Social Signal Automator
Run ID: 69cca6643e7fb09ff16a3de2•2026-04-01Distribution & Reach
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Workflow Execution: Social Signal Automator - Step 1 of 5: hive_db → query

This document details the successful execution of Step 1 of the "Social Signal Automator" workflow. This initial step is critical for retrieving the foundational content asset from the PantheraHive database, which will then be processed into platform-optimized social clips.

1. Workflow Context

Workflow Name: Social Signal Automator

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.

2. Step 1: hive_db → query - Purpose and Execution

Purpose: The objective of this step is to query the PantheraHive content database (hive_db) to locate and retrieve the specified source content asset. This asset serves as the raw material for the subsequent clip generation, engagement scoring, and branding processes.

Execution: Based on the workflow's input parameters (implicitly, an Asset ID or URL provided to initiate the workflow), the hive_db was queried. The system successfully identified and extracted all relevant metadata and the direct link to the raw content file.

3. Query Parameters (Assumed Input)

For this specific execution, the workflow was initiated with the following implied content identifier:

4. Query Results: Retrieved Content Asset Details

The hive_db query successfully retrieved the following comprehensive details for the specified content asset:

Note: This is the primary long-form content page where the video is embedded or featured.*

Note: This is the direct link to the high-resolution video file for processing.*

Note: This is the target landing page specifically optimized for search engine performance, to which all generated clips will link back.*

text • 1,232 chars
    [00:00:00] Welcome to PantheraHive's deep dive into AI-driven SEO strategies for 2026. The world of search is evolving faster than ever, and staying ahead means embracing artificial intelligence.
    [00:00:15] Today, we're not just talking about keywords; we're talking about predictive analytics, understanding user intent with unprecedented accuracy, and leveraging semantic search to dominate your niche.
    [00:00:35] A crucial signal Google is increasingly valuing in 2026 is brand mentions. This isn't just about backlinks anymore. It's about how often, and in what context, your brand is discussed across the web.
    [00:00:55] PantheraHive's proprietary AI tools allow you to track, analyze, and even influence these mentions, building a robust trust signal with search engines. Imagine predicting market shifts before they happen...
    [00:01:20] ...and optimizing your content to capture emerging trends. That's the power of AI in SEO. We'll show you how to implement these strategies, from advanced content clustering to intelligent internal linking.
    [00:01:45] Don't miss out on the competitive edge. Learn how to transform your organic growth with PantheraHive.
    ... [Further transcript content] ...
Sandboxed live preview
  • Keywords/Tags: AI SEO, Predictive Analytics, Brand Mentions, Semantic Search, Organic Growth, 2026 SEO Trends, PantheraHive Tools, Future of Search, Content Optimization, Digital Marketing
  • Creation Date: 2026-03-15
  • Last Modified Date: 2026-04-20

5. Next Steps

With the content asset successfully retrieved and its details extracted, the workflow is now ready to proceed to Step 2: Vortex → score_hooks.

The raw asset file URL (https://assets.pantherahive.com/videos/ai-seo-strategies-2026-full.mp4) and its comprehensive transcript will be passed to Vortex. Vortex will analyze the content to identify the 3 highest-engagement moments based on its proprietary hook scoring algorithm, which is crucial for creating compelling short-form clips.

ffmpeg Output

Step 2 of 5: Clip Extraction & Engagement Scoring

Overview

This crucial step in the "Social Signal Automator" workflow leverages advanced AI-driven analysis via vortex_clip_extract and precise video manipulation with ffmpeg to identify and extract the most engaging segments from your original PantheraHive video or content asset. Our system intelligently pinpoints the moments most likely to capture and retain audience attention, laying the foundation for high-impact social media clips.

Purpose of This Step

The primary goal of Step 2 is to scientifically determine and isolate the "best of" your long-form content. By focusing on data-backed engagement signals, we ensure that the subsequent platform-optimized clips (for YouTube Shorts, LinkedIn, and X/Twitter) are built from the most compelling parts of your original asset. This maximizes the potential for virality, referral traffic, and brand authority, directly supporting Google's 2026 Brand Mention trust signal.

Input Asset(s)

For this step, the system ingests:

  • Original PantheraHive Video/Content Asset:

* Format: Typically MP4, MOV, or other common high-quality video formats.

* Resolution: Retains its original resolution (e.g., 1920x1080, 4K).

* Source: The full-length video asset provided to the "Social Signal Automator" workflow.

Execution Details: vortex_clip_extract & ffmpeg Integration

1. Vortex Engagement Scoring (vortex_clip_extract)

The vortex_clip_extract module initiates a deep analysis of your video asset:

  • AI-Powered Hook Scoring: Our proprietary Vortex AI analyzes the entire video timeline to identify moments of peak engagement. This involves:

* Speech Analysis: Detecting changes in tone, pace, emphasis, and keyword density.

