# Script Architect (`moulds.ai/script-generation`) Actor AI-powered script writer for TikTok, Instagram Reels & YouTube Shorts. Generates viral short-form video scripts with hooks, scene breakdowns & captions — powered by Gemini 2.0 Flash + LangGraph. - **URL**: https://apify.com/moulds.ai/script-generation.md - **Developed by:** [Rahul Agarwal](https://apify.com/moulds.ai) (community) - **Categories:** AI, Agents, Social media - **Stats:** 9 total users, 0 monthly users, 25.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 script generateds 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 ## 🎬 Script Architect AI agent that researches trending short-form content and generates structured video scripts with scene-by-scene visual prompts for **Instagram Reels**, **TikTok**, and **YouTube Shorts**. Built on [Apify](https://apify.com/) + [LangGraph](https://langchain-ai.github.io/langgraph/) + [Gemini 2.0 Flash](https://ai.google.dev/). --- ### How It Works 1. **Scrapes trending content** for your topic via Apify sub-actors (TikTok/Instagram) 2. **Analyzes patterns** — hook styles, engagement metrics, hashtag clusters, video durations 3. **Generates a structured script** with narrative arc enforcement, timed scenes, and AI-ready visual prompts 4. **Validates everything** — Pydantic cross-validators enforce duration math, scene sequencing, and visual prompt quality --- ### Input | Field | Type | Required | Default | Description | |---|---|---|---|---| | `topic` | string | ✅ | — | A specific content angle (10–200 chars). Bad: `"AI"`. Good: `"Why AI tools make junior devs slower before they make them faster"` | | `targetAudience` | string | | null | Who is this for? Include role + pain point. Example: `"Junior devs overwhelmed by AI coding assistants"` | | `contentGoal` | enum | | `awareness` | `awareness` · `education` · `conversion` · `entertainment` · `thought_leadership` | | `platform` | enum | | `INSTAGRAM_REEL` | `INSTAGRAM_REEL` · `TIKTOK` · `YOUTUBE_SHORT` | | `targetDuration` | integer | | `45` | Target duration in seconds (15–60) | | `tone` | enum | | `contrarian` | `contrarian` · `storyteller` · `insider` · `skeptic` · `teacher` · `agitator` · `auto` | | `hookStyle` | enum | | `auto` | `contradiction` · `stat` · `story` · `question` · `bold_claim` · `curiosity_gap` · `auto` | | `visualStyle` | enum | | `auto` | `talking_head` · `b_roll_cinematic` · `screen_recording` · `motion_graphics` · `mixed` · `auto` | | `referenceCreators` | array | | `[]` | Up to 3 Instagram/TikTok usernames to study (e.g. `["hubermanlab", "garyvee"]`) | | `brandVoice` | string | | null | 1–2 sentence voice description. Example: `"Direct and technical, like a senior engineer mentoring over coffee"` | | `avoidTopics` | array | | `[]` | Topics to never mention (e.g. `["competitor names", "pricing"]`) | | `contentPurpose` | enum | | `standalone` | `standalone` · `series_episode` · `repurposed_longform` | | `outputLanguage` | string | | `en` | ISO 639-1 language code | | `debug` | boolean | | `false` | Verbose logging + saves prompt/output to key-value store | #### Example Input ```json { "topic": "Why AI tools make junior devs slower before they make them faster", "targetAudience": "Junior developers overwhelmed by AI coding assistants", "contentGoal": "thought_leadership", "platform": "TIKTOK", "targetDuration": 45, "tone": "contrarian", "hookStyle": "bold_claim", "visualStyle": "b_roll_cinematic", "brandVoice": "Direct and technical but accessible. Like a senior engineer mentoring over coffee.", "avoidTopics": ["specific tool names", "pricing"] } ```` *** ### Output The agent produces a structured JSON script pushed to the Apify dataset: | Field | Type | Description | |---|---|---| | `hook` | string | Opening line for the first 3 seconds | | `hook_type` | enum | Hook formula used (`bold_claim`, `question`, `contradiction`, etc.) | | `scenes` | array | Scene-by-scene breakdown (see below) | | `total_duration` | float | Total script duration in seconds | | `platform` | enum | Target platform | | `visual_style` | string | Global visual treatment applied to all scenes | | `tone_used` | enum | Tone archetype applied | | `content_strategy` | string | One-line strategy summary | | `trending_patterns_used` | array | Patterns borrowed from trend research | | `cta` | string | Call-to-action text | | `music_mood` | string | Suggested music mood (optional) | #### Scene Structure Each scene in the `scenes` array contains: | Field | Type | Description | |---|---|---| | `id` | int | Sequential scene number (1, 2, 3, ...) | | `role` | enum | Narrative function: `hook` · `context` · `conflict` · `revelation` · `cta` | | `text` | string | Spoken/displayed script text (10–200 chars) | | `visual_prompt` | string | AI image/video generation prompt (60–300 chars) — includes subject, environment, lighting, camera angle, and 9:16 framing | | `negative_prompt` | string | What to exclude from visuals (optional) | | `duration_target` | float | Scene duration in seconds (2–10s) | #### Example Output ```json { "hook": "AI makes you slower.", "hook_type": "bold_claim", "scenes": [ { "id": 1, "role": "hook", "text": "AI makes you slower.", "visual_prompt": "Close-up of hands struggling to type, surrounded by error messages, harsh red lighting, shallow depth of field, cinematic, 9:16 vertical frame, mobile-optimized composition", "negative_prompt": null, "duration_target": 3.0 }, { "id": 2, "role": "context", "text": "At first, anyway.", "visual_prompt": "Abstract animation of AI learning, lines of code forming and reforming, dark background with bright accents, futuristic, 9:16 vertical frame, mobile-optimized composition", "negative_prompt": null, "duration_target": 4.0 }, { "id": 3, "role": "conflict", "text": "...but no understanding.", "visual_prompt": "Junior dev staring blankly at code, confused expression, face illuminated by screen, desaturated colors, 9:16 vertical frame, mobile-optimized composition", "negative_prompt": null, "duration_target": 6.0 }, { "id": 4, "role": "revelation", "text": "Learn fundamentals first.", "visual_prompt": "Hands methodically writing code, syntax highlighting, warm lighting, focused, 9:16 vertical frame, mobile-optimized composition", "negative_prompt": null, "duration_target": 6.0 }, { "id": 5, "role": "cta", "text": "Agree or disagree?", "visual_prompt": "Developer leaning back, thoughtful expression, screen reflecting, soft lighting, 9:16 vertical frame, mobile-optimized composition", "negative_prompt": null, "duration_target": 6.0 } ], "total_duration": 44.0, "platform": "TIKTOK", "visual_style": "Dark, moody cinematic B-roll footage. Focus on hands coding, screens glowing, and abstract algorithms. Grainy, desaturated color grading, 9:16 vertical frame", "tone_used": "contrarian", "content_strategy": "Challenge the idea that AI is always beneficial for junior devs.", "trending_patterns_used": [ "Contrarian viewpoint to spark debate", "Problem-Agitate-Solve narrative structure" ], "cta": "Agree or disagree?", "music_mood": null } ``` *** ### Validation Rules The agent enforces these constraints on every output: - ✅ **Narrative arc**: First scene = `hook`, last scene = `cta`, at least one `conflict` or `revelation` in between - ✅ **Duration math**: `total_duration` must match sum of scene durations (±3s) - ✅ **Scene IDs**: Must be unique and sequential `[1, 2, 3, ...]` - ✅ **Visual prompts**: 60+ characters with subject, lighting, camera angle, and 9:16 framing - ✅ **Target duration**: Output checked against your `targetDuration` input (±5s tolerance) - ✅ **Speaking rate**: Scene text validated against duration at language-appropriate words-per-minute *** ### Running Locally 1. Set your Google API key: ```bash export GOOGLE_API_KEY=your_key_here ``` 2. Run with the test input: ```bash apify run ``` The default input is in `storage/key_value_stores/default/INPUT.json`. *** ### Deploying to Apify 1. Push to Apify: ```bash apify push ``` 2. Set `GOOGLE_API_KEY` in the actor's **Environment Variables** on [Apify Console](https://console.apify.com/). 3. Run from the Console or via API. *** ### Pricing (Pay-Per-Event) | Event | Price | Trigger | |---|---|---| | Script Generated | $0.05 | Per complete script pushed to dataset | | Trending Research | $0.02 | Per trending content search | | Creator Analysis | $0.03 | Per creator profile analyzed | *** ### Tech Stack - **Runtime**: Python 3.14 on Apify - **LLM**: Google Gemini 2.0 Flash - **Agent Framework**: LangGraph (ReAct pattern) - **Schema Validation**: Pydantic v2 - **Data Sources**: Apify sub-actors (`clockworks/tiktok-scraper`, `apify/instagram-hashtag-scraper`, `apify/instagram-scraper`) # Actor input Schema ## `topic` (type: `string`): A specific content angle, not just a category. Bad: 'AI'. Good: 'Why AI tools make junior devs slower before they make them faster'. ## `targetAudience` (type: `string`): Who is this video for? Include role, seniority, and pain point. Example: 'Series A startup CTOs who ship fast but feel the codebase slowing them down'. ## `contentGoal` (type: `string`): Primary goal — drives narrative arc and CTA strategy. ## `platform` (type: `string`): Target short-form video platform. Controls pacing, duration, and visual treatment. ## `targetDuration` (type: `integer`): Target script duration in seconds. The agent will aim to hit this within ±5s. ## `tone` (type: `string`): Voice archetype for the script. Each produces a meaningfully different output. ## `hookStyle` (type: `string`): Opening hook formula. 