An advanced integration for the TaoStats API providing real-time Bittensor blockchain metrics, subnet economics, and validator performance data.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bittensor-taostats
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bittensor-taostats using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is TaoStats Skill?
The TaoStats Skill is a robust toolset designed for developers and traders operating within the Bittensor ecosystem. By utilizing this integration within Openclaw Skills, users gain programmatic access to critical on-chain data including dTAO pool liquidity, validator APY, and subnet emissions. This skill bridges the gap between raw blockchain data and actionable financial intelligence, enabling automated portfolio monitoring and high-precision entry validation for decentralized AI assets.
Built with developer experience in mind, it provides both Bash helper functions and a Python client to streamline interactions with the TaoStats API. Whether you are monitoring capital flows or selecting the highest-performing validators, this skill ensures you have the technical edge in the Bittensor network through the power of Openclaw Skills.
TaoStats Skill Use Cases
- Validating dTAO pool entry quality using root proportion metrics to avoid artificial price pumps.
- Monitoring validator APY and performance across multiple subnets for optimized staking returns.
- Tracking portfolio balances and historical transaction data across coldkeys.
- Conducting fundamental analysis on subnet emissions and capital flows to identify momentum.
- Identifying pruning risks and neuron immunity status for miners and validators.
How TaoStats Skill Works
- The skill initializes by sourcing environment variables and helper functions to authenticate with the TaoStats API using a secure API key.
- Users execute specialized Bash functions or Python methods to query specific endpoints for subnets, validators, or wallet data.
- The skill retrieves raw data from the TaoStats API, handling rate limits of 5 calls per minute on the free tier.
- Integrated logic processes the JSON response, utilizing tools like jq to filter for critical trading metrics such as root_prop or APY.
- The skill normalizes data, such as converting RAO units to TAO, to provide accurate financial reporting and risk assessment.
TaoStats Skill Setup
First, obtain a free API key from taostats.io and export it as an environment variable in your terminal:
export TAOSTATS_API_KEY="tao-xxxxx:yyyyy"
Next, source the provided helper functions into your environment to enable the Openclaw Skills CLI commands:
source ~/.openclaw/workspace/skills/taostats/taostats.sh
TaoStats Skill Data Schema & Taxonomy
The skill organizes Bittensor data into several key taxonomies for clear analysis:
| Entity | Key Data Fields | Purpose |
|---|---|---|
| dTAO Pool | price, root_prop, fear_and_greed_sentiment, volume | Entry and Sentiment Analysis |
| Validator | seven_day_apy, take, stake_24_hr_change | Yield and Reliability Selection |
| Subnet | emission, net_flow_7_days, registration_cost | Ecosystem Economic Health |
| Metagraph | rank, consensus, pruning_score, in_danger | Neuron Risk and Performance |
| Portfolio | balance_as_tao, root_prop, slippage | Risk Management and Tax Tracking |
TaoStats Skill Advanced Features
- Multi-subnet entry scanner that cross-references root_prop with APY and capital flow.
- Automated portfolio risk monitor that flags high-risk positions based on root proportion thresholds.
- Robust Python client featuring automatic retry logic and seamless pagination handling.
- Historical backtesting support through specialized pool snapshot and validator trend endpoints.
- Real-time capital flow monitoring to detect emerging S-tier subnets in the Bittensor network.
SKILL.md
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