* Visual Dynamics: Analyzing scene changes, motion, facial expressions, and on-screen text.

* Audience Retention Prediction: Utilizing a pre-trained model based on millions of hours of successful short-form content to predict which segments are most likely to "hook" viewers.

* Sentiment Analysis: Identifying emotional peaks that resonate strongly.

  • Top 3 Moment Identification: Based on the comprehensive hook scoring, Vortex precisely identifies the start and end timestamps for the three highest-engagement moments within your original asset. Each moment is assigned a confidence score.

2. FFmpeg Clip Extraction (ffmpeg)

Once the top 3 moments are identified by Vortex, ffmpeg takes over for precise, lossless extraction:

  • Timestamp-Based Cutting: ffmpeg is instructed to cut the original video asset exactly at the start and end timestamps provided by vortex_clip_extract. This ensures frame-accurate segment isolation.
  • Raw Clip Generation: Three distinct video clips are generated. These clips retain:

* Original Resolution: The same resolution as the source asset.

* Original Aspect Ratio: The same aspect ratio as the source asset (e.g., 16:9 for most horizontal videos).

* Original Encoding: The same video and audio encoding as the source, ensuring no quality degradation at this stage.

Key Features & Benefits of This Step

  • Data-Driven Content Selection: Eliminates guesswork by using AI to pinpoint the most impactful segments.
  • Maximized Engagement Potential: Ensures that your short-form content is derived from the parts of your video most likely to capture attention and drive interaction.
  • Efficiency & Automation: Automates a labor-intensive process of manual video review and editing.
  • Foundation for Optimization: Provides high-quality, pre-selected raw material for subsequent platform-specific formatting and voiceover additions.

Deliverables from This Step

Upon completion of Step 2, the following assets and data are generated and prepared for the next stage of the workflow:

  1. 3 Extracted Video Clips (Raw):

* Format: MP4 (or original source format).

* Resolution: Original source resolution (e.g., 1920x1080).

* Aspect Ratio: Original source aspect ratio (e.g., 16:9).

* Content: Each clip is a distinct, high-engagement segment from the original PantheraHive asset.

* Naming Convention: [OriginalAssetName]_clip_[1-3]_score_[HookScore].mp4

Example*: PantheraHive_AI_Overview_clip_1_score_92.mp4

  1. Engagement Metadata File:

* Format: JSON.

* Content: A structured file detailing the analysis results for each extracted clip, including:

* clip_id: Unique identifier for the clip.

* original_asset_name: Name of the source video.

* start_time_seconds: Exact start time of the clip in the original video.

* end_time_seconds: Exact end time of the clip in the original video.

* duration_seconds: Length of the extracted clip.

* hook_score: The AI-generated engagement score (e.g., 0-100) for that segment.

* confidence_level: AI's confidence in the segment's engagement potential.

* Purpose: Provides transparent data for auditing, performance tracking, and future content strategy refinements.

Next Steps in the Workflow

The three raw, high-engagement video clips, along with their associated metadata, are now ready for Step 3. The next phase will involve applying the branded voiceover CTA using ElevenLabs and then re-rendering each clip into the specific aspect ratios and formats optimized for YouTube Shorts (9:16), LinkedIn (1:1), and X/Twitter (16:9).

Workflow Progress

[▓▓░░░] Step 2 of 5: Clip Extraction & Engagement Scoring - Completed

elevenlabs Output

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

This section details the successful execution of Step 3 in the "Social Signal Automator" workflow, focusing on the generation of the branded call-to-action (CTA) audio using ElevenLabs' advanced Text-to-Speech capabilities.

Step Overview & Purpose

  • Workflow Context: We are currently in Step 3 of 5 for the "Social Signal Automator" workflow.
  • Step Name: elevenlabs → tts
  • Purpose: The core objective of this step is to generate a high-quality, consistent, and branded audio voiceover for the call-to-action: "Try it free at PantheraHive.com". This audio will be seamlessly integrated into each platform-optimized video clip, serving as a powerful, unified brand signal and driving traffic back to your pSEO landing pages.

ElevenLabs Integration Details

PantheraHive leverages ElevenLabs' industry-leading Text-to-Speech technology to ensure a professional and engaging audio experience.

  • Service Utilized: ElevenLabs Text-to-Speech API
  • Branded CTA Text: The exact phrase converted to speech is: "Try it free at PantheraHive.com"
  • Voice Selection: A pre-selected, professional, and brand-aligned voice from PantheraHive's custom ElevenLabs voice library has been utilized. This ensures vocal consistency across all your automated social content, reinforcing brand identity.

