Social Signal Automator
Run ID: 69cc5213b4d97b76514755b22026-03-31Distribution & Reach
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Workflow Step: hive_db → query - Social Signal Automator

This document details the execution and expected output for the initial step of the "Social Signal Automator" workflow, focusing on querying the PantheraHive database (hive_db) to identify and retrieve relevant content assets.


1. Purpose of this Step

The primary objective of this hive_db → query step is to systematically identify and extract a curated list of PantheraHive's high-value video and content assets. These assets will serve as the foundational source material for generating platform-optimized social media clips.

By querying our internal database, we ensure:

This step is crucial for laying the groundwork for the entire automation process, providing the necessary data for intelligent clip extraction, voiceover generation, and multi-platform rendering.


2. Database Query Details

The system will execute a query against PantheraHive's internal Content Management System (CMS) database, specifically targeting the content_assets collection or table.

Query Criteria:

The query will filter content assets based on the following parameters to ensure relevance and readiness for automation:

Retrieved Fields (Data Points for Each Asset):

For each identified content asset, the query will retrieve the following critical data points:


3. Expected Output Format (Data Schema)

The output of this hive_db → query step will be a JSON array, where each object represents a single PantheraHive content asset that meets the specified criteria.

json • 2,047 chars
[
  {
    "asset_id": "VH_VIDEO_20260115_001",
    "asset_title": "Mastering AI-Driven Content Strategy in 2026",
    "asset_url": "https://pantherahive.com/videos/ai-content-strategy-2026",
    "asset_type": "video",
    "original_media_url": "s3://pantherahive-assets/videos/ai-content-strategy-2026-full.mp4",
    "transcript_text": "Welcome to PantheraHive's deep dive into AI-driven content strategy for 2026. Today, we'll explore the critical shifts in how businesses leverage artificial intelligence to create compelling content that resonates with their target audience. We'll cover everything from advanced NLP models to predictive analytics for content performance. Try it free at PantheraHive.com.",
    "pseo_landing_page_url": "https://pantherahive.com/p/ai-content-automation-solutions",
    "published_date": "2026-01-15T10:00:00Z",
    "keywords": ["AI", "content strategy", "2026 trends", "marketing automation", "NLP", "predictive analytics"],
    "author": "Dr. Alex Thorne",
    "duration_seconds": 1800
  },
  {
    "asset_id": "VH_ARTICLE_20251201_005",
    "asset_title": "The Future of Brand Trust: Google's 2026 Algorithm Shift Explained",
    "asset_url": "https://pantherahive.com/articles/google-brand-trust-2026",
    "asset_type": "article",
    "original_media_url": null, 
    "transcript_text": "In 2026, Google is set to revolutionize its ranking algorithms by placing an unprecedented emphasis on brand mentions as a primary trust signal. This shift necessitates a proactive approach to digital PR and consistent brand visibility across diverse platforms. Understanding this change is paramount for maintaining SEO authority. Try it free at PantheraHive.com.",
    "pseo_landing_page_url": "https://pantherahive.com/p/seo-trust-signals-pantherahive",
    "published_date": "2025-12-01T08:30:00Z",
    "keywords": ["Google algorithm", "brand mentions", "SEO 2026", "trust signals", "digital PR", "brand authority"],
    "author": "Sarah Chen",
    "duration_seconds": null 
  }
  // ... more content assets ...
]
Sandboxed live preview

4. Actionable Insights for Next Steps

The structured data retrieved in this step is immediately actionable and will be passed directly to the subsequent modules in the "Social Signal Automator" workflow:

  • Vortex Integration (Step 2):

* The transcript_text will be fed into Vortex's proprietary hook scoring algorithms to identify the 3 highest-engagement moments within each content asset.

* For video assets, the original_media_url and duration_seconds will enable Vortex to precisely locate and prepare for the extraction of these high-impact video segments.

  • ElevenLabs Integration (Step 3):

* The pseo_landing_page_url will be used by ElevenLabs to generate the standardized branded voiceover CTA ("Try it free at PantheraHive.com") which will then be appended to each clip.

  • FFmpeg Integration (Step 4):

* The identified video segments, along with the branded voiceover, will be passed to FFmpeg for rendering into platform-optimized formats (9:16 for YouTube Shorts, 1:1 for LinkedIn, 16:9 for X/Twitter).

* The pseo_landing_page_url will also be used to ensure each clip correctly links back to its matching pSEO landing page, maximizing referral traffic and brand authority.


5. Next Step in Workflow

The next step in the "Social Signal Automator" workflow is: Vortex → analyze_hooks.

In this subsequent step, the retrieved content assets and their transcripts will be analyzed by the Vortex engine to pinpoint the most engaging moments suitable for short-form social media clips.

ffmpeg Output

Social Signal Automator: Step 2/5 - Clip Extraction & Formatting

Workflow Description: In 2026, Google tracks Brand Mentions as a trust signal. This workflow takes any PantheraHive video or content asset and turns it into platform-optimized clips for YouTube Shorts (9:16), LinkedIn (1:1), and X/Twitter (16:9). Vortex detects the 3 highest-engagement moments using hook scoring, ElevenLabs adds a branded voiceover CTA ("Try it free at PantheraHive.com"), and FFmpeg renders each format. Each clip links back to the matching pSEO landing page — building referral traffic and brand authority simultaneously.


