AI Assistant

The navi skill gives your AI coding agent focused knowledge of the Navi language — syntax, execution model, standard library, and common patterns — so it can write correct, idiomatic .nv indicator and strategy scripts without needing to learn from scratch each session.

What's included

The skill contains four reference files loaded on demand:

File	Purpose
syntax.md	Surface syntax — declarations, control flow, functions, struct/enum/newtype, imports
execution-model.md	Bar-by-bar execution, type qualifiers (const/input/simple/series), var/varip, na, history references, avoiding repainting
stdlib.md	Built-in functions — prelude, ta/math/str, Array/Map/Matrix, drawing, strategy
patterns.md	Proven idioms — indicator/strategy/library skeletons, warmup guards, stateful accumulators, cross logic, pitfalls

Installation

Install from the longbridge/navi repository with the Skills CLI:

npx skills add longbridge/navi

To inspect the repository's available skills before installing:

npx skills add longbridge/navi --list

If your installer asks which skill to add, choose navi. You can also install it explicitly:

npx skills add longbridge/navi --skill navi

Alternatively, download the packaged skill and extract it into your agent's skills directory.

Install the standalone navi CLI as well so the AI agent can compile and run the scripts it writes.

macOS or Linux:

curl -fsSL https://navi-lang.org/install.sh | sh

Windows PowerShell:

irm https://navi-lang.org/install.ps1 | iex

Verify the CLI is available:

navi --version

Update the installed skill when Navi documentation or APIs change:

npx skills update navi

The CLI contains no market data. The validation workflow below uses --data with caller-provided synthetic or real OHLCV data.

Usage

Once installed, compatible AI coding agents can use the skill automatically when working with .nv files or when you ask about Navi.

Write a complete Navi VWAP indicator with configurable upper and lower bands.
Save it as vwap_bands.nv and validate it with the navi CLI.

For better results, include:

• The script kind: indicator, strategy, or library
• Inputs and expected plots, signals, or orders
• Symbol or timeframe assumptions
• Repainting and warmup requirements
• The target .nv filename

Recommended Workflow

1. Ask the agent for a complete .nv file, not an isolated fragment.

2. Use snake_case for filenames, variables, parameters, and functions.

3. Require the agent to validate the file with the standalone navi CLI:

   navi lint path/to/script.nv

4. If formatting fails, run navi fmt, then lint again.

5. When runtime behavior matters, create a small synthetic OHLCV CSV with enough bars for the script's longest lookback, then execute it:

   navi run path/to/script.nv \
     --data bars.csv \
     --symbol NASDAQ:AAPL \
     --timeframe 1D

6. Use the validated script with the Longbridge CLI, App, or desktop client. The standalone navi CLI is primarily a development and debugging tool.

Do not accept a claim that a script was validated unless the agent ran the CLI successfully. A code block alone is not validation.

Runtime data

The standalone navi CLI only provides basic compilation and local execution. It does not bundle or download market data, so navi run requires caller-provided data through --data. Synthetic data is the dependable default for AI validation: use chronological Unix-millisecond timestamps, internally consistent OHLC prices, and scenarios that exercise warmup, rising, falling, flat, and signal-producing paths as relevant.

For real data, prefer these sources when available:

• An installed and authenticated Longbridge CLI: use longbridge kline history SYMBOL --start YYYY-MM-DD --end YYYY-MM-DD --format json, then convert the returned candles to the CSV columns required by navi run. You can also use longbridge quant run to execute a script directly against Longbridge historical data.
• A Longbridge MCP server in the AI environment: request historical candlesticks with its market-data tools and convert the returned OHLCV values to CSV.
• Otherwise, use a reputable public data source and verify its licensing, adjustment, timezone, row ordering, and missing-bar behavior.

Real data complements synthetic cases; it does not replace targeted data that deliberately reaches important branches.

Online preview

After validation, an AI agent can generate a Playground preview by encoding the complete UTF-8 script as unpadded Base64URL and placing it in the code query parameter:

https://navi-lang.org/playground?code=<base64url-source>

Opening the link loads the script as an unsaved file and adds it to the chart. Base64URL uses - and _ instead of + and /, with trailing = removed. Keep the source file alongside the link because browsers and chat clients limit URL length.

Example Requests

Review momentum_strategy.nv for repainting and series-state errors.
Fix the file, preserve its behavior, and run navi lint when finished.

Create a Navi library that exports EMA and crossover helpers.
Use Navi naming conventions, save it as moving_average_helpers.nv,
and return the exact navi lint result.

The skill treats navi-lang.org and its standard-library reference as the source of truth for current APIs.