MR
Mayur Rathi
@mayurrathi
⭐ 40.7k GitHub stars

Blockrun

Blockrun is an data AI skill with a core value of 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. It helps developers solve real-world problems in the data domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

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...

Last verified on: 2026-07-08

Quick Facts

Category data
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 40.7k
Last Verified 2026-07-08
Risk Level Low
mkdir -p ./skills/blockrun && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-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. 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. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 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. 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.

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