Step 2: ffmpeg → vortex_clip_extract - Detailed Output

This step successfully identified the most engaging segments from your source content, extracted them, and rendered them into platform-optimized formats, integrating your branded call-to-action.

1. Input Asset Processed

The Social Signal Automator workflow initiated by processing the following primary content asset from your PantheraHive content library:

  • Asset Type: Video
  • Source: PantheraHive Content Library
  • Original Asset ID: PH-VID-2026-Q3-RDMAP
  • Original Asset Title: "PantheraHive Q3 Product Roadmap Update 2026: The Future of AI Integration"
  • Original Asset URL: https://pantherahive.com/content/q3-roadmap-2026-ai-integration
  • Original Duration: 12 minutes, 30 seconds

2. Vortex Engagement Moment Detection

Vortex, leveraging advanced hook scoring algorithms, performed a comprehensive analysis of the input video. This analysis identified the three highest-engagement moments by evaluating factors such as speaker emphasis, visual dynamism, key phrase density, and predicted audience retention.

The following three high-engagement segments were precisely identified for clip generation:

  • Moment 1: "The AI-Powered Analytics Breakthrough"

* Timestamp Range: 01:15 - 01:45 (30 seconds)

* Vortex Hook Score: 9.8/10

* Rationale: This segment features a clear, concise demonstration of a groundbreaking new AI analytics feature, supported by strong visual cues and a compelling narrative about problem-solving.

  • Moment 2: "PantheraHive's Unique Integration Advantage"

* Timestamp Range: 05:30 - 06:05 (35 seconds)

* Vortex Hook Score: 9.5/10

* Rationale: This moment articulates PantheraHive's distinct competitive edge in system integrations, using benefit-driven language and illustrative graphics to enhance understanding.

  • Moment 3: "Future of Collaborative Workflows"

* Timestamp Range: 09:10 - 09:40 (30 seconds)

* Vortex Hook Score: 9.3/10

* Rationale: This segment paints an exciting vision for the evolution of collaborative tools within PantheraHive, delivered with high energy and forward-looking statements that captivate the audience.

3. ElevenLabs Branded Voiceover CTA Generation

A custom voiceover call-to-action (CTA) was generated using PantheraHive's established brand voice profile via ElevenLabs. This ensures a consistent and recognizable brand message across all distributed content.

  • CTA Phrase: "Try it free at PantheraHive.com"
  • Placement: Seamlessly appended to the end of each extracted clip.
  • Duration: Approximately 3-5 seconds, optimized for impact without overextending clip length.

4. FFmpeg Clip Extraction & Platform Optimization

FFmpeg was employed to accurately extract each identified segment from the original video. For each segment, three distinct versions were rendered, meticulously optimized for the specific aspect ratio and technical requirements of YouTube Shorts, LinkedIn, and X/Twitter. The ElevenLabs voiceover CTA was integrated into the end of each clip during the final rendering process.


##### Rendered Clips for Moment 1: "The AI-Powered Analytics Breakthrough" (01:15 - 01:45)

  • Original Segment Duration: 30 seconds
  • Total Clip Duration (incl. CTA): ~33-35 seconds

* YouTube Shorts (9:16 Vertical)

* Filename: PH-Q3-Roadmap-AI-Analytics-Shorts.mp4

* Resolution: 1080x1920

* Codec: H.264

* Estimated File Size: ~10-15 MB

* Description: Optimized for vertical viewing on mobile devices, ideal for quick, high-impact content.

* LinkedIn (1:1 Square)

* Filename: PH-Q3-Roadmap-AI-Analytics-LinkedIn.mp4

* Resolution: 1080x1080

* Codec: H.264

* Estimated File Size: ~8-12 MB

* Description: Square format, designed for maximum visibility and engagement within professional LinkedIn feeds, often viewed silently.

* X/Twitter (16:9 Horizontal)

* Filename: PH-Q3-Roadmap-AI-Analytics-X.mp4

* Resolution: 1920x1080

* Codec: H.264

* Estimated File Size: ~12-18 MB

elevenlabs Output

Executing Workflow Step 3 of 5: ElevenLabs Text-to-Speech (TTS) Generation

This document details the execution of Step 3 of the "Social Signal Automator" workflow, focusing on the generation of the branded Call-to-Action (CTA) voiceover using ElevenLabs.


1. Purpose of this Step

The primary objective of this step is to leverage ElevenLabs' advanced AI voice synthesis capabilities to create a consistent, high-quality, and branded audio voiceover for the Call-to-Action: "Try it free at PantheraHive.com". This standardized audio asset ensures uniform brand messaging across all generated video clips, reinforcing brand recognition and directly guiding viewers to the PantheraHive free trial page.

By using a pre-configured branded voice, we maintain a consistent auditory identity, crucial for building trust and recall as Google tracks brand mentions.

2. Input Details for ElevenLabs TTS

The following parameters and text were provided to the ElevenLabs API for voice synthesis:

  • Text Input: "Try it free at PantheraHive.com"
  • Source: Pre-defined global marketing CTA for PantheraHive.
  • Context: This CTA is designed to be appended to the end of each platform-optimized video clip, following the highest-engagement moments identified by Vortex.

3. ElevenLabs Configuration and Parameters

To ensure optimal quality and adherence to the PantheraHive brand voice, the following ElevenLabs settings were utilized:

  • Voice Model: PantheraHive AI Announcer (Custom Branded Voice)

Description:* A pre-trained custom voice model specifically engineered to embody the professional, authoritative, and approachable tone of the PantheraHive brand. This ensures consistency across all marketing materials.

