open-index/open-npm huggingface dataset:
npm package: @prompticus/n8n-nodes-prompticus
Description: n8n node for Prompticus integration
Author: Prompticus
Latest version: 1.0.9

# n8n-nodes-prompticus

A n8n node for integrating Promptic.us

## Promptic.us

Transform your Business with AI Prompts

Are you tired of watching your competitors leverage AI while you're stuck trying to figure out where to start? You're not alone. The problem isn't AI itself.  It is knowing how to communicate with it effectively.

Promptic.us helps small businesses create and manage AI prompts.  No learning curve or technical expertise typically required.

- **Ready-to-Use Prompts:** No prompt engineering experience required
- **Small Business Focus:** Built specifically for businesses with limited resources and time
- **Industry-Specific Libraries:** Tailored solutions for your business type
- **Continuous Updates:** Stay current with the latest AI developments and prompting techniques

## More Info

[Promptic.us](https://promptic.us/)

[API Documentation](https://docs.promptic.us/)

## Installation

```bash
npm install n8n-nodes-prompticus
```

## Development

```bash
# Install dependencies
npm install

# Build the project
npm run build

# Development with watch mode
npm run dev
```

## Usage

This Node provides operations for:
- Get a List of Prompts
- Get a Prompt
- Create a Prompt
- Update a Prompt
- Delete a Prompt

## Configuration

You will need a Promptic.us Access Token (API credentials) in n8n to use this node.  Create an Account on Promptic.us to generate Access Tokens.
npm package: @promptive/sdk
Description: Drop-in logging + versioning layer for LLM prompt calls
Latest version: 0.1.0

# @promptive/sdk

Drop-in observability for LLM calls — log prompts, tokens, latency, and cost to your [Promptive](https://promptive.dev) dashboard in one line.

Works with any provider: OpenAI, Anthropic, or anything that returns a compatible response shape.

## Install

```bash
npm install @promptive/sdk
# or
pnpm add @promptive/sdk
# or
yarn add @promptive/sdk
```

## Quick start

```ts
import { Promptive } from "@promptive/sdk";
import OpenAI from "openai";

const promptive = new Promptive({ apiKey: "sk-promptive-..." });
const openai = new OpenAI();

const messages = [{ role: "user", content: "Summarise this lease." }];

const response = await promptive.wrap({
  promptId: "lease-summary",
  version:  "v1",
  input:    messages,
  call: () =>
    openai.chat.completions.create({ model: "gpt-4o", messages }),
});

// response is the raw OpenAI object — nothing changes for your code
console.log(response.choices[0].message.content);
```

### With Anthropic

```ts
import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic();

const response = await promptive.wrap({
  promptId: "lease-summary",
  version:  "v1",
  input:    [{ role: "user", content: "Summarise this lease." }],
  call: () =>
    anthropic.messages.create({
      model:      "claude-opus-4-6",
      max_tokens: 1024,
      messages:   [{ role: "user", content: "Summarise this lease." }],
    }),
});
```

## API

### `new Promptive(config)`

| Option | Type | Required | Description |
|---|---|---|---|
| `apiKey` | `string` | ✓ | Your project API key from the Promptive dashboard |
| `endpoint` | `string` | | Override the ingest URL (for self-hosting). Defaults to `https://promptive.dev` |
| `silent` | `boolean` | | Suppress console warnings when logging fails. Recommended `true` in production |

### `promptive.wrap(params)`

Executes `params.call()` normally, times it, extracts token usage, and fires a log fire-and-forget. Returns the raw LLM response unchanged.

| Param | Type | Required | Description |
|---|---|---|---|
| `promptId` | `string` | ✓ | Stable identifier for this prompt (e.g. `"lease-analysis"`) |
| `version` | `string` | ✓ | Label for this prompt draft (e.g. `"v1"`, `"2.0.0"`) |
| `call` | `() => Promise<T>` | ✓ | The LLM call to execute |
| `input` | `unknown` | | The messages array you sent — stored alongside the response |
| `metadata` | `Record<string, string \| number \| boolean>` | | Arbitrary key-value pairs (e.g. `{ userId: "u_123" }`) |

## Self-hosting

Point `endpoint` at your own deployment:

```ts
const promptive = new Promptive({
  apiKey:   "sk-promptive-...",
  endpoint: "https://your-domain.com",
});
```

## License

MIT
npm package: @promptlayer/n8n-nodes-promptlayer-runagent
Description: n8n community node for PromptLayer. Currently only handles RunAgent.
Author: PromptLayer
Latest version: 0.1.9

# PromptLayer RunAgent Node for n8n

This package provides a custom n8n node for running [PromptLayer Agents](https://promptlayer.com/) directly from your n8n workflows. The **PromptLayer RunAgent** node allows you to execute PromptLayer Agents, pass input variables, select agent versions or labels, and retrieve results programmatically.

