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Version: 0.3.0

Quick Start

Install the CLI​

# From source (recommended during beta)
git clone https://github.com/SkardiLabs/skardi.git
cd skardi
cargo install --locked --path crates/cli

Or grab a pre-built binary:

curl -fSL "https://github.com/SkardiLabs/skardi/releases/latest/download/skardi-$(uname -m | sed 's/arm64/aarch64/')-$(uname -s | sed 's/Linux/unknown-linux-gnu/' | sed 's/Darwin/apple-darwin/').tar.gz" | tar xz
sudo mv skardi /usr/local/bin/
PlatformTarget
Linux x86_64skardi-x86_64-unknown-linux-gnu.tar.gz
Linux ARM64skardi-aarch64-unknown-linux-gnu.tar.gz
macOS ARM64 (Apple Silicon)skardi-aarch64-apple-darwin.tar.gz

macOS Intel binaries are not published. Build from source if you need one.

First-time agent loop (two minutes)​

# 1. Ad-hoc SQL across local + remote data — no server, no pre-registration
skardi query --sql "SELECT * FROM './data/products.csv' LIMIT 10"
skardi query --sql "SELECT * FROM 's3://mybucket/events.parquet' LIMIT 10"

# 2. Register named sources in a ctx, query them by name
skardi query --ctx ./ctx.yaml --sql "SELECT * FROM products LIMIT 10"

# 3. Turn a parameterized SQL into an agent-callable verb (alias + pipeline)
#    — now any agent with a shell can call it:
skardi grep "turing machine computation" --limit=10

Drop skardi into a Claude Code or Cursor session and the agent can already use any pipeline you've declared as a tool via its Bash integration. No MCP config, no separate server — that's the MVP design intent.

Skardi Server — online serving + offline jobs​

cargo run --bin skardi-server -- \
  --ctx ctx.yaml \
  --pipeline pipelines/ \
  --jobs jobs/ \
  --port 8080
# Pipelines: synchronous answer
curl -X POST http://localhost:8080/product-search-demo/execute \
  -H "Content-Type: application/json" \
  -d '{"brand": null, "max_price": 100.0, "limit": 5}'

# Jobs: submit an async write-to-destination
skardi job run backfill-to-lake --param from_date='2026-01-01'
skardi job status <run_id>

Full reference:

Next Steps​