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openseti-skill skill

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This skill helps you contribute compute power to OpenSETI scans, analyze Breakthrough Listen data, and earn tokens for detected anomalies.

npx playbooks add skill openclaw/skills --skill openseti-skill

Review the files below or copy the command above to add this skill to your agents.

Files (3)

SKILL.md

2.3 KB

---
name: openseti
description: Distributed SETI scanner - contribute compute power to analyze real radio telescope data from Breakthrough Listen. Earn tokens when your analysis discovers anomalies. Use when setting up distributed alien signal detection, running SETI scans, or contributing to the OpenSETI network.
---

# OpenSETI Distributed Scanner

Contribute your compute power to scan real Breakthrough Listen radio telescope data for signs of extraterrestrial intelligence. This is a SETI@home-style distributed computing project with token rewards.

## Quick Start

1. Register your Solana wallet:
```bash
python scripts/openseti.py register <your-wallet-address>
```

2. Run a scan:
```bash
python scripts/openseti.py scan
```

3. Run continuous scanning (background):
```bash
python scripts/openseti.py scan --continuous
```

## How It Works

1. Your machine requests a work unit from the OpenSETI network
2. Work units contain real radio telescope data chunks (~1MB each)
3. Your machine analyzes the data using FFT and signal processing
4. Results are submitted back to the network
5. If an anomaly is detected, you earn tokens

## Analysis Criteria

The scanner looks for signals that match ETI signatures:

- **Narrowband signals** (< 10 Hz bandwidth) - Natural sources are broadband
- **Doppler drift** - Frequency shift indicating non-terrestrial origin
- **High SNR** - Strong signals above noise floor
- **Hydrogen line proximity** - 1420.405 MHz is the "water hole"
- **Non-RFI patterns** - Doesn't match known Earth interference

## Reward Structure

| Classification | Score | Tokens |
|---------------|-------|--------|
| NATURAL | 0.0 - 0.15 | 0 |
| WEAK_SIGNAL | 0.15 - 0.4 | 0 |
| INVESTIGATING | 0.4 - 0.7 | 2,500 |
| ANOMALY_FLAGGED | 0.7+ | 5,000 |

Tokens are tracked on-chain and distributed when the token launches.

## Commands

- `openseti register <wallet>` - Register your Solana wallet
- `openseti scan` - Run one scan cycle
- `openseti scan --continuous` - Run continuous scanning
- `openseti stats` - Show your contribution stats
- `openseti leaderboard` - Show top contributors

## Requirements

- Python 3.8+
- NumPy and SciPy (`pip install numpy scipy requests`)

## Data Source

All data comes from the Breakthrough Listen Open Data Archive:
https://breakthroughinitiatives.org/opendatasearch

Observations from the Green Bank Telescope and Parkes Observatory.

Overview

This skill is a distributed SETI scanner that lets users contribute compute to analyze real Breakthrough Listen radio telescope data and earn tokens for flagged anomalies. It provides simple CLI commands to register a Solana wallet, run scans (one-off or continuous), and track contributions. The scanner applies FFT and signal-processing heuristics to detect narrowband, Doppler-shifted, high-SNR features near key frequencies.

How this skill works

The tool requests work units (small radio data chunks) from the OpenSETI network, runs local analysis using NumPy/SciPy routines, and submits results back to the network. It checks for narrowband signals, Doppler drift, high signal-to-noise ratios, hydrogen-line proximity, and non-RFI patterns. Detected anomalies receive a classification score that determines token rewards when the token launch occurs.

When to use it

When you want to contribute spare CPU cycles to scan real radio telescope data.
When setting up a continuous background node for distributed SETI analysis.
If you want to earn tokens for validated anomaly detections in the OpenSETI network.
When testing signal-processing pipelines on authentic Breakthrough Listen observations.
To compare local detection results with network leaderboards and contribution stats.

Best practices

Register your Solana wallet before scanning to ensure rewards are credited.
Run continuous scans on reliably provisioned machines with stable internet.
Use machines with sufficient RAM and CPU for FFT-heavy processing; Python 3.8+ required.
Isolate scanning in a container or VM if you want reproducible environments and dependency control.
Monitor logs and stats to detect persistent RFI or misclassifications and adjust filters.

Example use cases

A home server running continuous scans to donate idle compute while tracking leaderboard progress.
A research lab validating detection heuristics on Green Bank Telescope or Parkes data.
A data scientist benchmarking novel signal-classification models against OpenSETI work units.
Community events where volunteers register wallets and compete to find high-scoring anomalies.
Automated provisioning of scan nodes in a cloud cluster for short-term observation campaigns.

FAQ

What do I need installed to run scans?

Python 3.8+ and the Python packages numpy, scipy, and requests are required.

How are rewards awarded?

Detections are scored by classification thresholds; qualifying anomalies earn token payouts tracked on-chain when the project token launches.

Where does the data come from?

All work units are derived from the Breakthrough Listen Open Data Archive (observations like Green Bank and Parkes).