# Reverse Prompt Engineer (`scenic_telescope/reverse-prompt-engineer`) Actor Reverse-engineer reusable image-generation prompts from reference images or visual descriptions through a protected Codex worker. - **URL**: https://apify.com/scenic\_telescope/reverse-prompt-engineer.md - **Developed by:** [ping](https://apify.com/scenic_telescope) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $190.00 / 1,000 successful reverse prompts This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Reverse Prompt Engineer Reverse-engineer reusable image-generation prompts from a reference image URL or a detailed visual description, powered by a token-protected Codex worker. This Actor is designed for creators, prompt engineers, AI image teams, and agent workflows that need visual references converted into structured Chinese/English prompts for Midjourney, Flux, Stable Diffusion, Doubao, Jimeng, DALL-E, or model-neutral use. ### What It Does - Accepts a reference image URL or visual description. - Classifies the image type: logo, typography, landscape, portrait, illustration, 3D/C4D, toy character, or mixed. - Applies the reverse-prompt-engineer workflow locally through Codex. - Returns a structured markdown prompt package. - Labels uncertain inferences instead of inventing hidden details. - Avoids protected character or living-artist names in generation prompts by converting them into visual traits. ### When To Use It Use this Actor when you need: - image-to-prompt analysis, - bilingual Chinese/English prompts, - Midjourney-style or model-neutral generation prompts, - negative prompts and recommended parameters, - an Apify API wrapper around a Codex prompt workflow. ### Input Required: - One of: - `referenceImageUrl` - `visualDescription` Optional: - `targetModel`: `model-neutral`, `midjourney`, `stable-diffusion`, `flux`, `doubao`, `jimeng`, or `dall-e`. - `imageType`: optional category hint. - `outputLanguage`: `bilingual`, `zh`, or `en`. - `includeNegativePrompt`: include a negative prompt section. - `includeParameters`: include generation parameters when useful. - `notes`: extra user intent or constraints. Example input: ```json { "visualDescription": "Chrome 3D text logo on a blue gradient background, beveled edges, glossy reflection, centered studio composition", "targetModel": "midjourney", "imageType": "typography-logo", "outputLanguage": "bilingual" } ```` ### Output The default dataset receives one item: ```json { "ok": true, "status": "completed", "chargedEvent": "reverse-prompt", "chargedCount": 1, "provider": "codex-local", "targetModel": "midjourney", "imageType": "typography-logo", "outputLanguage": "bilingual", "markdown": "### 中文拆解\n..." } ``` The `markdown` field contains: - Chinese visual breakdown - Chinese generation prompt - English prompt - Negative prompt when useful - Parameters when useful ### Pricing This Actor uses pay-per-event pricing. - Event: `reverse-prompt` - Price: `$0.19` per successful reverse prompt - Failed validation and worker errors are not charged by this custom event. ### Worker Requirement The Actor calls a protected Codex worker configured by the Actor owner through secret environment variables. Public users do not need to provide worker credentials. For local development of this project, run: ```bash npm run serve-apify-worker ``` Cloud runs require `WORKER_URL` and `WORKER_TOKEN` to be configured privately on the Actor. Secret values must never be committed to source control or pasted into issue threads. ### Security - The Actor does not accept shell commands, process settings, or executable paths from user input. - JSON keys and string values are scanned for script and command-like content. - Local filesystem paths are removed from public output. - Worker URL and token are owner-managed secrets, not user input fields. - The Codex skill runs behind a protected worker, not inside user-controlled request JSON. ### Suggested Store Metadata - Category: AI tools - Tags: `reverse-prompt`, `image-to-prompt`, `prompt-engineering`, `midjourney`, `flux`, `stable-diffusion`, `codex-local`, `a2a`, `agent` - Pricing: pay per event, `$0.19` per successful `reverse-prompt` # Actor input Schema ## `referenceImageUrl` (type: `string`): HTTPS URL for the visual reference. Use this when the local Codex worker can access the image URL. ## `visualDescription` (type: `string`): Text description of the image when no direct image URL is available. ## `targetModel` (type: `string`): Prompt format to optimize for. ## `imageType` (type: `string`): Closest visual category. Use auto unless you know the reference type. ## `outputLanguage` (type: `string`): Language format for the returned analysis and prompts. ## `includeNegativePrompt` (type: `boolean`): Include a negative prompt section when