Menu
Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.
Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/BEKO2210/Firstbrain --skill blockrunLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
Basé sur la classification professionnelle SOC
Affichage de SKILL.md
| name | blockrun |
| description | BlockRun works with Claude Code and Google Antigravity. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | ai-ml |
| category | llm-agents |
| risk | unknown |
| source | community |
| tags | ["skill","ai-ml","llm-agents","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.
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