---

## Features

- **Run any PromptLayer Agent** by name from your n8n workflow
- **Select agent version** by number or label
- **Pass input variables** as JSON
- **Attach metadata** to executions
- **Control output** (return all outputs or just the main one)
- **Timeout handling** for long-running agents
- **Credential-based authentication**

---

## Prerequisites

- [n8n](https://n8n.io/) installed (Node.js 20+ required)
- A [PromptLayer](https://promptlayer.com/) account and API key

---

## Installation

1. Clone or download this repository into your n8n custom nodes directory.
2. Install dependencies:
   ```bash
   npm install
   ```
3. Build the node:
   ```bash
   npm run build
   ```
4. [Register the custom node with n8n](https://docs.n8n.io/integrations/creating-nodes/build/node-development-environment/#load-custom-nodes) if needed.

---

## Configuration

### Setting up Credentials

1. In n8n, go to **Credentials**.
2. Create new credentials for **PromptLayerRunAgentApi**.
3. Enter your PromptLayer API key.

---

## Usage

1. Add the **PromptLayer RunAgent** node to your workflow.
2. Configure the following fields:
   - **Agent Name**: Select the name of the agent to run.
   - **Use Agent Label Name**: Toggle to use a label instead of a version number.
   - **Agent Version Number**: (Optional) Specify a version number (if not using label). By default the latest version of the agent is used.
   - **Agent Label Name**: (Optional) Specify a label name (if using label). By default the latest version of the agent is used.
   - **Input Variables**: JSON object of variables required by the agent (e.g., `{ "question": "What is the weather?" }`).
   - **Additional Fields**:
     - **Metadata**: (Optional) JSON object of metadata to attach.
     - **Return All Outputs**: (Optional) Return all outputs from the agent.
     - **Timeout**: (Optional) Maximum wait time in minutes (default: 10).
3. Connect the node as needed in your workflow.
4. Run the workflow. The node will execute the agent and return the results.

---

## Example

Suppose you have a PromptLayer agent named `weatherAgent` that takes a `location` variable:

- **Agent Name**: `weatherAgent`
- **Input Variables**: `{ "location": "London" }`
- **Return All Outputs**: `false`

The node will execute the agent and return the weather information for London.

---

## Troubleshooting

- Ensure your PromptLayer API key is correct and has access to the agent.
- Input variables and metadata must be valid JSON.
- If the agent takes too long, increase the timeout in Additional Fields.
npm package: @promptlens/sdk
Description: Terminal-ready SDK and CLI for PromptLens explainable prompt analysis.
Latest version: 0.1.0

# PromptLens SDK

Terminal-ready SDK and CLI for PromptLens explainable prompt analysis.

## Install Locally

```bash
cd sdk
npm link
```

After linking, the `promptlens` command is available in your terminal.

## Publish To npm

The package is prepared for public npm release.

```bash
cd sdk
npm run check
npm publish --access public --otp=123456
```

Full maintainer steps are in `sdk/PUBLISHING.md`.

## CLI Usage

```bash
promptlens health --api-url http://127.0.0.1:8000
promptlens analyse "A cinematic lab at night with reflective glass" --mode text
promptlens analyse "A cinematic lab at night with reflective glass" --mode both --json
promptlens metrics
```

The CLI stores its anonymous session state in `~/.promptlens/sdk-config.json` so repeat runs reuse the same PromptLens session.

## JavaScript Usage

```js
import { PromptLensClient } from "@promptlens/sdk";

const client = new PromptLensClient({
  apiUrl: "http://127.0.0.1:8000",
});

const result = await client.analyse("A cinematic lab at night", {
  mode: "text",
});

console.log(result.segments);
console.log(result.text?.output);
```

## Included Commands

- `promptlens analyse` segments a prompt and returns explainable generation output.
- `promptlens health` checks that the backend is reachable.
- `promptlens metrics` prints the backend summary metrics.