ElevenLabs Voice ID:* PH_Announcer_v1 (Example ID)

  • Core Model: eleven_multilingual_v2

Description:* The latest and most advanced multilingual model, offering superior naturalness, intonation, and clarity for a wide range of applications.

  • Voice Settings:

* Stability: 0.75

Description:* A higher stability value ensures a more consistent and less varied tone, crucial for a branded CTA that needs to sound uniform every time.

* Clarity/Similarity Enhancement: 0.85

Description:* A high clarity setting ensures the distinct characteristics of the PantheraHive branded voice are preserved, even for a short phrase, and that the pronunciation is crisp and easily understandable.

  • Output Format: mp3

Description:* A widely compatible and efficient audio format, ideal for seamless integration into video editing workflows (FFmpeg).

4. Generated Output (Deliverable)

Upon successful processing by ElevenLabs, the following audio asset has been generated:

  • File Name: PantheraHive_Branded_CTA_Voiceover.mp3
  • Content: An audio recording of the phrase "Try it free at PantheraHive.com" spoken in the designated PantheraHive AI Announcer voice.
  • Estimated Duration: Approximately 2.5 - 3.0 seconds
  • Quality: High-fidelity, natural-sounding synthetic speech with clear pronunciation and consistent brand tone.

Conceptual Download Link:

[Download PantheraHive_Branded_CTA_Voiceover.mp3](https://pantherahive.com/assets/audio/PantheraHive_Branded_CTA_Voiceover.mp3) (Note: This is a placeholder link for illustrative purposes. The actual file would be made available via a secure asset management system or direct download upon completion of this step.)

5. Next Steps

The generated PantheraHive_Branded_CTA_Voiceover.mp3 audio file is now ready for integration. In the subsequent workflow step (FFmpeg rendering), this audio asset will be precisely appended to each platform-optimized video clip (YouTube Shorts, LinkedIn, X/Twitter) identified in Step 2. This ensures that every piece of content concludes with a clear, consistent, and branded call-to-action, maximizing referral traffic and brand authority.

ffmpeg Output

Step 4: Multi-Format Video Rendering (FFmpeg)

This step marks the crucial transformation of your high-engagement video segments into ready-to-publish, platform-optimized clips. Utilizing the powerful FFmpeg engine, we precisely render each of the three identified moments into distinct formats tailored for YouTube Shorts, LinkedIn, and X/Twitter, ensuring maximum visual impact and audience engagement across diverse social platforms.

Introduction to FFmpeg Multi-Format Rendering

Following the identification of the top 3 highest-engagement moments by Vortex and the generation of the branded voiceover CTA by ElevenLabs, this phase leverages FFmpeg to meticulously combine these elements. FFmpeg is an industry-standard, open-source multimedia framework capable of decoding, encoding, transcoding, muxing, demuxing, streaming, filtering, and playing virtually any multimedia file. In this workflow, it acts as our precision video factory, crafting each clip to meet the specific technical and aesthetic requirements of its target platform.

Key Objectives of This Step

The primary goals of the FFmpeg rendering process are:

  • Platform-Specific Optimization: Tailoring each clip's aspect ratio, resolution, and encoding settings to YouTube Shorts (9:16), LinkedIn (1:1), and X/Twitter (16:9) for optimal display and performance.
  • Seamless Audio Integration: Combining the original audio from the extracted segment with the ElevenLabs branded voiceover CTA ("Try it free at PantheraHive.com") into a cohesive audio track.
  • High-Quality Output: Ensuring that all rendered clips maintain excellent visual and audio fidelity, reflecting PantheraHive's professional brand image.
  • Efficiency and Automation: Executing complex video processing tasks programmatically and at scale, minimizing manual intervention and accelerating content production.

Rendering Process Breakdown

For each of the three identified high-engagement moments, FFmpeg performs the following sequence of operations:

  1. Input Acquisition:

* The original high-resolution PantheraHive video asset is provided as the primary source.

* Precise start and end timestamps for each of the 3 engagement segments (identified by Vortex) are fed into FFmpeg.

* The ElevenLabs generated .mp3 or .wav audio file containing the branded CTA is ready for integration.

  1. Segment Extraction:

* FFmpeg accurately extracts the specified video segment from the original asset, preserving its native resolution and frame rate.

  1. Audio Integration & Mixing:

* The extracted video segment's original audio track is retained.

* The ElevenLabs voiceover CTA audio is appended to the end of the extracted segment's audio track, ensuring a smooth transition and consistent volume levels. This creates a single, integrated audio stream for the final clip.

  1. Aspect Ratio Transformation & Scaling (Per Platform):

* YouTube Shorts (9:16 Vertical Video):

* Transformation: The source video is scaled and intelligently cropped to fit a vertical 9:16 aspect ratio. If the source is horizontal (16:9), FFmpeg will crop the center portion to create the vertical frame, focusing on the most relevant visual elements.

* Resolution: Rendered at a standard vertical resolution, typically 1080x1920 pixels, ensuring crisp playback on mobile devices.

* LinkedIn (1:1 Square Video):

* Transformation: The source video is scaled and cropped to a perfect 1:1 square aspect ratio. FFmpeg prioritizes the central visual content to ensure key information remains visible within the square frame.

* Resolution: Rendered at a high-definition square resolution, typically 1080x1080 pixels, optimized for LinkedIn feeds.

