---
name: blockrun-f1658d2cde53
description: 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")
allowed-tools: Read, Bash(python:*), Bash(python3:*), Bash(pip:*), Bash(source:*)
---

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

### Advanced X Search with Filters

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

**News Source:**
```python
{
    "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:
```python
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):**
```python
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`:
```python
response = client.chat("xai/grok-3", "What's trending?", search=True)
```

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

## Updates

```bash
pip install --upgrade blockrun-llm
```


---
Source attribution: Dicklesworthstone
Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/templates-davila7/cli-tool/components/skills/web-development/blockrun/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License (with OpenAI/Anthropic Rider)

Copyright (c) 2026 Jeffrey Emanuel

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

ADDITIONAL RIDER / RESTRICTION (OpenAI / Anthropic):

This rider is part of the "conditions" of this License. In the event of any
conflict between this rider and any other portion of this License, this rider
controls.

"Restricted Parties" means OpenAI, L.L.C.; Anthropic, PBC; any of their
respective Affiliates; and any person or entity acting directly or indirectly
on behalf of, for the benefit of, or under the direction of any of the
foregoing (including any officer, director, employee, contractor, agent,
consultant, service provider, or representative).

Notwithstanding any other provision of this License, no rights are granted to
any Restricted Party. Any purported license, sublicense, assignment, transfer,
or other permission to any Restricted Party is null and void absent the
express prior written permission of Jeffrey Emanuel.

You may not provide, disclose, distribute, sublicense, sell, lease, lend,
host, make available, or otherwise permit access to the Software or any
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(collectively, "Derivative Works") to or for any Restricted Party.

For purposes of this rider, "use" includes, without limitation: copying,
modifying, merging, publishing, distributing, sublicensing, selling,
transferring, making available, hosting, deploying, executing, benchmarking,
testing, analyzing, indexing, or incorporating the Software or any Derivative
Works into any dataset, training corpus, evaluation harness, or pipeline for
machine learning or other automated systems.

This rider applies to the Software and all Derivative Works. As a condition of
use, you agree that this rider is a precondition to exercising any rights
under this License, and you agree that any distribution of the Software or any
Derivative Works must include this rider provision unmodified.

Any breach of this rider automatically and immediately terminates the
permissions granted by this License. Upon termination, you must immediately
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destroy all copies under your control.

You agree that a breach of this rider would cause irreparable harm and that
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For purposes of this rider, "Affiliate" means any entity that directly or
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# Dave AI resource metadata

Creator / publisher: Dicklesworthstone
Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/templates-davila7/cli-tool/components/skills/web-development/blockrun/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Marketing, Product Management
Roles: Data Scientist, Growth Marketer, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API
Tools: Blockrun Llm, Dall E, Deepseek, Grok
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: f8c8125fbf9581bcadccce04fcec491a09e612d8e677580a66a2f16999a64c0d