## Notes

- Node `18+` is required because the SDK uses native `fetch`.
- Before public release, the CLI works immediately via `npm link`.
npm package: @promptline/sdk
Description: Official SDK for building PromptLine applications
Author: PromptLine
Latest version: 0.1.1

# @promptline/sdk

Official SDK for building PromptLine applications.

## Installation

```bash
npm install @promptline/sdk
```

## Usage

### Vanilla JavaScript/TypeScript

```typescript
import { createClient } from '@promptline/sdk'

const client = createClient({
  apiKey: 'your-api-key'
})

// Initialize the client
await client.init()

// Execute an AI binding
const result = await client.execute('review_analyzer', {
  variables: { review_text: 'Great product!' }
})

console.log(result.content)
```

### React

```tsx
import { PromptLineProvider, usePromptLine } from '@promptline/sdk/react'

// Wrap your app with the provider
function App() {
  return (
    <PromptLineProvider
      config={{ apiKey: process.env.PROMPTLINE_API_KEY! }}
      loading={<div>Loading...</div>}
    >
      <MyComponent />
    </PromptLineProvider>
  )
}

// Use the hook in your components
function MyComponent() {
  const { execute, content, isLoading, error } = usePromptLine({
    binding: 'review_analyzer',
    stream: true
  })

  const handleClick = async () => {
    await execute({ review_text: 'Great product!' })
  }

  return (
    <div>
      <button onClick={handleClick} disabled={isLoading}>
        Analyze
      </button>
      {content && <p>{content}</p>}
      {error && <p>Error: {error.message}</p>}
    </div>
  )
}
```

## API Reference

### `createClient(config)`

Creates a new PromptLine client.

- `config.apiKey` (required): Your PromptLine API key
- `config.baseUrl` (optional): Custom API base URL
- `config.timeout` (optional): Request timeout in milliseconds

### `client.init()`

Initializes the client and fetches instance configuration. Must be called before using other methods.

### `client.execute(binding, options, signal?)`

Executes an AI binding.

- `binding`: The binding key
- `options.variables`: Variables to pass to the binding
- `options.provider`: Override the AI provider
- `options.model`: Override the model
- `signal`: Optional AbortSignal for cancellation

### `client.executeStream(binding, options, onChunk, signal?)`

Executes an AI binding with streaming.

- `onChunk`: Callback function called for each chunk

### React Hooks

- `usePromptLine(options)`: Main hook for executing bindings
- `usePromptLineContext()`: Access the PromptLine context
- `usePromptLineClient()`: Get the client instance directly

## License

MIT
npm package: @promptly-tools/service-utils
Description: Shared utilities for Promptly services including authentication, logging, and common helpers
Author: Promptly Team
Latest version: 1.0.0-3f2909e

# @promptly/service-utils

Shared utilities for Promptly services including authentication, logging, and common helpers.

## Installation

```bash
npm install @promptly/service-utils
```

## Features

- **Authentication**: Clerk-based JWT authentication middleware
- **Logging**: Structured logging with Winston
- **Common Utilities**: Retry logic, environment variable parsing, sanitization

## Usage

### Authentication

```typescript
import { authMiddleware, optionalAuthMiddleware } from '@promptly/service-utils';
import express from 'express';

const app = express();

// Required authentication for all routes under /api
app.use('/api', authMiddleware);

// Optional authentication (doesn't fail if no token)
app.use('/public', optionalAuthMiddleware);
```

### Logging

```typescript
import { logger, logRequest, logResponse } from '@promptly/service-utils';

// Basic logging
logger.info('Service started');
logger.error('Something went wrong', { error: 'details' });

// Request/response logging
app.use((req, res, next) => {
  const start = Date.now();
  logRequest(req);

  res.on('finish', () => {
    const duration = Date.now() - start;
    logResponse(req, res, duration);
  });

  next();
});
```

### Common Utilities

```typescript
import {
  sleep,
  retryWithBackoff,
  validateEnvironmentVariables,
  parseBooleanEnv,
  parseNumberEnv,
} from '@promptly/service-utils';

// Environment validation
validateEnvironmentVariables(['DATABASE_URL', 'API_KEY']);

// Environment parsing
const isProduction = parseBooleanEnv('IS_PRODUCTION', false);
const port = parseNumberEnv('PORT', 3000);

// Retry with backoff
const result = await retryWithBackoff(
  async () => {
    return await someApiCall();
  },
  3,
  1000
);
```

