Blockrun
Use when user needs capabilities Claude lacks (image generation, real-time X/Twitter data) or explicitly requests external models (\\\"blockrun\\\", \\\"use grok\\\", \\\"use gpt\\\", \\\"da...
mkdir -p ./skills/blockrun && curl -sfL https://raw.githubusercontent.com/mayurrathi/awesome-agent-skills/main/skills/blockrun/SKILL.md -o ./skills/blockrun/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# BlockRun
**BlockRun works with Claude Code and Google Antigravity.**
You can't generate images. You have no real-time X/Twitter access. BlockRun gives you these capabilities.
Philosophy
You have a wallet. When you need a capability you lack, you pay for it autonomously.
| You Need | You Call | Pricing |
|----------|----------|---------|
| Image generation | DALL-E | $0.04/image |
| Real-time X data | Grok + Live Search | $0.025/source |
| Second opinion | GPT-5.2 | $1.75/M input, $14/M output |
| Cheaper processing | DeepSeek | $0.14/M input, $0.28/M output |
**How it works:** BlockRun uses x402 micropayments to route your requests to OpenAI, xAI, Google, and other providers. No API keys needed - your wallet pays per token.
Budget Control (Optional)
If the user specifies a budget (e.g., "use at most $1"), track spending and stop when budget is reached:
```python
from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet()
budget = 1.0 # User's budget
# Before each call, check if within budget
spending = client.get_spending()
if spending['total_usd'] >= budget:
print(f"Budget reached: ${spending['total_usd']:.4f} spent")
# Stop making calls
else:
response = client.chat("openai/gpt-5.2", "Hello!")
# At the end, report spending
spending = client.get_spending()
print(f"💰 Total spent: ${spending['total_usd']:.4f} across {spending['calls']} calls")
```
When to Use
| Trigger | Your Action |
|---------|-------------|
| User explicitly requests ("blockrun second opinion with GPT on...", "use grok to check...", "generate image with dall-e") | Execute via BlockRun |
| User needs something you can't do (images, live X data) | Suggest BlockRun, wait for confirmation |
| You can handle the task fine | Do it yourself, don't mention BlockRun |
Example User Prompts
Users will say things like:
| User Says | What You Do |
|-----------|-------------|
| "blockrun generate an image of a sunset" | Call DALL-E via ImageClient |
| "use grok to check what's trending on X" | Call Grok with `search=True` |
| "blockrun GPT review this code" | Call GPT-5.2 via LLMClient |
| "what's the latest news about AI agents?" | Suggest Grok (you lack real-time data) |
| "generate a logo for my startup" | Suggest DALL-E (you can't generate images) |
| "blockrun check my balance" | Show wallet balance via `get_balance()` |
| "blockrun deepseek summarize this file" | Call DeepSeek for cost savings |
Wallet & Balance
Use `setup_agent_wallet()` to auto-create a wallet and get a client. This shows the QR code and welcome message on first use.
**Initialize client (always start with this):**
```python
from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet() # Auto-creates wallet, shows QR if new
```
**Check balance (when user asks "show balance", "check wallet", etc.):**
```python
balance = client.get_balance() # On-chain USDC balance
print(f"Balance: ${balance:.2f} USDC")
print(f"Wallet: {client.get_wallet_address()}")
```
**Show QR code for funding:**
```python
from blockrun_llm import generate_wallet_qr_ascii, get_wallet_address
# ASCII QR for terminal display
print(generate_wallet_qr_ascii(get_wallet_address()))
```
SDK Usage
**Prerequisite:** Install the SDK with `pip install blockrun-llm`
Basic Chat
```python
from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet() # Auto-creates wallet if needed
response = client.chat("openai/gpt-5.2", "What is 2+2?")
print(response)
# Check spending
spending = client.get_spending()
print(f"Spent ${spending['total_usd']:.4f}")
```
Real-time X/Twitter Search (xAI Live Search)
**IMPORTANT:** For real-time X/Twitter data, you MUST enable Live Search with `search=True` or `search_parameters`.
```python
from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet()
# Simple: Enable live search with search=True
response = client.chat(
"xai/grok-3",
"What are the latest posts from @blockrunai on X?",
🎯 Best For
- Claude users
- Data professionals
- Analytics teams
- Researchers
💡 Use Cases
- Data pipeline auditing
- Query optimization
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Blockrun to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install Blockrun?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/blockrun/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Ignoring data quality
AI analysis inherits all data quality issues — profile your data first.