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Agent Skill · 效率

bittensor-sdk(bittensor SDK 效率)

bittensor-sdk 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

bittensor-sdk 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 1,778。

总安装 34,979周安装 1,415GitHub Stars 1下载 10,980核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.2

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:bittensor-sdk(bittensor SDK 效率)
来源仓库:https://github.com/taoleeh/bittensor-sdk
安装命令:
openclaw skills install bittensor-sdk
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install bittensor-sdk

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

与 Bittensor 区块链交互以管理钱包、质押 TAO、注册神经元、查询子网/元图、跟踪排放并设置神经元权重。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install bittensor-sdk;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

bittensor-sdk 怎么安装?

在终端执行 openclaw skills install bittensor-sdk。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

bittensor-sdk 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

bittensor-sdk 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

bittensor-sdk 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

bittensor-sdk 用的人多吗?

公开数据是累计安装 34,979 次,近一周安装 1,415 次,下载 10,980 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

bittensor-sdk 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
bittensor-sdk

bittensor SDK 效率 · 本页

34,9791需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
bittensor-sdk
description
Comprehensive Bittensor blockchain interaction skill with wallet management, staking, subnet operations, neuron registration, and emissions tracking. Use for: bittensor operations, subtensor queries, stake/unstake TAO, register neurons, query subnet info, wallet operations, metagraph analysis, emissions tracking, and weight management.
license
MIT
compatibility
Requires Python 3.8+, bittensor>=8.0.0, network access to Bittensor network endpoints
metadata
author
bittensor_quest
version
1.0.0

Bittensor SDK Skill

Comprehensive Bittensor blockchain interaction skill for agents. Enables seamless interaction with the Bittensor decentralized AI network through the Python SDK.

Overview

Bittensor is a decentralized machine learning network where independent subnets compete for TAO token emissions. This skill provides agents with full access to:

  • Wallet Management: Coldkey/hotkey operations, proxy relationships
  • Staking Operations: Stake/unstake TAO, auto-staking, safe staking
  • Subnet Management: Query subnet info, hyperparameters, registration
  • Neuron Operations: Register neurons, query metagraphs, weight management
  • Emissions & Rewards: Track emissions, claim root dividends, reward distribution

Key Concepts

Core Terminology

  • Coldkey: User's main wallet key for transfers and overall wallet management
  • Hotkey: Key used for neuron operations (mining/validation)
  • Netuid: Unique identifier for a subnet (0 = Root Subnet)
  • UID: Unique identifier for a neuron on a specific subnet
  • Metagraph: Complete state of a subnet at a given block
  • TAO: Base network token (1 TAO = 1e9 Rao)
  • Alpha: Subnet-specific token representing staked TAO
  • Rao: Smallest unit of TAO

Network Types

  • finney: Bittensor mainnet
  • test: Bittensor test network
  • local: Locally deployed blockchain

Installation

Prerequisites

pip install bittensor>=8.0.0

Opencode Installation

cp -r skills/bittensor-sdk ~/.opencode/skills/

How It Works

1. Initialization

The skill initializes the Subtensor interface for blockchain interaction:

import bittensor as bt

# Connect to mainnet
subtensor = bt.Subtensor(network="finney")

# Connect to testnet
subtensor = bt.Subtensor(network="test")

# Custom network with fallback endpoints
subtensor = bt.Subtensor(
    network="finney",
    fallback_endpoints=["wss://entrypoint-finney.opentensor.ai:443"],
    retry_forever=True
)

2. Wallet Setup

from bittensor import wallet

# Load existing wallet
wallet = bt.Wallet()

# Create new wallet
wallet = bt.Wallet(name="my_wallet", hotkey="miner1")

# Check balances
coldkey_balance = wallet.coldkey_balance
print(f"Coldkey balance: {coldkey_balance}")

3. Core Operations

Query Subnet Information
# Get all subnet netuids
netuids = subtensor.get_all_subnets_netuid()
print(f"Available subnets: {netuids}")

# Get detailed subnet info
subnet_info = subtensor.get_subnet_info(netuid=1)
print(f"Subnet 1 info: {subnet_info}")
Stake TAO
from bittensor import Balance

# Stake TAO to a hotkey
amount = Balance.from_tao(10.0)  # 10 TAO
result = subtensor.add_stake(
    wallet=wallet,
    netuid=1,
    hotkey_ss58="5Hx...",  # Hotkey SS58 address
    amount=amount,
    safe_staking=True,     # Enable price protection
    allow_partial_stake=True
)
print(f"Stake result: {result}")
Register Neuron
# Burned registration (recycle TAO)
result = subtensor.burned_register(
    wallet=wallet,
    netuid=1
)

# POW registration (computational proof)
result = subtensor.register(
    wallet=wallet,
    netuid=1
)
Query Metagraph
# Get metagraph for a subnet
metagraph = subtensor.metagraph(netuid=1)

print(f"Number of neurons: {metagraph.n}")
print(f"Stake per neuron: {metagraph.S}")
print(f"Rewards: {metagraph.R}")
print(f"Hotkeys: {metagraph.hotkeys}")
Set Weights
import numpy as np

