semvec/pypi-packages huggingface dataset:
tencoin-core 0.4.4
Official Python library for Tencoin – HD wallets, SegWit, BIP39, RPC client
License: MIT
Requires Python: >=3.8
Keywords: tencoin, blockchain, cryptocurrency, hd-wallet, bip39, segwit, rpc

# Tencoin Core

Official Python library for Tencoin – HD wallets, transactions, and RPC client.

## Features

- **HD Wallets**: BIP-39 (seed phrases) + BIP-84 (SegWit native addresses)
- **Full BIP-32 Support**: Standard xprv/xpub extended keys with master/account-level derivation
- **Watch-Only Wallets**: Create wallets directly from xpub (no seed/private key required)
- **Multiple Address Types**: 
  - **SegWit v0** (P2WPKH): Native SegWit addresses (`tc1q...`)
  - **Legacy P2PKH**: Pay-to-pubkey-hash addresses (`T...`)
  - **Legacy P2SH**: Pay-to-script-hash addresses (`M...`)
  - **P2WSH**: SegWit script addresses (`tc1q...` with 32-byte program)
- **Custom Scripts**: Support for multisig and custom redeem/witness scripts
- **Transaction Signing**: Automatic detection and signing for SegWit (BIP-143) and Legacy (P2PKH/P2SH) inputs
پش 49- **Key Derivation**:
  - **BIP-84 (P2WPKH)**: `m/84'/5353'/0'/0/0` for native SegWit addresses
  - **BIP-44 (P2PKH)**: `m/44'/5353'/0'/0/0` for legacy P2PKH addresses
  - Additional custom BIP-32 paths supported via the BIP-32 API
- **Seed Phrases**: 12–24 word English mnemonics (full BIP-39 compliance)
- **Wallet Recovery**: From mnemonic phrases – compatible with standard BIP-39 implementations
- **Full BIP-39 Compatibility**: Mnemonics generated by other standard libraries (e.g., bip-utils, Electrum, MetaMask, Ian Coleman's tool) are validated and recovered
- **Secure Seed Generation**: PBKDF2-HMAC-SHA512 with official 2048-word English wordlist

## Installation

```bash
pip install tencoin-core
haiku.skills 0.4.2
Skill-powered AI agents implementing the Agent Skills specification with pydantic-ai
License: MIT
Requires Python: >=3.12
Keywords: agent-skills, ai-agents, ai-tools, mcp, pydantic-ai, skill-discovery, sub-agent

# haiku.skills

[![Tests](https://github.com/ggozad/haiku.skills/actions/workflows/test.yml/badge.svg)](https://github.com/ggozad/haiku.skills/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/ggozad/haiku.skills/graph/badge.svg)](https://codecov.io/gh/ggozad/haiku.skills)

Skill-powered AI agents implementing the [Agent Skills specification](https://agentskills.io/specification) with [pydantic-ai](https://ai.pydantic.dev/).

## How it works

`SkillToolset` is a pydantic-ai `FunctionToolset` that you attach to your own agent. It exposes a single `execute_skill` tool. When the agent calls it, a **focused sub-agent** spins up with only that skill's instructions and tools — then returns the result. The main agent never sees the skill's internal tools, so its tool space stays clean no matter how many skills you load.

This sub-agent architecture means each skill runs in isolation with its own system prompt, tools, and token budget. Skills don't interfere with each other, tool descriptions don't compete for attention, and failures in one skill can't confuse another.

## Features

- **Sub-agent execution** — Each skill runs in its own agent with dedicated instructions and tools
- **Skill discovery** — Scan filesystem paths for [SKILL.md](https://agentskills.io/specification) directories or load from Python entrypoints
- **In-process tools** — Attach pydantic-ai `Tool` functions or `AbstractToolset` instances to skills
- **Per-skill state** — Skills declare a Pydantic state model and namespace; state is passed to tools via `RunContext` and tracked on the toolset
- **AG-UI protocol** — State changes emit `StateDeltaEvent` (JSON Patch), compatible with the [AG-UI protocol](https://docs.ag-ui.com)
- **Script tools** — Python scripts in `scripts/` with a `main()` function, discovered and executed via `uv run`
- **MCP integration** — Wrap any MCP server (stdio, SSE, streamable HTTP) as a skill

## Quick start

```bash
uv add haiku.skills
```

```python
from pathlib import Path
from pydantic_ai import Agent
from haiku.skills import SkillToolset

toolset = SkillToolset(skill_paths=[Path("./skills")])
agent = Agent(
    "anthropic:claude-sonnet-4-5-20250929",
    instructions=toolset.system_prompt,
    toolsets=[toolset],
)

result = await agent.run("Analyze this dataset.")
print(result.output)
```

## Documentation

Full documentation at [ggozad.github.io/haiku.skills](https://ggozad.github.io/haiku.skills/).

## License

MIT