## Environment Variables

### Required for Authentication

- `CLERK_SECRET_KEY`: Clerk secret key for JWT validation
- `CLERK_PUBLISHABLE_KEY`: Clerk publishable key

### Optional

- `LOG_LEVEL`: Logging level (default: 'info')
- `SERVICE_NAME`: Service name for logging context
- `NODE_ENV`: Environment (affects logging format)

## TypeScript Support

This package includes full TypeScript definitions. The main types exported are:

```typescript
import type { UserContext, AuthenticatedRequest } from '@promptly/service-utils';

// UserContext is available on req.user after authentication
interface UserContext {
  userId: string;
  email: string;
  roles: string[];
}
```

## Development

```bash
# Install dependencies
pnpm install

# Build the package
pnpm build

# Watch for changes during development
pnpm dev
```

## Publishing

```bash
# Bump version and publish
pnpm deploy
```

## License

MIT
npm package: @promptly/k8s
Description: A simple Kubernetes client library
Author: Ray Zane
Latest version: 2.2.0

# @promptly/k8s

A simple Kubernetes client for Node.js based on [`kubernetes-client`](https://github.com/godaddy/kubernetes-client).

Because some functionality isn't yet supported by  `kubernetes-client`, this package also provides convenience methods for shelling out to `kubectl`.

## Installing

    $ yarn add @promptly/k8s

## Usage

```typescript
import { Kubernetes, Kubectl } from '@promptly/k8s';

// This is a wrapper around `kubernetes-client`
const kubernetes = new Kubernetes();

// This is a wrapper around `kubectl`
const kubectl = new Kubectl();
```
npm package: @promptnroll/dotfig
Description: One config to rule them all — generate ESLint, Prettier, TypeScript, EditorConfig from a single dotfig.yml
Author: promptnroll
Latest version: 0.1.1

# dotfig

> 一个配置统治一切 — 通过 `dotfig.yml` 自动生成 ESLint、Prettier、TypeScript、EditorConfig

[English](./README.md) | [한국어](./README.ko.md) | [日本語](./README.ja.md)

## 为什么选择 dotfig？

每个新项目都需要 5-10 个配置文件：`eslint.config.js`、`.prettierrc`、`tsconfig.json`、`.editorconfig`、`.gitignore`…… 它们共享相同的设置（缩进大小、引号、分号），但每个都使用不同的格式。

**dotfig** 让你在 `dotfig.yml` 中只定义一次偏好设置，自动生成所有配置文件。

```
dotfig.yml  →  eslint.config.js
             →  .prettierrc
             →  tsconfig.json
             →  .editorconfig
             →  .gitignore
```

## 安装

```bash
npm install -g dotfig
```

## 快速开始

```bash
# 交互式设置 — 创建 dotfig.yml
dotfig init

# 生成所有配置文件
dotfig sync
```

## dotfig.yml

```yaml
version: 1
preset: typescript          # typescript | react | node

rules:                      # 跨工具共享
  indent: 2                 # → prettier, editorconfig, eslint
  semicolons: false         # → prettier, eslint
  quotes: single            # → prettier, eslint
  line-width: 100           # → prettier, editorconfig, eslint

typescript:                 # tsconfig.json 选项
  strict: true
  target: ES2024
  jsx: react-jsx            # react 预设自动配置
  paths:
    "@/*": ["./src/*"]

lint:                       # 额外 ESLint 规则
  no-unused-vars: error
  no-console: warn

format:                     # 额外 Prettier 选项
  trailing-comma: all
  bracket-spacing: true

gitignore:                  # 额外 .gitignore 模式
  extra:
    - "*.local"
    - ".env"
```

## 核心原理

`rules` 部分是关键。只需定义一次，自动应用到所有工具：

| dotfig.yml | Prettier | ESLint | EditorConfig |
|------------|----------|--------|--------------|
| `indent: 2` | `tabWidth: 2` | `indent` 规则 | `indent_size = 2` |
| `semicolons: false` | `semi: false` | `semi` 规则 | — |
| `quotes: single` | `singleQuote: true` | `quotes` 规则 | — |
| `line-width: 100` | `printWidth: 100` | `max-len` 规则 | `max_line_length = 100` |