useful for the target model. ## `includeParameters` (type: `boolean`): Include recommended aspect ratio, style, quality, lens, or render parameters when useful. ## `notes` (type: `string`): Optional user intent, constraints, or model-specific notes. ## `requestTimeoutSecs` (type: `integer`): Timeout for the reverse-prompt request. ## `includeFullResult` (type: `boolean`): Include the sanitized worker response in the dataset output. Local filesystem paths are removed. ## Actor input object example ```json { "referenceImageUrl": "https://example.com/reference-image.png", "visualDescription": "Chrome 3D text logo on a blue gradient background, beveled edges, glossy reflection, centered studio composition", "targetModel": "model-neutral", "imageType": "auto", "outputLanguage": "bilingual", "includeNegativePrompt": true, "includeParameters": true, "requestTimeoutSecs": 300, "includeFullResult": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "visualDescription": "Chrome 3D text logo on a blue gradient background, beveled edges, glossy reflection, centered studio composition" }; // Run the Actor and wait for it to finish const run = await client.actor("scenic_telescope/reverse-prompt-engineer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "visualDescription": "Chrome 3D text logo on a blue gradient background, beveled edges, glossy reflection, centered studio composition" } # Run the Actor and wait for it to finish run = client.actor("scenic_telescope/reverse-prompt-engineer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "visualDescription": "Chrome 3D text logo on a blue gradient background, beveled edges, glossy reflection, centered studio composition" }' | apify call scenic_telescope/reverse-prompt-engineer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scenic_telescope/reverse-prompt-engineer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reverse Prompt Engineer", "description": "Reverse-engineer reusable image-generation prompts from reference images or visual descriptions through a protected Codex worker.", "version": "0.1", "x-build-id": "LG4SGrmI0LaTuscfE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scenic_telescope~reverse-prompt-engineer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scenic_telescope-reverse-prompt-engineer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scenic_telescope~reverse-prompt-engineer/runs": { "post": { "operationId": "runs-sync-scenic_telescope-reverse-prompt-engineer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scenic_telescope~reverse-prompt-engineer/run-sync": { "post": { "operationId": "run-sync-scenic_telescope-reverse-prompt-engineer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "referenceImageUrl": { "title": "Reference image URL", "type": "string", "description": "HTTPS URL for the visual reference. Use this when the local Codex worker can access the image URL." }, "visualDescription": { "title": "Visual description", "type": "string", "description": "Text description of the image when no direct image URL is available." }, "targetModel": { "title": "Target model", "enum": [ "model-neutral", "midjourney", "stable-diffusion", "flux", "doubao", "jimeng", "dall-e" ], "type": "string", "description": "Prompt format to optimize for.", "default": "model-neutral" }, "imageType": { "title": "Image type", "enum": [ "auto", "general", "typography-logo", "landscape-scene", "illustration-2d", "photography-portrait", "3d-c4d-render", "ip-toy-character", "mixed" ], "type": "string", "description": "Closest visual category. Use auto unless you know the reference type.", "default": "auto" }, "outputLanguage": { "title": "Output language", "enum": [ "bilingual", "zh", "en" ], "type": "string", "description": "Language format for the returned analysis and prompts.", "default": "bilingual" }, "includeNegativePrompt": { "title": "Include negative prompt", "type": "boolean", "description": "Include a negative prompt section when useful for the target model.", "default": true }, "includeParameters": { "title": "Include parameters", "type": "boolean", "description": "Include recommended aspect ratio, style, quality, lens, or render parameters when useful.", "default": true }, "notes": { "title": "Extra notes", "type": "string", "description": "Optional user intent, constraints, or model-specific notes." }, "requestTimeoutSecs": { "title": "Request timeout seconds", "minimum": 10, "maximum": 600, "type": "integer", "description": "Timeout for the reverse-prompt request.", "default": 300 }, "includeFullResult": { "title": "Include full sanitized worker result", "type": "boolean", "description": "Include the sanitized worker response in the dataset output. Local filesystem paths are removed.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```