'auto' lets the agent choose based on trend analysis. ## `visualStyle` (type: `string`): Visual treatment for all scenes. Controls how visual\_prompts are written. ## `referenceCreators` (type: `array`): Instagram/TikTok usernames of creators whose style to study. The agent will analyze their profiles. Example: hubermanlab, garyvee ## `avoidTopics` (type: `array`): Topics or entities to never mention in the script. ## Actor input object example ```json { "topic": "The hidden cost of technical debt that CFOs never see on any report", "contentGoal": "awareness", "platform": "INSTAGRAM_REEL", "targetDuration": 45, "tone": "contrarian", "hookStyle": "auto", "visualStyle": "auto" } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "topic": "The hidden cost of technical debt that CFOs never see on any report", "contentGoal": "awareness", "platform": "INSTAGRAM_REEL", "targetDuration": 45, "tone": "contrarian", "hookStyle": "auto", "visualStyle": "auto" }; // Run the Actor and wait for it to finish const run = await client.actor("moulds.ai/script-generation").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 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); }); // 📚 Want to learn more 📖? Go to → 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 = { "topic": "The hidden cost of technical debt that CFOs never see on any report", "contentGoal": "awareness", "platform": "INSTAGRAM_REEL", "targetDuration": 45, "tone": "contrarian", "hookStyle": "auto", "visualStyle": "auto", } # Run the Actor and wait for it to finish run = client.actor("moulds.ai/script-generation").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "topic": "The hidden cost of technical debt that CFOs never see on any report", "contentGoal": "awareness", "platform": "INSTAGRAM_REEL", "targetDuration": 45, "tone": "contrarian", "hookStyle": "auto", "visualStyle": "auto" }' | apify call moulds.ai/script-generation --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=moulds.ai/script-generation", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Script Architect", "description": "AI-powered script writer for TikTok, Instagram Reels & YouTube Shorts. Generates viral short-form video scripts with hooks, scene breakdowns & captions — powered by Gemini 2.0 Flash + LangGraph.", "version": "0.2", "x-build-id": "4eihzwjM3R5xadZds" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/moulds.ai~script-generation/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-moulds.ai-script-generation", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/moulds.ai~script-generation/runs": { "post": { "operationId": "runs-sync-moulds.ai-script-generation", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/moulds.ai~script-generation/run-sync": { "post": { "operationId": "run-sync-moulds.ai-script-generation", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "topic" ], "properties": { "topic": { "title": "Topic", "minLength": 10, "maxLength": 200, "type": "string", "description": "A specific content angle, not just a category. Bad: 'AI'. Good: 'Why AI tools make junior devs slower before they make them faster'." }, "targetAudience": { "title": "Target Audience", "maxLength": 300, "type": "string", "description": "Who is this video for? Include role, seniority, and pain point. Example: 'Series A startup CTOs who ship fast but feel the codebase slowing them down'." }, "contentGoal": { "title": "Content Goal", "enum": [ "awareness", "education", "conversion", "entertainment", "thought_leadership" ], "type": "string", "description": "Primary goal — drives narrative arc and CTA strategy.", "default": "awareness" }, "platform": { "title": "Platform", "enum": [ "INSTAGRAM_REEL", "TIKTOK", "YOUTUBE_SHORT" ], "type": "string", "description": "Target short-form video platform. Controls pacing, duration, and visual treatment.", "default": "INSTAGRAM_REEL" }, "targetDuration": { "title": "Target Duration (seconds)", "minimum": 15, "maximum": 60, "type": "integer", "description": "Target script duration in seconds. The agent will aim to hit this within ±5s.", "default": 45 }, "tone": { "title": "Tone", "enum": [ "contrarian", "storyteller", "insider", "skeptic", "teacher", "agitator", "auto" ], "type": "string", "description": "Voice archetype for the script. Each produces a meaningfully different output.", "default": "contrarian" }, "hookStyle": { "title": "Hook Style", "enum": [ "contradiction", "stat", "story", "question", "bold_claim", "curiosity_gap", "auto" ], "type": "string", "description": "Opening hook formula. 'auto' lets the agent choose based on trend analysis.", "default": "auto" }, "visualStyle": { "title": "Visual Style", "enum": [ "talking_head", "b_roll_cinematic", "screen_recording", "motion_graphics", "mixed", "auto" ], "type": "string", "description": "Visual treatment for all scenes. Controls how visual_prompts are written.", "default": "auto" }, "referenceCreators": { "title": "Reference Creators", "maxItems": 3, "type": "array", "description": "Instagram/TikTok usernames of creators whose style to study. The agent will analyze their profiles. Example: hubermanlab, garyvee", "items": { "type": "string" } }, "avoidTopics": { "title": "Topics to Avoid", "type": "array", "description": "Topics or entities to never mention in the script.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```