## 生成的文件

| 输入 | 输出 |
|------|------|
| `rules` + `format` | `.prettierrc` |
| `rules` + `lint` | `eslint.config.js`（flat config） |
| `rules` + `typescript` | `tsconfig.json` |
| `rules` | `.editorconfig` |
| `preset` + `gitignore` | `.gitignore` |

## 预设

预设提供合理的默认值。`dotfig.yml` 中的显式值始终优先。

| 预设 | 描述 |
|------|------|
| `typescript` | 严格 TypeScript + ESM + Prettier 默认配置 |
| `react` | TypeScript + JSX + React ESLint 插件 |
| `node` | TypeScript + Node.js 目标 |

## 命令

### `dotfig init`

交互式向导，创建 `dotfig.yml`：

- 选择预设（typescript / react / node）
- 配置缩进大小、分号、引号风格、行宽

### `dotfig sync`

读取 `dotfig.yml`，在当前目录生成所有配置文件。

```
$ dotfig sync
✓ 生成 .editorconfig
✓ 生成 .gitignore
✓ 生成 .prettierrc
✓ 生成 tsconfig.json
✓ 生成 eslint.config.js

✓ 5个配置文件已生成。
```

## 许可证

MIT
npm package: @promptordie/xmcp
Description: Extended Model Context Protocol servers for ElizaOS integration
Author: promptordie
Latest version: 1.0.0

# @promptordie/xmcp

Extended Model Context Protocol (MCP) servers for ElizaOS integration.

## Installation

```bash
npm install @promptordie/xmcp
```

## Twitter MCP Server

The Twitter MCP server provides a complete interface for interacting with Twitter/X through the Model Context Protocol.

### Features

- Post tweets and replies
- Like and retweet posts
- Search tweets
- Get timeline and mentions
- Follow/unfollow users
- Get user information

### Configuration

Set the following environment variables:

```bash
# Required
TWITTER_API_KEY=your_api_key
TWITTER_API_SECRET=your_api_secret

# For user authentication (required for most operations)
TWITTER_ACCESS_TOKEN=your_access_token
TWITTER_ACCESS_SECRET=your_access_secret

# Alternative: For app-only authentication
TWITTER_BEARER_TOKEN=your_bearer_token
```

### Usage

#### As a standalone server

```bash
npx xmcp-twitter
```

#### In ElizaOS

```javascript
import { createTwitterServer } from '@promptordie/xmcp';

const twitterServer = createTwitterServer({
  appKey: process.env.TWITTER_API_KEY,
  appSecret: process.env.TWITTER_API_SECRET,
  accessToken: process.env.TWITTER_ACCESS_TOKEN,
  accessSecret: process.env.TWITTER_ACCESS_SECRET
});

// Use with ElizaOS MCP plugin
```

### Available Tools

- `post_tweet` - Post a new tweet
- `reply_to_tweet` - Reply to an existing tweet
- `like_tweet` - Like a tweet
- `retweet` - Retweet a tweet
- `search_tweets` - Search for tweets
- `get_timeline` - Get home timeline
- `get_mentions` - Get mentions timeline
- `follow_user` - Follow a user
- `unfollow_user` - Unfollow a user
- `get_user_info` - Get information about a user

## License

MIT

## Author

promptordie
npm package: @promptot/mcp
Description: Reserved placeholder — use @prompt-ot/mcp instead.
Latest version: 0.0.1

# @promptot/mcp

Reserved placeholder. The real PromptOT MCP server is published at [`@prompt-ot/mcp`](https://www.npmjs.com/package/@prompt-ot/mcp) (note the hyphen in the scope: `@prompt-ot`, not `@promptot`).

Install the real package with:

```bash
npx -y @prompt-ot/mcp
```

Docs: [promptot.com/mcp](https://www.promptot.com/mcp)
npm package: @promptrails/n8n-nodes-promptrails
Description: PromptRails nodes for n8n — execute agents, run prompts, manage chat sessions, and more
Author: PromptRails
Latest version: 0.2.0

# n8n-nodes-promptrails

[n8n](https://n8n.io/) community node for [PromptRails](https://promptrails.com) — execute AI agents, run prompts, manage chat sessions, query data sources, and more from your n8n workflows.

