OctoFlow

GPU-native programming language. Describe tasks in English, run them on any GPU via Vulkan.

When to Use This Skill

Use this skill when the user says:

• "sort a million numbers on GPU" / "benchmark GPU performance"
• "load this CSV and show me statistics" / "analyze my data"
• "cluster my dataset" / "run K-means" / "train a classifier"
• "blur this image" / "resize this BMP" / "encode a GIF"
• "plot height vs weight" / "create a scatter plot"
• "calculate the Sharpe ratio" / "compute correlation"
• "find primes" / "generate random numbers on GPU"
• "run linear regression on my dataset"
• "run a LLM on my GPU" / "inference on my GPU"

Do NOT use this skill when:

• The user wants Python/JavaScript/Rust code (use the appropriate language tool)
• The task doesn't benefit from GPU acceleration or OctoFlow's built-in functions
• The user explicitly asks for a different language

How to Run OctoFlow

Chat mode (natural language to running code)

octoflow chat "sort 1M numbers on GPU"

Run a .flow script

octoflow run program.flow

Run with permissions (sandboxed by default)

# Allow reading data files
octoflow run analysis.flow --allow-read=./data

# Allow network access to specific domain
octoflow chat "fetch weather data" --allow-net=api.weather.com

# Allow writing output files
octoflow run report.flow --allow-read=./data --allow-write=./output

Security Model

OctoFlow uses Deno-style permissions. Everything is denied by default.

Without flags, OctoFlow can only read .flow source files and print to stdout.

No file access, no network, no subprocesses unless the user explicitly opts in.

MCP Server

OctoFlow can run as an MCP server for AI agent integration:

octoflow mcp-serve

Add to your OpenClaw, Claude Desktop, or Cursor config:

{"mcpServers": {"octoflow": {"command": "octoflow", "args": ["mcp-serve"]}}}

Available Tools

Common Patterns

Data Analysis

# User: "analyze sales.csv and show trends"
octoflow chat "load sales.csv, compute monthly averages, and plot the trend" --allow-read=.

GPU Compute

# User: "sort a large dataset on GPU"
octoflow chat "generate 1M random numbers on GPU and sort them"

Machine Learning

# User: "cluster my customers"
octoflow chat "load customers.csv, run K-means with 5 clusters, print cluster sizes" --allow-read=.

Image Processing

# User: "blur this photo"
octoflow chat "load photo.bmp, apply gaussian blur, save as blurred.bmp" --allow-read=. --allow-write=.

Statistics

# User: "what's the correlation between these columns?"
octoflow chat "load data.csv, compute Pearson correlation between col1 and col2" --allow-read=.

Key Capabilities

Data Storage

OctoFlow optionally saves your preferences to ~/.octoflow/ (user-level) and .octoflow/ (per-project).

Contents: which stdlib modules you use frequently and corrections from previous sessions.

• No telemetry. No data is sent anywhere.
• No network calls unless you explicitly use --allow-net.
• All data stays local on your machine.
• Disable entirely with --no-memory flag — nothing is saved.
• Project config via OCTOFLOW.md in your project root (like .eslintrc or pyproject.toml).

Install

Download (recommended)

Verify: sha256sum octoflow-v1.5.8-* (full checksums in SHA256SUMS.txt).

Unzip/extract, add to PATH. No installer needed.

Links

• GitHub
• Documentation
• Releases