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blockrunblockrun 搜索

Agent Skill

blockrun 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

9,284

周安装

383

GitHub Stars

35,717

下载量

3,033
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:blockrun(blockrun 搜索)
来源仓库:https://github.com/sickn33/antigravity-awesome-skills
仓库路径:skills/blockrun
安装命令:
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill blockrun
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill blockrun

简介

集成 x402 微支付协议的外部能力网关,按需调用图像生成与实时数据源。

  • 支持 DALL-E 绘图、X/Twitter 舆情抓取与 GPT-5.2 推理服务低成本接入。
  • 费用自动从绑定钱包扣除,DeepSeek 与 OpenAI 双通道平衡性价比。
  • 严禁用于欺诈、骚扰或违反平台政策的内容生成,违者承担全部法律责任。
  • blockrun 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

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 NeedYou CallPricing
Image generationDALL-E$0.04/image
Real-time X dataGrok + Live Search$0.025/source
Second opinionGPT-5.2$1.75/M input, $14/M output
Cheaper processingDeepSeek$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

TriggerYour 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 fineDo it yourself, don't mention BlockRun

Example User Prompts

Users will say things like:

User SaysWhat 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

ParameterTypeDefaultDescription
modestring"auto""off", "auto", or "on"
sourcesarrayweb,news,xData sources to query
return_citationsbooltrueInclude source URLs
from_datestring-Start date (YYYY-MM-DD)
to_datestring-End date (YYYY-MM-DD)
max_search_resultsint10Max 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

ModelBest ForPricing
openai/gpt-5.2Second opinions, code review, general$1.75/M in, $14/M out
openai/gpt-5-miniCost-optimized reasoning$0.30/M in, $1.20/M out
openai/o4-miniLatest efficient reasoning$1.10/M in, $4.40/M out
openai/o3Advanced reasoning, complex problems$10/M in, $40/M out
xai/grok-3Real-time X/Twitter data$3/M + $0.025/source
deepseek/deepseek-chatSimple tasks, bulk processing$0.14/M in, $0.28/M out
google/gemini-2.5-flashVery long documents, fast$0.15/M in, $0.60/M out
openai/dall-e-3Photorealistic images$0.04/image
google/nano-bananaFast, 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).

ModelInputOutput
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")
  1. 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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.28%
按下载量换算858

OpenCode

22.58%
按下载量换算685

Gemini CLI

16.36%
按下载量换算496

Antigravity

13.12%
按下载量换算398

Cursor

6.68%
按下载量换算203

Codex

3.65%
按下载量换算111

安全审计

Gen Agent Trust Hub

可疑

Socket

可疑

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill blockrun;npx skills add sickn33/antigravity-awesome-skills --skill "blockrun" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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