Best use case
blockrun is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
BlockRun works with Claude Code and Google Antigravity.
Teams using blockrun should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/blockrun/SKILL.mdinside your project - Restart your AI agent ā it will auto-discover the skill
How blockrun Compares
| Feature / Agent | blockrun | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
BlockRun works with Claude Code and Google Antigravity.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
ChatGPT vs Claude for Agent Skills
Compare ChatGPT and Claude for AI agent skills across coding, writing, research, and reusable workflow execution.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
SKILL.md 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"š° 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"**
ā You forgot to enable Live Search. Add `search=True`:
```python
response = client.chat("xai/grok-3", "What's trending?", search=True)
```
**Module not found**
ā Install the SDK: `pip install blockrun-llm`
## Updates
```bash
pip install --upgrade blockrun-llm
```Related Skills
using-git-worktrees
Git worktrees create isolated workspaces sharing the same repository, allowing work on multiple branches simultaneously without switching.
slack-gif-creator
A toolkit providing utilities and knowledge for creating animated GIFs optimized for Slack.
skill-creator
To create new CLI skills following Anthropic's official best practices with zero manual configuration. This skill automates brainstorming, template application, validation, and installation processes while maintaining progressive disclosure patterns and writing style standards.
project-skill-audit
Audit a project and recommend the highest-value skills to add or update.
notebooklm
Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.
molykit
CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE streaming, AI chat, molykit, PlatformSend, spawn(), ThreadToken, cross-platform async, Chat widget, Messages, PromptInput, Avatar, LLM
diary
Unified Diary System: A context-preserving automated logger for multi-project development.
claude-settings-audit
Analyze a repository to generate recommended Claude Code settings.json permissions. Use when setting up a new project, auditing existing settings, or determining which read-only bash commands to allow. Detects tech stack, build tools, and monorepo structure.
claude-in-chrome-troubleshooting
Diagnose and fix Claude in Chrome MCP extension connectivity issues. Use when mcp__claude-in-chrome__* tools fail, return "Browser extension is not connected", or behave erratically.
claude-code-expert
Especialista profundo em Claude Code - CLI da Anthropic. Maximiza produtividade com atalhos, hooks, MCPs, configuracoes avancadas, workflows, CLAUDE.md, memoria, sub-agentes, permissoes e integracao com ecossistemas.
auri-core
Auri: assistente de voz inteligente (Alexa + Claude claude-opus-4-20250805). Visao do produto, persona Vitoria Neural, stack AWS, modelo Free/Pro/Business/Enterprise, roadmap 4 fases, GTM, north star WAC e analise competitiva.
audit-skills
Expert security auditor for AI Skills and Bundles. Performs non-intrusive static analysis to identify malicious patterns, data leaks, system stability risks, and obfuscated payloads across Windows, macOS, Linux/Unix, and Mobile (Android/iOS).