BlockRun works with Claude Code and Google Antigravity.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionblockrunExecute the skills CLI command in your project's root directory to begin installation:
Fetches blockrun from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate blockrun. Access via /blockrun in your agent's command palette.
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.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
24.2K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
24.2K
stars
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/ ✓ Make data-driven prioritization decisions faster Stakeholder CommunicationDraft PRDs, status updates, and stakeholder presentations Example Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement ✓ Save 3-5 hours/week on communication overhead Implementation GuidePrerequisites
Time Estimate 30-60 minutes to see productivity improvements Steps
Common Pitfalls
Best Practices✓ Do
✗ Don't
💡 Pro Tips
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
Related Skillsml-paper-writing77davila7/claude-code-templates AI/MLsame repo grill-me712mattpocock/skills Productivitysame category premortem218parcadei/continuous-claude-v3 Productivitysame category deslop165cursor/plugins Productivitysame category travel-planner147ailabs-393/ai-labs-claude-skills Productivitysame category nutritional-specialist142ailabs-393/ai-labs-claude-skills Productivitysame category Reviews4.7★★★★★73 reviews
showing 1-10 of 73 1 / 8 DiscussionComments — not star reviews
|