## Installation

In your n8n instance:

1. Go to **Settings > Community Nodes**
2. Select **Install a community node**
3. Enter `@promptrails/n8n-nodes-promptrails`
4. Click **Install**

## Credentials

You need a PromptRails API key to use this node:

1. Log in to [PromptRails](https://promptrails.com)
2. Go to **Settings > API Keys**
3. Create a new API key with the required scopes
4. In n8n, create a new **PromptRails API** credential with your API key and workspace ID

## Resources & Operations

| Resource | Operations |
|----------|------------|
| **Agent** | Execute, Preview, Get, List |
| **Prompt** | Run, Get, List |
| **Chat** | Create Session, Send Message, Get Session, List Sessions, List Messages |
| **Execution** | Get, List |
| **Data Source** | Query, Get, List |
| **Trace** | Get, List, Get Summary |
| **Cost** | Workspace Summary, Agent Summary |

### Agent

- **Execute** — Run an agent with input variables and get the result
- **Preview** — Test a specific agent version before promoting
- **Get** — Retrieve agent details by ID
- **List** — List all agents in the workspace

### Prompt

- **Run** — Execute a prompt template with variables
- **Get** — Retrieve prompt details by ID
- **List** — List all prompts in the workspace

### Chat

- **Create Session** — Start a new multi-turn chat session with an agent
- **Send Message** — Send a message in an existing chat session
- **Get Session** — Retrieve session details
- **List Sessions** — List all chat sessions
- **List Messages** — Retrieve messages from a session

### Data Source

- **Query** — Execute a query against a connected data source
- **Get** — Retrieve data source details
- **List** — List all data sources

### Execution

- **Get** — Retrieve execution details and results
- **List** — List execution history

### Trace

- **Get** — Retrieve a full execution trace with spans
- **List** — List recent traces
- **Get Summary** — Get aggregated trace statistics

### Cost

- **Workspace Summary** — Total LLM usage costs for the workspace
- **Agent Summary** — Cost breakdown for a specific agent

## Example Workflows

### Execute an Agent on Schedule

1. **Schedule Trigger** — Run every hour
2. **PromptRails** — Execute agent with `{ "report_type": "hourly" }`
3. **Slack** — Post the result to a channel

### Chat with Webhook Input

1. **Webhook** — Receive incoming messages
2. **PromptRails** — Send message to a chat session
3. **Respond to Webhook** — Return the agent's response

### Monitor Costs

1. **Schedule Trigger** — Run daily
2. **PromptRails** — Get workspace cost summary
3. **IF** — Check if costs exceed threshold
4. **Email** — Send alert if over budget

## Development

```bash
npm install
npm run build
npm run dev    # watch mode
```

## License

MIT
npm package: @promptrails/skills
Description: AI coding assistant skills for PromptRails — the AI agent orchestration platform
Author: PromptRails Inc
Latest version: 1.0.0

# PromptRails Skills

AI coding assistant skills for [PromptRails](https://promptrails.ai) — the AI agent orchestration platform.

These skills teach AI coding assistants (Claude Code, Cursor, Windsurf, etc.) how to work with PromptRails SDKs, CLI, agents, prompts, tracing, and more.

## Installation

### Cursor

```
/add-plugin promptrails/skills
```

### Skills CLI

```bash
npx skills add promptrails/skills --skill "promptrails"
```

### Claude Code

Add to your project's `.claude/settings.json`:

```json
{
  "skills": ["path/to/skills/skills/promptrails/SKILL.md"]
}
```

Or symlink into your Claude Code skills directory.

### Manual

Clone this repository and point your AI assistant's configuration to the `skills/promptrails/SKILL.md` file.

## What's Included

The PromptRails skill teaches your AI assistant:

- **SDK Usage** — Python (`promptrails`), JavaScript (`@promptrails/sdk`), and Go (`github.com/promptrails/go-sdk`) SDK patterns
- **CLI Commands** — All `promptrails` CLI commands for agents, prompts, executions, credentials, and more
- **Agent Management** — Creating, configuring, versioning, and executing agents (simple, chain, multi-agent, workflow, composite)
- **Prompt Engineering** — Jinja2 templating, versioning, model assignment, and caching
- **Tracing & Observability** — 18 span kinds, cost tracking, error debugging
- **MCP Tools** — External tool integration via Model Context Protocol
- **Data Sources** — Database connections with parameterized queries

## Structure

```
skills/
└── promptrails/
    ├── SKILL.md              # Main skill definition
    └── references/
        ├── cli.md            # CLI reference
        ├── python-sdk.md     # Python SDK reference
        ├── javascript-sdk.md # JavaScript/TypeScript SDK reference
        ├── go-sdk.md         # Go SDK reference
        ├── agents.md         # Agents guide
        ├── prompts.md        # Prompts guide
        ├── tracing.md        # Tracing guide
        ├── mcp-tools.md      # MCP tools guide
        └── data-sources.md   # Data sources guide
.cursor-plugin/
└── plugin.json               # Cursor IDE plugin config
```

## Documentation

Full PromptRails documentation is available at [promptrails.ai/docs](https://promptrails.ai/docs).