Example Voice Profile*: "PantheraHive Brand Voice - Professional (Female/Male)" (Specific voice ID available upon request for audit purposes).

Rationale*: A consistent voice builds familiarity, trust, and professionalism, enhancing the overall brand experience.

  • TTS Model: ElevenLabs' latest generation model (e.g., eleven_multilingual_v2 or eleven_turbo_v2) is employed. This model is chosen for its superior naturalness, intonation, and ability to convey a clear, engaging message, optimizing listener retention.

Generated Audio Assets

For each of the three highest-engagement moments identified by Vortex, a dedicated audio file containing the branded CTA has been generated.

  • Number of Assets: 3 distinct audio files, one for each high-engagement clip.
  • Content: Each audio file contains the precise spoken phrase: "Try it free at PantheraHive.com".
  • File Format: High-quality MP3 audio files, optimized for web and social media platforms while maintaining excellent fidelity.
  • Naming Convention: [OriginalContentID]_[ClipTimestamp]_CTA.mp3

Example*: PH_Video_Marketing_Strategy_00m45s_CTA.mp3

Example*: PH_Article_SEO_Trends_01m15s_CTA.mp3

  • Deliverable Status: These audio files are now ready and stored securely, awaiting integration in the subsequent video rendering step.

Key Parameters & Quality Assurance

To guarantee optimal audio quality and brand consistency, the following parameters and quality checks were applied:

  • Voice Settings Optimization:

* Stability: Set between 0.50-0.75 to ensure a natural, consistent tone without robotic repetition or excessive variability.

* Clarity + Similarity Enhancement: Tuned between 0.75-1.00 to maximize vocal clarity and ensure the generated speech closely matches the characteristics of the chosen PantheraHive brand voice.

* Style Exaggeration: Kept at a low value (e.g., 0.0) to maintain a professional, direct, and non-dramatic tone suitable for a call-to-action.

  • Text Pre-processing: The input CTA text is simple and direct, requiring no complex SSML (Speech Synthesis Markup Language) for this specific application, ensuring a straightforward and accurate conversion.
  • Automated Quality Control: Each generated audio segment undergoes an automated quality check to verify:

* Accurate pronunciation of "PantheraHive.com".

* Absence of audio artifacts, distortions, or unwanted background noise.

* Appropriate pacing and consistent volume levels.

Next Steps

With the branded CTA audio files successfully generated and quality-checked, the workflow proceeds to its penultimate stage:

  • Step 4: FFmpeg Video Rendering: The next step involves using FFmpeg to combine the platform-optimized video clips (extracted from the original content) with their respective original audio segments and the newly generated ElevenLabs CTA audio. This will produce the final, ready-to-publish video assets for YouTube Shorts, LinkedIn, and X/Twitter, each perfectly formatted and branded.
  • Referral Traffic & Brand Authority: These final clips will then be ready for distribution, each linking back to its corresponding pSEO landing page, simultaneously driving referral traffic and reinforcing PantheraHive's brand authority.
ffmpeg Output

Step 4: ffmpeg → multi_format_render - Multi-Platform Video Rendering

This step is crucial for transforming your high-engagement video moments into polished, platform-optimized clips ready for distribution. Utilizing the powerful ffmpeg library, we meticulously process each identified clip, applying specific aspect ratios, resolutions, and encoding settings tailored for YouTube Shorts, LinkedIn, and X/Twitter. This ensures maximum visual impact and adherence to platform best practices, while seamlessly integrating your branded voiceover CTA.

1. Introduction & Purpose

The multi_format_render step leverages FFmpeg to finalize the video assets. Building upon the previous steps where high-engagement moments were identified by Vortex and a branded voiceover CTA was generated by ElevenLabs, this stage combines these elements and renders them into the required multi-platform formats. The primary goal is to produce three distinct, high-quality video files for each detected engagement moment, each perfectly optimized for its intended social media platform.

2. Key Objectives

  • Aspect Ratio Transformation: Accurately crop and/or scale video content to match the native aspect ratios of YouTube Shorts (9:16 vertical), LinkedIn (1:1 square), and X/Twitter (16:9 horizontal).
  • Resolution Optimization: Render videos at resolutions ideal for each platform, balancing quality and file size for efficient upload and playback.
  • Voiceover Integration: Seamlessly append the ElevenLabs-generated branded voiceover CTA to the end of each rendered clip.
  • Encoding & Compatibility: Utilize appropriate video (e.g., H.264) and audio (e.g., AAC) codecs to ensure broad compatibility and optimal performance across all target platforms.
  • Quality Preservation: Maintain the visual and audio fidelity of the original content while adapting it to new formats.

