jeremylongshore

fireflies-reference-architecture

@jeremylongshore/fireflies-reference-architecture
jeremylongshore
1,004
123 forks
Updated 1/18/2026
View on GitHub

Implement Fireflies.ai reference architecture with best-practice project layout. Use when designing new Fireflies.ai integrations, reviewing project structure, or establishing architecture standards for Fireflies.ai applications. Trigger with phrases like "fireflies architecture", "fireflies best practices", "fireflies project structure", "how to organize fireflies", "fireflies layout".

Installation

$skills install @jeremylongshore/fireflies-reference-architecture
Claude Code
Cursor
Copilot
Codex
Antigravity

Details

Pathplugins/saas-packs/fireflies-pack/skills/fireflies-reference-architecture/SKILL.md
Branchmain
Scoped Name@jeremylongshore/fireflies-reference-architecture

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions


name: fireflies-reference-architecture description: | Implement Fireflies.ai reference architecture with best-practice project layout. Use when designing new Fireflies.ai integrations, reviewing project structure, or establishing architecture standards for Fireflies.ai applications. Trigger with phrases like "fireflies architecture", "fireflies best practices", "fireflies project structure", "how to organize fireflies", "fireflies layout". allowed-tools: Read, Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Fireflies.ai Reference Architecture

Overview

Production-ready architecture patterns for Fireflies.ai integrations.

Prerequisites

  • Understanding of layered architecture
  • Fireflies.ai SDK knowledge
  • TypeScript project setup
  • Testing framework configured

Project Structure

my-fireflies-project/
├── src/
│   ├── fireflies/
│   │   ├── client.ts           # Singleton client wrapper
│   │   ├── config.ts           # Environment configuration
│   │   ├── types.ts            # TypeScript types
│   │   ├── errors.ts           # Custom error classes
│   │   └── handlers/
│   │       ├── webhooks.ts     # Webhook handlers
│   │       └── events.ts       # Event processing
│   ├── services/
│   │   └── fireflies/
│   │       ├── index.ts        # Service facade
│   │       ├── sync.ts         # Data synchronization
│   │       └── cache.ts        # Caching layer
│   ├── api/
│   │   └── fireflies/
│   │       └── webhook.ts      # Webhook endpoint
│   └── jobs/
│       └── fireflies/
│           └── sync.ts         # Background sync job
├── tests/
│   ├── unit/
│   │   └── fireflies/
│   └── integration/
│       └── fireflies/
├── config/
│   ├── fireflies.development.json
│   ├── fireflies.staging.json
│   └── fireflies.production.json
└── docs/
    └── fireflies/
        ├── SETUP.md
        └── RUNBOOK.md

Layer Architecture

┌─────────────────────────────────────────┐
│             API Layer                    │
│   (Controllers, Routes, Webhooks)        │
├─────────────────────────────────────────┤
│           Service Layer                  │
│  (Business Logic, Orchestration)         │
├─────────────────────────────────────────┤
│          Fireflies.ai Layer        │
│   (Client, Types, Error Handling)        │
├─────────────────────────────────────────┤
│         Infrastructure Layer             │
│    (Cache, Queue, Monitoring)            │
└─────────────────────────────────────────┘

Key Components

Step 1: Client Wrapper

// src/fireflies/client.ts
export class Fireflies.aiService {
  private client: Fireflies.aiClient;
  private cache: Cache;
  private monitor: Monitor;

  constructor(config: Fireflies.aiConfig) {
    this.client = new Fireflies.aiClient(config);
    this.cache = new Cache(config.cacheOptions);
    this.monitor = new Monitor('fireflies');
  }

  async get(id: string): Promise<Resource> {
    return this.cache.getOrFetch(id, () =>
      this.monitor.track('get', () => this.client.get(id))
    );
  }
}

Step 2: Error Boundary

// src/fireflies/errors.ts
export class Fireflies.aiServiceError extends Error {
  constructor(
    message: string,
    public readonly code: string,
    public readonly retryable: boolean,
    public readonly originalError?: Error
  ) {
    super(message);
    this.name = 'Fireflies.aiServiceError';
  }
}

export function wrapFireflies.aiError(error: unknown): Fireflies.aiServiceError {
  // Transform SDK errors to application errors
}

Step 3: Health Check

// src/fireflies/health.ts
export async function checkFireflies.aiHealth(): Promise<HealthStatus> {
  try {
    const start = Date.now();
    await firefliesClient.ping();
    return {
      status: 'healthy',
      latencyMs: Date.now() - start,
    };
  } catch (error) {
    return { status: 'unhealthy', error: error.message };
  }
}

Data Flow Diagram

User Request
     │
     ▼
┌─────────────┐
│   API       │
│   Gateway   │
└──────┬──────┘
       │
       ▼
┌─────────────┐    ┌─────────────┐
│   Service   │───▶│   Cache     │
│   Layer     │    │   (Redis)   │
└──────┬──────┘    └─────────────┘
       │
       ▼
┌─────────────┐
│ Fireflies.ai    │
│   Client    │
└──────┬──────┘
       │
       ▼
┌─────────────┐
│ Fireflies.ai    │
│   API       │
└─────────────┘

Configuration Management

// config/fireflies.ts
export interface Fireflies.aiConfig {
  apiKey: string;
  environment: 'development' | 'staging' | 'production';
  timeout: number;
  retries: number;
  cache: {
    enabled: boolean;
    ttlSeconds: number;
  };
}

export function loadFireflies.aiConfig(): Fireflies.aiConfig {
  const env = process.env.NODE_ENV || 'development';
  return require(`./fireflies.${env}.json`);
}

Instructions

Step 1: Create Directory Structure

Set up the project layout following the reference structure above.

Step 2: Implement Client Wrapper

Create the singleton client with caching and monitoring.

Step 3: Add Error Handling

Implement custom error classes for Fireflies.ai operations.

Step 4: Configure Health Checks

Add health check endpoint for Fireflies.ai connectivity.

Output

  • Structured project layout
  • Client wrapper with caching
  • Error boundary implemented
  • Health checks configured

Error Handling

IssueCauseSolution
Circular dependenciesWrong layeringSeparate concerns by layer
Config not loadingWrong pathsVerify config file locations
Type errorsMissing typesAdd Fireflies.ai types
Test isolationShared stateUse dependency injection

Examples

Quick Setup Script

# Create reference structure
mkdir -p src/fireflies/{handlers} src/services/fireflies src/api/fireflies
touch src/fireflies/{client,config,types,errors}.ts
touch src/services/fireflies/{index,sync,cache}.ts

Resources

Flagship Skills

For multi-environment setup, see fireflies-multi-env-setup.

More by jeremylongshore

View all
oauth-callback-handler
1,004

Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.

rabbitmq-queue-setup
1,004

Rabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.

model-evaluation-suite
1,004

evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".

neural-network-builder
1,004

building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").