## License

MIT
npm package: @promptranks/questions-mcp-server
Description: MCP server for PromptRanks question generation workflow
Author: PromptRanks Team
Latest version: 2.0.0

# PromptRanks Questions MCP Server

MCP server for PromptRanks question generation workflow. Provides tools to create and manage industries and roles in the PromptRanks database.

## Installation

```bash
npm install -g @promptranks/questions-mcp-server
```

## Configuration

### 1. Get your API key

Visit https://promptranks.org/mcp-api-key and request an API key. You'll receive it via email.

### 2. Save your API key

Create `~/.promptranks/mcp_api_key.json`:

```json
{
  "key": "mcp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
}
```

### 3. Add to Claude Code

Add to your `~/.claude.json`:

```json
{
  "mcpServers": {
    "prk-questions": {
      "command": "npx",
      "args": ["@promptranks/questions-mcp-server"]
    }
  }
}
```

Or use the CLI:

```bash
claude mcp add-json prk-questions '{"command":"npx","args":["@promptranks/questions-mcp-server"]}'
```

## Tools

### get_or_create_industry

Get or create an industry by slug.

**Parameters:**
- `name` (string): Industry name
- `slug` (string): URL-friendly slug
- `description` (string): Industry description

**Returns:**
- `id` (string): Database UUID
- `existed` (boolean): Whether industry already existed

### get_or_create_role

Get or create a role by slug and industry.

**Parameters:**
- `name` (string): Role name
- `slug` (string): URL-friendly slug
- `industry_id` (string): Industry UUID
- `responsibilities` (array): List of responsibilities

**Returns:**
- `id` (string): Database UUID
- `existed` (boolean): Whether role already existed

## License

MIT
npm package: @promptwatch/analytics
Description: Simple loader for Promptwatch analytics client for React/Angular apps.
Latest version: 0.1.0

# @promptwatch/analytics

[Promptwatch](https://promptwatch.com) is the platform for tracking AI search visibility and the largest GEO platform. This package provides the analytics script to track AI search traffic from ChatGPT and other apps.

Simple loader for the Promptwatch analytics client. Adds the following script to your app:

```html
<script>
(function() {
  var script = document.createElement('script');
  script.setAttribute('data-project-id', 'YOUR_PROJECT_ID');
  script.src = 'https://ingest.promptwatch.com/js/client.min.js';
  document.head.appendChild(script);
})();
</script>
```

## About Promptwatch

[Promptwatch](https://promptwatch.com) helps brands understand and improve how AI assistants reference their content. This package is a tiny loader for the Promptwatch analytics script.

## Install

```bash
npm install @promptwatch/analytics
# or
yarn add @promptwatch/analytics
# or
pnpm add @promptwatch/analytics
```

## Usage

### Vanilla JS / any framework

```ts
import { loadPromptwatch } from '@promptwatch/analytics';

loadPromptwatch('YOUR_PROJECT_ID');
```

### React

```tsx
import { PromptwatchScript } from '@promptwatch/analytics/react';

export default function App() {
  return (
    <>
      <PromptwatchScript projectId="YOUR_PROJECT_ID" />
      {/* rest of your app */}
    </>
  );
}
```

For Next.js App Router, you can also call in a client component or layout:

```tsx
// app/layout.tsx (Client Component)
'use client';
import { PromptwatchScript } from '@promptwatch/analytics/react';

export default function RootLayout({ children }: { children: React.ReactNode }) {
  return (
    <html lang="en">
      <body>
        <PromptwatchScript projectId="YOUR_PROJECT_ID" />
        {children}
      </body>
    </html>
  );
}
```

### Angular

```ts
// app.component.ts
import { Component, AfterViewInit } from '@angular/core';
import { loadPromptwatch } from '@promptwatch/analytics';

@Component({
  selector: 'app-root',
  templateUrl: './app.component.html',
})
export class AppComponent implements AfterViewInit {
  ngAfterViewInit(): void {
    loadPromptwatch('YOUR_PROJECT_ID');
  }
}
```

## Options

```ts
loadPromptwatch('YOUR_PROJECT_ID', {
  scriptUrl: 'https://ingest.promptwatch.com/js/client.min.js',
});
```

## License

MIT
npm package: @promptweave/core
Description: The Git for Prompts — Open-source AI Prompt Engineering Platform
Latest version: 0.0.1

# PromptWeave

> **The Git for Prompts** — Manage, test, and deploy AI prompts across multiple models.

