Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Bittensor SDK
Interact with the Bittensor blockchain to manage wallets, stake TAO, register neurons, query subnets/metagraphs, track emissions, and set neuron weights.
skill openclawclawhub Free
Interact with the Bittensor blockchain to manage wallets, stake TAO, register neurons, query subnets/metagraphs, track emissions, and set neuron weights.
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- Download the package from Yavira.
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Package facts
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- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/API_REFERENCE.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
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- taoleeh
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Documentation
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
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
Prerequisites
pip install bittensor>=8.0.0
Opencode Installation
cp -r skills/bittensor-sdk ~/.opencode/skills/
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 )
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}")
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: Check balance (need > 1 TAO for burn registration) Verify POW solution is correct Check network connectivity 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
Always close connections: Use subtensor.close() when done Handle errors gracefully: Use try-except blocks Implement rate limiting: Don't exceed network limits Use MEV protection: Enable for large transactions Monitor emissions: Track network health Use safe staking: Enable price protection Keep keys secure: Never expose private keys
Security Considerations
Private keys: Never expose or log private keys Seed phrases: Store securely, never share Transaction signing: Always verify before signing MEV protection: Enable for large transactions Proxy permissions: Understand proxy types before delegating Rate limiting: Prevent DoS by respecting limits
Present Results to Users
When presenting Bittensor SDK results to users: Format TAO amounts clearly: Show both TAO and Rao when relevant Explain network concepts: Clarify coldkey/hotkey, netuid, UID for non-technical users Highlight key metrics: Emphasize important values like stake, emission, registration costs Include relevant links: Link to documentation for deeper exploration 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% βββββββββββββββββββββββββββββββββββ
References
Bittensor Documentation Bittensor SDK Reference Learn Bittensor Taostats API Bittensor GitHub
Detailed Documentation
For complete API reference, extended examples, and comprehensive troubleshooting, see: API Reference - Complete method documentation Extended Examples - Advanced usage patterns Quick Reference - Common operations summary
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- references/API_REFERENCE.md