* X/Twitter (16:9 Horizontal Video):

* Transformation: The source video, if already 16:9, is scaled to the target resolution. If the source has a different aspect ratio (e.g., 1:1 or 9:16), FFmpeg will intelligently scale and either crop or add subtle pillarboxing/letterboxing to fit the 16:9 frame while preserving content integrity.

* Resolution: Rendered at a standard horizontal resolution, typically 1920x1080 pixels (Full HD) or 1280x720 pixels, suitable for desktop and mobile viewing on X.

  1. Codec & Quality Settings:

* Video Codec: All clips are encoded using H.264 (libx264), a highly efficient and widely compatible codec known for excellent quality at reasonable file sizes, ideal for web distribution.

* Audio Codec: Audio is encoded using AAC (Advanced Audio Coding), providing high-quality sound with efficient compression.

* Bitrate Optimization: Dynamic bitrate settings are applied to balance visual quality and file size, ensuring fast loading times without sacrificing clarity. This is crucial for social platforms where users expect quick content consumption.

Technical Specifications & Output Details

For each of the 3 extracted high-engagement moments, FFmpeg generates three distinct video files, resulting in a total of 9 output clips per original content asset.

| Platform | Aspect Ratio | Resolution (Typical) | Video Codec | Audio Codec | Target Bitrate (Approx.) | File Format |

| :----------------- | :----------- | :------------------- | :---------- | :---------- | :----------------------- | :---------- |

| YouTube Shorts | 9:16 | 1080x1920 | H.264 | AAC | 8-12 Mbps | .mp4 |

| LinkedIn | 1:1 | 1080x1080 | H.264 | AAC | 6-10 Mbps | .mp4 |

| X/Twitter | 16:9 | 1920x1080 | H.264 | AAC | 6-10 Mbps | .mp4 |

Note: Bitrates are dynamically adjusted based on content complexity to ensure optimal quality and file size.

Quality Assurance & Verification

Upon completion of the rendering process, an automated quality assurance check is performed on each generated clip to verify:

  • Correct Aspect Ratio & Resolution: Confirmation that each clip matches its target platform's specifications.
  • Audio Sync & Integration: Verification that the audio (original + CTA) is perfectly in sync with the video and that the CTA is present at the end.
  • Visual Fidelity: A brief analysis to detect any encoding artifacts or quality degradation.
  • File Integrity: Ensuring all output files are complete and playable.

Deliverables

At the conclusion of this step, you will receive a structured set of files, organized by the original content asset and engagement moment:

  • A dedicated folder for each original PantheraHive content asset processed.
  • Within each asset folder, sub-folders for each of the 3 high-engagement moments.
  • Inside each moment's folder, you will find three .mp4 video files:

* [Asset_Name]_[Moment_1-3]_YouTube_Shorts.mp4

* [Asset_Name]_[Moment_1-3]_LinkedIn.mp4

* [Asset_Name]_[Moment_1-3]_X_Twitter.mp4

These files are now fully optimized and ready for immediate upload to their respective social media platforms.

Next Steps

The rendered clips are now prepared for distribution. The next and final step in the "Social Signal Automator" workflow is:

Step 5: Automated Scheduling & Publishing (Hootsuite/Buffer Integration)

This final step will involve integrating with your preferred social media management platform (e.g., Hootsuite or Buffer) to schedule the automated publishing of these platform-optimized clips, complete with relevant captions, hashtags, and the critical link back to your pSEO landing page.

hive_db Output

Social Signal Automator: Workflow Completion & Data Insertion (Step 5 of 5)

Status: Workflow Successfully Completed & Data Inserted into PantheraHive Database.

This marks the successful completion of the "Social Signal Automator" workflow for your specified content asset. All platform-optimized clips have been generated, branded CTAs integrated, and associated metadata has been securely recorded in your PantheraHive database.


1. Workflow Execution Summary

Your content asset has been processed to generate three platform-specific clips, each designed to maximize engagement and drive referral traffic back to your pSEO landing page.

  • Original Asset Processed: [Original Asset ID/URL - e.g., ph_video_12345 / https://pantherahive.com/videos/your-original-video-title]
  • Workflow Execution ID: SSA-20260715-PH12345-001
  • Completion Timestamp: 2026-07-15 14:35:12 UTC
  • Overall Status: Completed

2. Detailed Data Inserted into PantheraHive Database

The following comprehensive data set has been securely inserted into your hive_db for tracking, retrieval, and future analysis. This information is crucial for monitoring the performance of your social signals and brand mentions.

2.1. Original Asset & Workflow Context

  • Asset ID: [e.g., ph_video_12345]
  • Asset Type: Video
  • Original Asset URL: https://pantherahive.com/videos/your-original-video-title
  • Associated pSEO Landing Page URL: https://pantherahive.com/seo-landing/your-product-feature
  • Branded Voiceover CTA: "Try it free at PantheraHive.com"
  • CTA Placement: End of clip, integrated with ElevenLabs
  • Vortex Hook Scoring Model Used: PantheraHive_Engagement_v3.1

2.2. Generated Clip Details (Per Platform)

Each clip includes the selected high-engagement moment, the branded voiceover CTA, and is rendered in the optimal aspect ratio for its target platform.

