

`/docs/examples/code_agent`

![Dashboard](https://raw.githubusercontent.com/ricardo-agz/nfactorial/main/docs/static/img/code-agent.png)

### Multi-Agent

`/docs/examples/multi_agent`

![Dashboard](https://raw.githubusercontent.com/ricardo-agz/nfactorial/main/docs/static/img/multi-agent-progress.png)

### Deliberations.ai
[Check it out](https://www.deliberations.ai/)

![Dashboard](https://raw.githubusercontent.com/ricardo-agz/nfactorial/main/docs/static/img/deliberations-demo-small.gif)
sqldq 0.2.6
Data Quality checks using SQL
Requires Python: >=3.10

# SQLDQ

`SQLDQ` is a Data Quality Testing library that keeps it simple and flexible.

Simply use SQL to define your checks.

### Support

You can run data quality checks on:

- In-memory:
    - Pandas (`.from_duckdb`)
    - Polars (`.from_duckdb`)
    - Pyspark (`.from_pyspark`), requires `pyspark>=3.4.0`
- Remotely, only results are collected:
    - Postgres (`.from_postgresql`)
    - AWS Athena (`.from_athena`)
- Everything else supported by DuckDB


### Installation

`pip install sqldq` / `uv pip install sqldq`

You also need to install the corresponding libraries for your backend of choice, .e.g. `duckdb` when using `.from_duckdb`. `sqldq`\`s error messages will also inform you about missing dependencies.

### Examples

To see all of its features and examples for all supported backends, see the `demo` folder.

The basic workflow is as follows:

```python
from sqldq import SQLDQ
import duckdb
import polars as pl

# Sample data
df_users = pl.DataFrame({
    "user_id": [1, 2, 2],          # Duplicate user_id=2
    "age": [25, 150, 45],          # Age 150 is an unplausible outlier
    "email": ["user1@example.com",
              "user2@example.com",
              "invalid-email"],    # Invalid email
})

# Connect via DuckDB
con = duckdb.connect()
con.register("users", df_users)

dq = SQLDQ.from_duckdb(connection=con)

# Define DQ checks
dq = (
    dq.add_check(
        name="check_duplicate_user_id",
        failure_rows_query="""
            WITH duplicate_users AS (
                SELECT user_id, COUNT(*) AS count
                FROM users
                GROUP BY user_id
            )
                SELECT user_id
                FROM duplicate_users
                WHERE count > 1""")
    .add_check(
        name="check_invalid_email",
        failure_rows_query="""
            SELECT user_id
            FROM users
            WHERE email NOT LIKE '%_@__%.__%'
        """)
    .add_check(
        name="check_age_outlier",
        failure_rows_query="""
            SELECT user_id, age
            FROM users
            WHERE age NOT BETWEEN 0 AND 120"""))

# Run checks
result = dq.execute()

# Report on results
report = result.report(include_rows=True,
                       include_summary_header=True,
                       fail_only=True)
print(report)

# Control flow
if result.has_failures():
    print("Checks failed. here we can take custom actions.")
```


# Development

```bash
# open devcontainer
uv sync
source .venv/bin/activate
make check
```