Use when user needs capabilities Claude lacks (image generation, real-time X/Twitter data) or explicitly requests external models ("blockrun", "use grok", "use gpt", "dall-e", "deepseek")
Use the skills CLI to install this skill with one command. Auto-detects all installed AI assistants.
Method 1 - skills CLI
npx skills i sickn33/antigravity-awesome-skills/skills/blockrunMethod 2 - openskills (supports sync & update)
npx openskills install sickn33/antigravity-awesome-skillsAuto-detects Claude Code, Cursor, Codex CLI, Gemini CLI, and more. One install, works everywhere.
Installation Path
Download and extract to one of the following locations:
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Best for coding tasks
No setup required
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.
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.
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']
| 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 |
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 |
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 newCheck 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()))Prerequisite: Install the SDK with pip install blockrun-llm
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}")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)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",
from blockrun_llm import ImageClient
client = ImageClient()
result = client.generate("A cute cat wearing a space helmet")
print(result.data[0].url)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})| 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 | - |
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
}| 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 |
M = million tokens. Actual cost depends on your prompt and response length.
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.
Wallet location: $HOME/.blockrun/.session (e.g., /Users/username/.blockrun/.session)
First-time setup:
setup_agent_wallet() is calledfrom 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")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()))"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
pip install --upgrade blockrun-llm| 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) |
| 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 |