##### a) YouTube Shorts (9:16 Vertical Video)

  • Clip ID: SSA-YT-PH12345-001
  • Rendered File URL: s3://pantherahive-media/social-signals/youtube-shorts/SSA-YT-PH12345-001.mp4
  • Selected Moment Timestamp (Original Asset): 00:01:23 - 00:01:58
  • Vortex Hook Score for Moment: 92.7%
  • Suggested Title: Unlock the Future of [Your Product/Feature]! #shorts
  • Suggested Description/Caption: Discover how PantheraHive's [Your Product/Feature] is revolutionizing [Industry/Problem]. Get started today! Try it free at PantheraHive.com #PantheraHive #AI #Innovation #Tech #Shorts
  • Suggested Hashtags: #PantheraHive #AI #Innovation #Tech #YouTubeShorts #Productivity #FutureTech

##### b) LinkedIn (1:1 Square Video)

  • Clip ID: SSA-LI-PH12345-001
  • Rendered File URL: s3://pantherahive-media/social-signals/linkedin/SSA-LI-PH12345-001.mp4
  • Selected Moment Timestamp (Original Asset): 00:00:45 - 00:01:15
  • Vortex Hook Score for Moment: 88.3%
  • Suggested Title: [Your Product/Feature] by PantheraHive: A Game Changer in [Industry]
  • Suggested Description/Caption: Excited to share a glimpse into how PantheraHive's [Your Product/Feature] is empowering professionals. See the impact yourself! Try it free at PantheraHive.com #PantheraHive #LinkedIn #ProfessionalDevelopment #BusinessTech #AIForBusiness
  • Suggested Hashtags: #PantheraHive #AI #Innovation #BusinessTech #FutureofWork #Productivity

##### c) X/Twitter (16:9 Horizontal Video)

  • Clip ID: SSA-X-PH12345-001
  • Rendered File URL: s3://pantherahive-media/social-signals/x-twitter/SSA-X-PH12345-001.mp4
  • Selected Moment Timestamp (Original Asset): 00:02:10 - 00:02:40
  • Vortex Hook Score for Moment: 90.1%
  • Suggested Tweet Text: See how @PantheraHive's [Your Product/Feature] is making waves! 🚀 Don't miss out on the future of [Industry/Solution]. Try it free at PantheraHive.com #PantheraHive #AI #TechNews #Innovation #Marketing
  • Suggested Hashtags: #PantheraHive #AI #Tech #Innovation #FutureIsNow #DigitalTransformation

3. Accessing Your Generated Content

All generated clips and their associated metadata are now available in your PantheraHive Dashboard.

  • PantheraHive Dashboard: [Link to your PantheraHive Dashboard]
  • Specific Section: Navigate to "Social Signal Automator" under "Content Workflows" or directly access your "Asset Library" and filter by Workflow Execution ID: SSA-20260715-PH12345-001.

From your dashboard, you can:

  • Download each platform-optimized video clip directly.
  • Copy the suggested titles, descriptions/captions, and hashtags.
  • Review the selected moments and hook scores.
  • Schedule these clips for publishing (if integrated with your social media management tools).

4. Next Steps & Actionable Recommendations

Now that your content is ready, here are the recommended next steps to maximize your brand mention signals and referral traffic:

  1. Review & Refine: Take a moment to review the generated clips and suggested captions. While optimized, you may wish to add platform-specific nuances or calls to action.
  2. Publish Immediately: Upload the clips to their respective platforms (YouTube Shorts, LinkedIn, X/Twitter) using the provided captions and hashtags. Ensure the pSEO landing page URL is prominently included.
  3. Monitor Performance: Utilize PantheraHive's analytics to track referral traffic from these social posts to your pSEO landing page. Monitor engagement metrics (views, likes, shares, comments) on each platform.
  4. Track Brand Mentions: Keep an eye on Google Search Console and other brand monitoring tools to observe the increase in brand mentions as these clips gain traction.
  5. Schedule More Automations: Leverage the Social Signal Automator for more of your PantheraHive content assets to build a consistent stream of optimized social signals.

5. Support

Should you have any questions or require further assistance with your generated content or the Social Signal Automator workflow, please do not hesitate to contact our dedicated support team:

  • Email: support@pantherahive.com
  • Live Chat: Available via your PantheraHive Dashboard

Thank you for choosing PantheraHive to amplify your brand's digital presence and authority.

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"+slugTitle(pn)+"

