blockrun

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

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

BlockRun works with Claude Code and Google Antigravity.

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 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 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

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.):

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

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/
how to use blockrun

How to use blockrun on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add blockrun
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches blockrun from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/blockrun

Reload or restart Cursor to activate blockrun. Access the skill through slash commands (e.g., /blockrun) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft 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 Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • 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

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.574 reviews
  • Ishan Srinivasan· Dec 28, 2024

    Solid pick for teams standardizing on skills: blockrun is focused, and the summary matches what you get after install.

  • Nia Abbas· Dec 24, 2024

    Useful defaults in blockrun — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Nikhil Sanchez· Dec 24, 2024

    I recommend blockrun for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ganesh Mohane· Dec 16, 2024

    Useful defaults in blockrun — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Mateo Torres· Dec 16, 2024

    I recommend blockrun for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mateo Lopez· Dec 16, 2024

    Keeps context tight: blockrun is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kabir Farah· Nov 19, 2024

    We added blockrun from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Xiao Lopez· Nov 15, 2024

    blockrun is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Xiao Wang· Nov 15, 2024

    Keeps context tight: blockrun is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Xiao Li· Nov 11, 2024

    Registry listing for blockrun matched our evaluation — installs cleanly and behaves as described in the markdown.

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