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")
npxskills add davila7/claude-code-templates--skill blockrunLoading…
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']:.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")
| 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 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()))
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",
"included_x_handles": ["blockrunai"],
"post_favorite_count": 5
}
],
"max_search_results": 20,
"return_citations": True
}
)
print(response)
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 | - | 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) |
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 | 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.
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
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).