Fetches blockrun from sickn33/antigravity-awesome-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/blockrun
Restart Cursor to activate blockrun. Access via /blockrun in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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:
from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet()budget =1.0# User's budget# Before each call, check if within budgetspending = client.get_spending()if spending['total_usd']>= budget:print(f"Budget reached: ${spending['total_usd']:.4f} spent")# Stop making callselse: response = client.chat("openai/gpt-5.2","Hello!")# At the end, report spendingspending = 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):
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.):
from blockrun_llm import generate_wallet_qr_ascii, get_wallet_address
# ASCII QR for terminal displayprint(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 neededresponse = client.chat("openai/gpt-5.2","What is 2+2?")print(response)# Check spendingspending = 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=Trueresponse = 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
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:
{"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
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/
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