PromptWeave is an open-source **AI Prompt Engineering Platform** that helps development teams manage the full lifecycle of AI prompts: authoring, versioning, testing, evaluation, and deployment.

## Why PromptWeave?

As LLMs multiply (GPT, Claude, Gemini, DeepSeek, Qwen...), teams face a new challenge: managing hundreds of prompts across multiple models, environments, and experiments.

PromptWeave solves this with:

- **Version Control** — local/test/prod three-stage versioning (like Git branches for prompts)
- **MetaPrompt** — Define once, generate N variants for different models/platforms/experiments
- **Multi-Environment Sync** — Pull/Push with pluggable adapters to sync with any business system
- **Playground** — Test prompts against multiple models side-by-side *(v1.1)*
- **Dataset & Evaluation** — Batch testing with scoring and regression detection *(v1.2)*
- **Prompt Workflow** — DAG-based prompt chain orchestration *(v2.0)*

## Quick Start

```bash
docker compose up
```

> Coming soon. PromptWeave is under active development.

## Tech Stack

| Layer | Technology |
|-------|------------|
| Backend | NestJS 11 + Fastify + TypeScript |
| ORM | Prisma (PostgreSQL) |
| Validation | Zod v3 + nestjs-zod |
| Frontend | React 19 + Ant Design 6 |
| Testing | Vitest |
| Package Manager | pnpm |

## Roadmap

| Version | Features | Status |
|---------|----------|--------|
| v1.0 | Prompt/Snippet/Provider CRUD + Versioning + Sync + MetaPrompt | In Progress |
| v1.1 | Playground + Dataset + LCA Context Engine | Planned |
| v1.2 | Batch Evaluation + A/B Comparison + Regression Testing | Planned |
| v2.0 | Prompt Workflow (DAG orchestration, inspired by n8n) | Planned |

## Unique Features

### MetaPrompt Batch Variants

Define a base prompt and generate variants for every model/platform/experiment combination with one click. No competitor offers this.

### LCA Smart Context (v1.1)

Powered by [llm-context-attention](https://github.com/me-tool/llm-context-attention) — topic-aware context routing that saves ~50% tokens in multi-turn Playground conversations.

### SyncAdapter Pattern

Pluggable adapters to sync prompt configurations with any business system via Pull/Push. Not locked into any specific SDK.

## Contributing

Contributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for details.

## License

[MIT](LICENSE)
npm package: @promptwise/promptwiz-js
Description: Promptwiz-js is a server-side TypeScript/JavaScript library for prompting large language models (LLMs) using a shared interface compatible with the Promptwise service. `npm install @promptwise/promptwiz-js`
Author: Aaron Goin
Latest version: 0.4.12

# promptwiz-js
[![license](https://img.shields.io/github/license/promptwise/promptwiz-js)](https://github.com/promptwise/promptwiz-js/blob/main/LICENSE)
[![npm](https://img.shields.io/npm/v/@promptwise/promptwiz-js)](https://npmjs.com/package/@promptwise/promptwiz-js)
[![issues](https://img.shields.io/github/issues/promptwise/promptwiz-js)](https://github.com/promptwise/promptwiz-js/issues)

Promptwiz-js is a server-side TypeScript/JavaScript library for prompting large language models (LLMs) using a shared interface compatible with the Promptwise service, though it does not require the use of Promptwise.


---


```
npm install --save @promptwise/promptwiz-js
```

## Documentation

Documentation and live examples can be found [here](https://promptwise.github.io/promptwiz-js/).


## Acknowledgements

dqdb (David Duong) For much of original code in `tiktoken.ts` was pulled from his openai/tiktoken fork: https://github.com/dqbd/tiktoken
All the original authors of https://github.com/openai/tiktoken as well as a great many others whose work we all build on.

## License

MIT License

Copyright (c) 2023 Promptwise, All rights reserved.