> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flare-db.com/llms.txt
> Use this file to discover all available pages before exploring further.

# FlareDB Skills for Agents

FlareDB Skills give AI coding agents the knowledge to build, test, debug, and run Apache Beam pipelines. Each skill focuses on a specific task, and agents can combine them when needed.

## Available Skills

| Skill | What it does |
| - | - |
| `beam-pipeline` | Helps agents write and test Apache Beam pipelines, choose transforms and connectors, and follow FlareDB's supported features. |
| `flaredb-skills` | Helps agents install and manage FlareDB, start local instances, submit pipelines, and inspect job logs. |
| `beam-debugging` | Helps agents diagnose pipeline errors, incorrect results, and performance issues. |
| `beam-streaming` | Guides agents through streaming concepts such as windows, triggers, watermarks, and stateful processing for runners other than FlareDB. |

## How They Work Together

The skills complement each other, allowing an agent to handle a task from pipeline development through execution and debugging.

For example, if you ask an agent to analyze thousands of log files:

1. **`beam-pipeline`** designs a Python Beam pipeline to process the files and produce a concise summary.
2. **`beam-streaming`** provides the guidance on how to write a streaming pipeline correctly.
3. **`flaredb-skills`** manages the local FlareDB environment and runs the pipeline after you approve the execution plan.
4. **`beam-debugging`** helps diagnose failures or incorrect results if something goes wrong.

## Example

Once the skills are installed, simply describe your task to your coding agent:

> Analyze all the CSV files in this directory, identify schema inconsistencies, and report the affected files with a summary of the issues.

The agent can use `beam-pipeline` to construct the processing pipeline and `flaredb-skills` to run it on FlareDB, rather than reading every file into its context.

The goal is simple: **let AI agents process large amounts of data efficiently using Apache Beam and FlareDB, while keeping only the relevant results in their context.**

## Review

Before running a pipeline on FlareDB, the agent presents a **pipeline brief** for your review and approval. This lets you verify what the pipeline will do before the agent executes the pipeline on FlareDB
Once approved, the agent can start FlareDB, execute the pipeline, inspect the job logs, and verify the results.

**Note:** Sometimes you agent harness might prevent your agents from running CLI commands, in that case please run the commands
for your agents.

## Install the skills

Make sure Node.js and `npx` are available, then run:

```bash theme={null}
npx skills add flare-db/skills
```

The installer will guide you through these choices:

1. **Select skills** — select the skills you want to install. You can install all four:
   * `beam-pipeline`
   * `flaredb-skills`
   * `beam-debugging`
   * `beam-streaming`
2. **Select coding agents** — choose the agents you use, such as Codex, Cursor, Gemini CLI, GitHub Copilot or others shown by the installer.
3. **Choose installation scope** — select Global to make the skills available across projects, or choose another scope if offered and preferred.
4. **Confirm installation** — review the summary and approve the installation.

The installer should report that installation is complete.


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