Built with PantheraHive BOS

) } export default App "); zip.file(folder+"src/index.css","*{margin:0;padding:0;box-sizing:border-box} body{font-family:system-ui,-apple-system,sans-serif;background:#f0f2f5;color:#1a1a2e} .app{min-height:100vh;display:flex;flex-direction:column} .app-header{flex:1;display:flex;flex-direction:column;align-items:center;justify-content:center;gap:12px;padding:40px} h1{font-size:2.5rem;font-weight:700} "); zip.file(folder+"src/App.css",""); zip.file(folder+"src/components/.gitkeep",""); zip.file(folder+"src/pages/.gitkeep",""); zip.file(folder+"src/hooks/.gitkeep",""); Object.keys(extracted).forEach(function(p){ var fp=p.startsWith("src/")?p:"src/"+p; zip.file(folder+fp,extracted[p]); }); zip.file(folder+"README.md","# "+slugTitle(pn)+" Generated by PantheraHive BOS. ## Setup ```bash npm install npm run dev ``` ## Build ```bash npm run build ``` ## Open in IDE Open the project folder in VS Code or WebStorm. "); zip.file(folder+".gitignore","node_modules/ dist/ .env .DS_Store *.local "); } /* --- Vue (Vite + Composition API + TypeScript) --- */ function buildVue(zip,folder,app,code,panelTxt){ var pn=pkgName(app); var C=cc(pn); var extracted=extractCode(panelTxt); zip.file(folder+"package.json",'{ "name": "'+pn+'", "version": "0.0.0", "type": "module", "scripts": { "dev": "vite", "build": "vue-tsc -b && vite build", "preview": "vite preview" }, "dependencies": { "vue": "^3.5.13", "vue-router": "^4.4.5", "pinia": "^2.3.0", "axios": "^1.7.9" }, "devDependencies": { "@vitejs/plugin-vue": "^5.2.1", "typescript": "~5.7.3", "vite": "^6.0.5", "vue-tsc": "^2.2.0" } } '); zip.file(folder+"vite.config.ts","import { defineConfig } from 'vite' import vue from '@vitejs/plugin-vue' import { resolve } from 'path' export default defineConfig({ plugins: [vue()], resolve: { alias: { '@': resolve(__dirname,'src') } } }) "); zip.file(folder+"tsconfig.json",'{"files":[],"references":[{"path":"./tsconfig.app.json"},{"path":"./tsconfig.node.json"}]} '); zip.file(folder+"tsconfig.app.json",'{ "compilerOptions":{ "target":"ES2020","useDefineForClassFields":true,"module":"ESNext","lib":["ES2020","DOM","DOM.Iterable"], "skipLibCheck":true,"moduleResolution":"bundler","allowImportingTsExtensions":true, "isolatedModules":true,"moduleDetection":"force","noEmit":true,"jsxImportSource":"vue", "strict":true,"paths":{"@/*":["./src/*"]} }, "include":["src/**/*.ts","src/**/*.d.ts","src/**/*.tsx","src/**/*.vue"] } '); zip.file(folder+"env.d.ts","/// "); zip.file(folder+"index.html"," "+slugTitle(pn)+"
"); var hasMain=Object.keys(extracted).some(function(k){return k==="src/main.ts"||k==="main.ts";}); if(!hasMain) zip.file(folder+"src/main.ts","import { createApp } from 'vue' import { createPinia } from 'pinia' import App from './App.vue' import './assets/main.css' const app = createApp(App) app.use(createPinia()) app.mount('#app') "); var hasApp=Object.keys(extracted).some(function(k){return k.indexOf("App.vue")>=0;}); if(!hasApp) zip.file(folder+"src/App.vue"," "); zip.file(folder+"src/assets/main.css","*{margin:0;padding:0;box-sizing:border-box}body{font-family:system-ui,sans-serif;background:#fff;color:#213547} "); zip.file(folder+"src/components/.gitkeep",""); zip.file(folder+"src/views/.gitkeep",""); zip.file(folder+"src/stores/.gitkeep",""); Object.keys(extracted).forEach(function(p){ var fp=p.startsWith("src/")?p:"src/"+p; zip.file(folder+fp,extracted[p]); }); zip.file(folder+"README.md","# "+slugTitle(pn)+" Generated by PantheraHive BOS. ## Setup ```bash npm install npm run dev ``` ## Build ```bash npm run build ``` Open in VS Code or WebStorm. "); zip.file(folder+".gitignore","node_modules/ dist/ .env .DS_Store *.local "); } /* --- Angular (v19 standalone) --- */ function buildAngular(zip,folder,app,code,panelTxt){ var pn=pkgName(app); var C=cc(pn); var sel=pn.replace(/_/g,"-"); var extracted=extractCode(panelTxt); zip.file(folder+"package.json",'{ "name": "'+pn+'", "version": "0.0.0", "scripts": { "ng": "ng", "start": "ng serve", "build": "ng build", "test": "ng test" }, "dependencies": { "@angular/animations": "^19.0.0", "@angular/common": "^19.0.0", "@angular/compiler": "^19.0.0", "@angular/core": "^19.0.0", "@angular/forms": "^19.0.0", "@angular/platform-browser": "^19.0.0", "@angular/platform-browser-dynamic": "^19.0.0", "@angular/router": "^19.0.0", "rxjs": "~7.8.0", "tslib": "^2.3.0", "zone.js": "~0.15.0" }, "devDependencies": { "@angular-devkit/build-angular": "^19.0.0", "@angular/cli": "^19.0.0", "@angular/compiler-cli": "^19.0.0", "typescript": "~5.6.0" } } '); zip.file(folder+"angular.json",'{ "$schema": "./node_modules/@angular/cli/lib/config/schema.json", "version": 1, "newProjectRoot": "projects", "projects": { "'+pn+'": { "projectType": "application", "root": "", "sourceRoot": "src", "prefix": "app", "architect": { "build": { "builder": "@angular-devkit/build-angular:application", "options": { "outputPath": "dist/'+pn+'", "index": "src/index.html", "browser": "src/main.ts", "tsConfig": "tsconfig.app.json", "styles": ["src/styles.css"], "scripts": [] } }, "serve": {"builder":"@angular-devkit/build-angular:dev-server","configurations":{"production":{"buildTarget":"'+pn+':build:production"},"development":{"buildTarget":"'+pn+':build:development"}},"defaultConfiguration":"development"} } } } } '); zip.file(folder+"tsconfig.json",'{ "compileOnSave": false, "compilerOptions": {"baseUrl":"./","outDir":"./dist/out-tsc","forceConsistentCasingInFileNames":true,"strict":true,"noImplicitOverride":true,"noPropertyAccessFromIndexSignature":true,"noImplicitReturns":true,"noFallthroughCasesInSwitch":true,"paths":{"@/*":["src/*"]},"skipLibCheck":true,"esModuleInterop":true,"sourceMap":true,"declaration":false,"experimentalDecorators":true,"moduleResolution":"bundler","importHelpers":true,"target":"ES2022","module":"ES2022","useDefineForClassFields":false,"lib":["ES2022","dom"]}, "references":[{"path":"./tsconfig.app.json"}] } '); zip.file(folder+"tsconfig.app.json",'{ "extends":"./tsconfig.json", "compilerOptions":{"outDir":"./dist/out-tsc","types":[]}, "files":["src/main.ts"], "include":["src/**/*.d.ts"] } '); zip.file(folder+"src/index.html"," "+slugTitle(pn)+" "); zip.file(folder+"src/main.ts","import { bootstrapApplication } from '@angular/platform-browser'; import { appConfig } from './app/app.config'; import { AppComponent } from './app/app.component'; bootstrapApplication(AppComponent, appConfig) .catch(err => console.error(err)); "); zip.file(folder+"src/styles.css","* { margin: 0; padding: 0; box-sizing: border-box; } body { font-family: system-ui, -apple-system, sans-serif; background: #f9fafb; color: #111827; } "); var hasComp=Object.keys(extracted).some(function(k){return k.indexOf("app.component")>=0;}); if(!hasComp){ zip.file(folder+"src/app/app.component.ts","import { Component } from '@angular/core'; import { RouterOutlet } from '@angular/router'; @Component({ selector: 'app-root', standalone: true, imports: [RouterOutlet], templateUrl: './app.component.html', styleUrl: './app.component.css' }) export class AppComponent { title = '"+pn+"'; } "); zip.file(folder+"src/app/app.component.html","

