blockrun

name	blockrun
description	ALWAYS use this when the request matches Blockrun: BlockRun works with Claude Code and Google Antigravity.

BlockRun

Selective Reading Rule

Start with:

• references/senior-master-standard.md
• references/usage-routing.md
• references/quality-checklist.md

Then load only the inherited docs, scripts, assets, or examples that match the user's actual task.

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:

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):

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.):

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:

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

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.

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?",
    search=True  # Enables real-time X/Twitter search
)
print(response)

Advanced X Search with Filters

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()

response = client.chat(
    "xai/grok-3",
    "Analyze @blockrunai's recent content and engagement",
    search_parameters={
        "mode": "on",
        "sources": [
            {
                "type": "x",
                "included_x_handles": ["blockrunai"],
                "post_favorite_count": 5
            }
        ],
        "max_search_results": 20,
        "return_citations": True
    }
)
print(response)

Image Generation

from blockrun_llm import ImageClient

client = ImageClient()
result = client.generate("A cute cat wearing a space helmet")
print(result.data[0].url)

xAI Live Search Reference

Live Search is xAI's real-time data API. Cost: $0.025 per source (default 10 sources = ~$0.26).

To reduce costs, set max_search_results to a lower value:

# Only use 5 sources (~$0.13)
response = client.chat("xai/grok-3", "What's trending?",
    search_parameters={"mode": "on", "max_search_results": 5})

Search Parameters

Parameter	Type	Default	Description
mode	string	"auto"	"off", "auto", or "on"
sources	array	web,news,x	Data sources to query
return_citations	bool	true	Include source URLs
from_date	string	-	Start date (YYYY-MM-DD)
to_date	string	-	End date (YYYY-MM-DD)
max_search_results	int	10	Max sources to return (customize to control cost)

Source Types

X/Twitter Source:

{
    "type": "x",
    "included_x_handles": ["handle1", "handle2"],  # Max 10
    "excluded_x_handles": ["spam_account"],        # Max 10
    "post_favorite_count": 100,  # Min likes threshold
    "post_view_count": 1000      # Min views threshold
}

Web Source:

{
    "type": "web",
    "country": "US",  # ISO alpha-2 code
    "allowed_websites": ["example.com"],  # Max 5
    "safe_search": True
}

News Source:

{
    "type": "news",
    "country": "US",
    "excluded_websites": ["tabloid.com"]  # Max 5
}

Available Models

Model	Best For	Pricing
openai/gpt-5.2	Second opinions, code review, general	$1.75/M in, $14/M out
openai/gpt-5-mini	Cost-optimized reasoning	$0.30/M in, $1.20/M out
openai/o4-mini	Latest efficient reasoning	$1.10/M in, $4.40/M out
openai/o3	Advanced reasoning, complex problems	$10/M in, $40/M out
xai/grok-3	Real-time X/Twitter data	$3/M + $0.025/source
deepseek/deepseek-chat	Simple tasks, bulk processing	$0.14/M in, $0.28/M out
google/gemini-2.5-flash	Very long documents, fast	$0.15/M in, $0.60/M out
openai/dall-e-3	Photorealistic images	$0.04/image
google/nano-banana	Fast, artistic images	$0.01/image

M = million tokens. Actual cost depends on your prompt and response length.

Cost Reference

All LLM costs are per million tokens (M = 1,000,000 tokens).

Model	Input	Output
GPT-5.2	$1.75/M	$14.00/M
GPT-5-mini	$0.30/M	$1.20/M
Grok-3 (no search)	$3.00/M	$15.00/M
DeepSeek	$0.14/M	$0.28/M

Fixed Cost Actions
Grok Live Search	$0.025/source (default 10 = $0.25)
DALL-E image	$0.04/image
Nano Banana image	$0.01/image

Typical costs: A 500-word prompt (~750 tokens) to GPT-5.2 costs ~$0.001 input. A 1000-word response (~1500 tokens) costs ~$0.02 output.

Setup & Funding

Wallet location: $HOME/.blockrun/.session (e.g., /Users/username/.blockrun/.session)

First-time setup:

1. Wallet auto-creates when setup_agent_wallet() is called
2. Check wallet and balance:

from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet()
print(f"Wallet: {client.get_wallet_address()}")
print(f"Balance: ${client.get_balance():.2f} USDC")

3. Fund wallet with $1-5 USDC on Base network

Show QR code for funding (ASCII for terminal):

from blockrun_llm import generate_wallet_qr_ascii, get_wallet_address
print(generate_wallet_qr_ascii(get_wallet_address()))

Troubleshooting

"Grok says it has no real-time access" → You forgot to enable Live Search. Add search=True:

response = client.chat("xai/grok-3", "What's trending?", search=True)

Module not found → Install the SDK: pip install blockrun-llm

Updates

pip install --upgrade blockrun-llm

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.