# Validator sets weights for miners
uids = np.array([0, 1, 2, 3, 4])  # Miner UIDs
weights = np.array([0.2, 0.3, 0.2, 0.15, 0.15])  # Normalized weights

result = subtensor.set_weights(
    wallet=validator_wallet,
    netuid=1,
    uids=uids,
    weights=weights,
    wait_for_inclusion=True,
    wait_for_finalization=True
)

Usage Examples

Example 1: Complete Miner Setup

import bittensor as bt
from bittensor import Balance
import numpy as np

# Initialize
subtensor = bt.Subtensor(network="finney")
wallet = bt.Wallet(name="miner_wallet", hotkey="miner1")

# Check balance
balance = subtensor.get_balance(wallet.coldkey.ss58_address)
print(f"Balance: {balance.tao} TAO")

# Register on subnet 1
print("Registering neuron...")
result = subtensor.register(
    wallet=wallet,
    netuid=1,
    wait_for_inclusion=True
)

# Get metagraph info
metagraph = subtensor.metagraph(netuid=1)
print(f"Neurons on subnet 1: {metagraph.n}")

# Check my neuron
my_uid = metagraph.hotkeys.index(wallet.hotkey.ss58_address)
my_neuron = metagraph.neurons[my_uid]
print(f"My UID: {my_uid}")
print(f"My stake: {my_neuron.stake}")
print(f"My emission: {my_neuron.emission}")

Example 2: Validator Operations

import bittensor as bt
from bittensor import Balance
import numpy as np

# Initialize
subtensor = bt.Subtensor(network="finney")
validator_wallet = bt.Wallet(name="validator", hotkey="val1")

# Get metagraph
metagraph = subtensor.metagraph(netuid=1)
print(f"Total miners: {metagraph.n}")

# Calculate weights based on performance
weights = np.zeros(metagraph.n)
for i in range(metagraph.n):
    weights[i] = metagraph.R[i] * 0.7 + metagraph.S[i] * 0.3

# Normalize weights
weights = weights / weights.sum()

# Set weights
result = subtensor.set_weights(
    wallet=validator_wallet,
    netuid=1,
    uids=np.arange(metagraph.n),
    weights=weights,
    wait_for_inclusion=True,
    wait_for_finalization=True
)

print(f"Weights set: {result.success}")

Troubleshooting

Connection Issues

Problem: Unable to connect to network Solution:

# Use fallback endpoints
subtensor = bt.Subtensor(
    network="finney",
    fallback_endpoints=[
        "wss://entrypoint-finney.opentensor.ai:443",
        "wss://finney.opentensor.io:443"
    ],
    retry_forever=True
)

Rate Limiting

Problem: Too many requests error Solution:

import time
time.sleep(1)  # Rate limit delays

Registration Failures

Problem: Registration fails repeatedly Solutions:

  1. Check balance (need > 1 TAO for burn registration)
  2. Verify POW solution is correct
  3. Check network connectivity
  4. Try different registration method

Wallet Issues

Problem: Wallet not found Solution:

# Create new wallet
wallet = bt.Wallet(name="new_wallet", hotkey="new_hotkey")
wallet.create_if_non_existing()

Best Practices

  1. Always close connections: Use subtensor.close() when done
  2. Handle errors gracefully: Use try-except blocks
  3. Implement rate limiting: Don't exceed network limits
  4. Use MEV protection: Enable for large transactions
  5. Monitor emissions: Track network health
  6. Use safe staking: Enable price protection
  7. Keep keys secure: Never expose private keys

Security Considerations

  1. Private keys: Never expose or log private keys
  2. Seed phrases: Store securely, never share
  3. Transaction signing: Always verify before signing
  4. MEV protection: Enable for large transactions
  5. Proxy permissions: Understand proxy types before delegating
  6. Rate limiting: Prevent DoS by respecting limits

Present Results to Users

When presenting Bittensor SDK results to users:

  1. Format TAO amounts clearly: Show both TAO and Rao when relevant
  2. Explain network concepts: Clarify coldkey/hotkey, netuid, UID for non-technical users
  3. Highlight key metrics: Emphasize important values like stake, emission, registration costs
  4. Include relevant links: Link to documentation for deeper exploration
  5. Note risks: Highlight potential issues like deregistration risk, rate limits

Example output format:

=== Subnet 1 Status ===
Neurons: 256 registered
Total Stake: 125,450.5 TAO
Emission: 0.123 TAO/block
Registration Cost: 5.2 TAO
Validator Take: 18%
═══════════════════════════════════

References

Detailed Documentation

For complete API reference, extended examples, and comprehensive troubleshooting, see:

平台分布

OpenClaw

83.25%
按下载量换算9,141

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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