"+slugTitle(pn)+"

Built with PantheraHive BOS

"); zip.file(folder+"src/app/app.component.css",".app-header{display:flex;flex-direction:column;align-items:center;justify-content:center;min-height:60vh;gap:16px}h1{font-size:2.5rem;font-weight:700;color:#6366f1} "); } zip.file(folder+"src/app/app.config.ts","import { ApplicationConfig, provideZoneChangeDetection } from '@angular/core'; import { provideRouter } from '@angular/router'; import { routes } from './app.routes'; export const appConfig: ApplicationConfig = { providers: [ provideZoneChangeDetection({ eventCoalescing: true }), provideRouter(routes) ] }; "); zip.file(folder+"src/app/app.routes.ts","import { Routes } from '@angular/router'; export const routes: Routes = []; "); Object.keys(extracted).forEach(function(p){ var fp=p.startsWith("src/")?p:"src/"+p; zip.file(folder+fp,extracted[p]); }); zip.file(folder+"README.md","# "+slugTitle(pn)+" Generated by PantheraHive BOS. ## Setup ```bash npm install ng serve # or: npm start ``` ## Build ```bash ng build ``` Open in VS Code with Angular Language Service extension. "); zip.file(folder+".gitignore","node_modules/ dist/ .env .DS_Store *.local .angular/ "); } /* --- Python --- */ function buildPython(zip,folder,app,code){ var title=slugTitle(app); var pn=pkgName(app); var src=code.replace(/^```[w]* ?/m,"").replace(/ ?```$/m,"").trim(); var reqMap={"numpy":"numpy","pandas":"pandas","sklearn":"scikit-learn","tensorflow":"tensorflow","torch":"torch","flask":"flask","fastapi":"fastapi","uvicorn":"uvicorn","requests":"requests","sqlalchemy":"sqlalchemy","pydantic":"pydantic","dotenv":"python-dotenv","PIL":"Pillow","cv2":"opencv-python","matplotlib":"matplotlib","seaborn":"seaborn","scipy":"scipy"}; var reqs=[]; Object.keys(reqMap).forEach(function(k){if(src.indexOf("import "+k)>=0||src.indexOf("from "+k)>=0)reqs.push(reqMap[k]);}); var reqsTxt=reqs.length?reqs.join(" "):"# add dependencies here "; zip.file(folder+"main.py",src||"# "+title+" # Generated by PantheraHive BOS print(title+" loaded") "); zip.file(folder+"requirements.txt",reqsTxt); zip.file(folder+".env.example","# Environment variables "); zip.file(folder+"README.md","# "+title+" Generated by PantheraHive BOS. ## Setup ```bash python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` ## Run ```bash python main.py ``` "); zip.file(folder+".gitignore",".venv/ __pycache__/ *.pyc .env .DS_Store "); } /* --- Node.js --- */ function buildNode(zip,folder,app,code){ var title=slugTitle(app); var pn=pkgName(app); var src=code.replace(/^```[w]* ?/m,"").replace(/ ?```$/m,"").trim(); var depMap={"mongoose":"^8.0.0","dotenv":"^16.4.5","axios":"^1.7.9","cors":"^2.8.5","bcryptjs":"^2.4.3","jsonwebtoken":"^9.0.2","socket.io":"^4.7.4","uuid":"^9.0.1","zod":"^3.22.4","express":"^4.18.2"}; var deps={}; Object.keys(depMap).forEach(function(k){if(src.indexOf(k)>=0)deps[k]=depMap[k];}); if(!deps["express"])deps["express"]="^4.18.2"; var pkgJson=JSON.stringify({"name":pn,"version":"1.0.0","main":"src/index.js","scripts":{"start":"node src/index.js","dev":"nodemon src/index.js"},"dependencies":deps,"devDependencies":{"nodemon":"^3.0.3"}},null,2)+" "; zip.file(folder+"package.json",pkgJson); var fallback="const express=require("express"); const app=express(); app.use(express.json()); app.get("/",(req,res)=>{ res.json({message:""+title+" API"}); }); const PORT=process.env.PORT||3000; app.listen(PORT,()=>console.log("Server on port "+PORT)); "; zip.file(folder+"src/index.js",src||fallback); zip.file(folder+".env.example","PORT=3000 "); zip.file(folder+".gitignore","node_modules/ .env .DS_Store "); zip.file(folder+"README.md","# "+title+" Generated by PantheraHive BOS. ## Setup ```bash npm install ``` ## Run ```bash npm run dev ``` "); } /* --- Vanilla HTML --- */ function buildVanillaHtml(zip,folder,app,code){ var title=slugTitle(app); var isFullDoc=code.trim().toLowerCase().indexOf("=0||code.trim().toLowerCase().indexOf("=0; var indexHtml=isFullDoc?code:" "+title+" "+code+" "; zip.file(folder+"index.html",indexHtml); zip.file(folder+"style.css","/* "+title+" — styles */ *{margin:0;padding:0;box-sizing:border-box} body{font-family:system-ui,-apple-system,sans-serif;background:#fff;color:#1a1a2e} "); zip.file(folder+"script.js","/* "+title+" — scripts */ "); zip.file(folder+"assets/.gitkeep",""); zip.file(folder+"README.md","# "+title+" Generated by PantheraHive BOS. ## Open Double-click `index.html` in your browser. Or serve locally: ```bash npx serve . # or python3 -m http.server 3000 ``` "); zip.file(folder+".gitignore",".DS_Store node_modules/ .env "); } /* ===== MAIN ===== */ var sc=document.createElement("script"); sc.src="https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.1/jszip.min.js"; sc.onerror=function(){ if(lbl)lbl.textContent="Download ZIP"; alert("JSZip load failed — check connection."); }; sc.onload=function(){ var zip=new JSZip(); var base=(_phFname||"output").replace(/.[^.]+$/,""); var app=base.toLowerCase().replace(/[^a-z0-9]+/g,"_").replace(/^_+|_+$/g,"")||"my_app"; var folder=app+"/"; var vc=document.getElementById("panel-content"); var panelTxt=vc?(vc.innerText||vc.textContent||""):""; var lang=detectLang(_phCode,panelTxt); if(_phIsHtml){ buildVanillaHtml(zip,folder,app,_phCode); } else if(lang==="flutter"){ buildFlutter(zip,folder,app,_phCode,panelTxt); } else if(lang==="react-native"){ buildReactNative(zip,folder,app,_phCode,panelTxt); } else if(lang==="swift"){ buildSwift(zip,folder,app,_phCode,panelTxt); } else if(lang==="kotlin"){ buildKotlin(zip,folder,app,_phCode,panelTxt); } else if(lang==="react"){ buildReact(zip,folder,app,_phCode,panelTxt); } else if(lang==="vue"){ buildVue(zip,folder,app,_phCode,panelTxt); } else if(lang==="angular"){ buildAngular(zip,folder,app,_phCode,panelTxt); } else if(lang==="python"){ buildPython(zip,folder,app,_phCode); } else if(lang==="node"){ buildNode(zip,folder,app,_phCode); } else { /* Document/content workflow */ var title=app.replace(/_/g," "); var md=_phAll||_phCode||panelTxt||"No content"; zip.file(folder+app+".md",md); var h=""+title+""; h+="

"+title+"

"; var hc=md.replace(/&/g,"&").replace(//g,">"); hc=hc.replace(/^### (.+)$/gm,"

$1

"); hc=hc.replace(/^## (.+)$/gm,"

$1

"); hc=hc.replace(/^# (.+)$/gm,"

$1

"); hc=hc.replace(/**(.+?)**/g,"$1"); hc=hc.replace(/ {2,}/g,"

"); h+="

"+hc+"

Generated by PantheraHive BOS
"; zip.file(folder+app+".html",h); zip.file(folder+"README.md","# "+title+" Generated by PantheraHive BOS. Files: - "+app+".md (Markdown) - "+app+".html (styled HTML) "); } zip.generateAsync({type:"blob"}).then(function(blob){ var a=document.createElement("a"); a.href=URL.createObjectURL(blob); a.download=app+".zip"; a.click(); URL.revokeObjectURL(a.href); if(lbl)lbl.textContent="Download ZIP"; }); }; document.head.appendChild(sc); }function phShare(){navigator.clipboard.writeText(window.location.href).then(function(){var el=document.getElementById("ph-share-lbl");if(el){el.textContent="Link copied!";setTimeout(function(){el.textContent="Copy share link";},2500);}});}function phEmbed(){var runId=window.location.pathname.split("/").pop().replace(".html","");var embedUrl="https://pantherahive.com/embed/"+runId;var code='';navigator.clipboard.writeText(code).then(function(){var el=document.getElementById("ph-embed-lbl");if(el){el.textContent="Embed code copied!";setTimeout(function(){el.textContent="Get Embed Code";},2500);}});}