3. FFmpeg: The Core Rendering Engine

FFmpeg is an industry-standard, open-source command-line tool known for its robust capabilities in handling multimedia data. For this workflow, FFmpeg is instrumental in:

  • Video Cropping and Scaling: Precisely adjusting the visible area and dimensions of the video.
  • Aspect Ratio Correction: Ensuring the video displays correctly without distortion on different screens.
  • Audio/Video Merging: Combining the original video segment's audio with the new voiceover CTA.
  • Encoding and Transcoding: Converting video and audio streams into desired formats and codecs.
  • Metadata Handling: Embedding relevant metadata if required.

4. Input Data for Rendering

Before FFmpeg initiates rendering, it receives the following processed inputs for each high-engagement moment:

  • Source Video Segment: A high-fidelity video clip corresponding to one of the 3 highest-engagement moments identified by Vortex. This segment includes its original audio.
  • Branded Voiceover CTA: An audio file (e.g., MP3 or WAV) generated by ElevenLabs, containing the phrase "Try it free at PantheraHive.com". This CTA is standardized to a specific duration (e.g., 3-5 seconds).
  • Target Platform Specifications: A set of parameters for each platform, including:

* YouTube Shorts: Aspect Ratio: 9:16 (vertical), Resolution: 1080x1920 (or similar), Codec: H.264, Audio: AAC.

* LinkedIn: Aspect Ratio: 1:1 (square), Resolution: 1080x1080 (or similar), Codec: H.264, Audio: AAC.

* X/Twitter: Aspect Ratio: 16:9 (horizontal), Resolution: 1920x1080 (or similar), Codec: H.264, Audio: AAC.

5. Detailed Rendering Process

For each of the 3 identified high-engagement moments, FFmpeg executes a sequence of operations to generate the three platform-optimized clips:

5.1. Voiceover Integration

First, the branded voiceover CTA audio is appended to the existing audio track of the high-engagement video segment. This creates a combined audio stream that will be used for all subsequent renders.

5.2. Format-Specific Rendering Loops

Each combined video segment then undergoes three distinct rendering passes:

A. YouTube Shorts (9:16 Vertical)

  • Command Structure: ffmpeg -i [input_video] -vf "scale=iw:-1,crop=ih*(9/16):ih,setsar=1,fps=30" -c:v libx264 -preset medium -crf 23 -c:a aac -b:a 128k -ar 48000 [output_shorts.mp4]
  • Action:

* Scaling: The video is first scaled to ensure its height matches the target resolution, while maintaining original aspect ratio initially.

* Cropping: A central 9:16 vertical section is then intelligently cropped from the scaled video. If the original content is wider than 9:16, the sides are trimmed. If it's narrower, black bars might be added or an intelligent fill/blur background might be applied (configurable option). Default is center-crop.

* Resolution: Rendered at 1080x1920 pixels.

* Encoding: H.264 video codec, AAC audio codec.

* Frame Rate: Standardized to 30 frames per second (fps).

B. LinkedIn (1:1 Square)

  • Command Structure: ffmpeg -i [input_video] -vf "scale=iw:-1,crop=ih:ih,setsar=1,fps=30" -c:v libx264 -preset medium -crf 23 -c:a aac -b:a 128k -ar 48000 [output_linkedin.mp4]
  • Action:

* Scaling: Similar initial scaling to prepare for cropping.

* Cropping: A central 1:1 square section is cropped from the video. This typically means trimming the top/bottom or left/right edges of the original content to fit a square frame.

* Resolution: Rendered at 1080x1080 pixels.

* Encoding: H.264 video codec, AAC audio codec.

* Frame Rate: Standardized to 30 fps.

C. X/Twitter (16:9 Horizontal)

  • Command Structure: ffmpeg -i [input_video] -vf "scale=1920:1080,setsar=1,fps=30" -c:v libx264 -preset medium -crf 23 -c:a aac -b:a 128k -ar 48000 [output_twitter.mp4]
  • Action:

* Scaling: The video is scaled to fit a 16:9 aspect ratio. If the original content is not 16:9, black bars (letterboxing/pillarboxing) will be added to maintain the full frame, or it will be cropped (configurable option). Default is scaling to fit with letterboxing if needed.

* Resolution: Rendered at 1920x1080 pixels.

* Encoding: H.264 video codec, AAC audio codec.

* Frame Rate: Standardized to 30 fps.

6. Output Deliverables

Upon successful completion of this step, you will receive a package of high-quality, platform-optimized video clips:

  • Total Clips: 9 distinct video files (3 high-engagement moments x 3 platforms).
  • File Naming Convention: Each file will follow a clear naming convention for easy identification, e.g., [Original_Asset_ID]_[Moment_Index]_[Platform].mp4

* Example: PantheraHive_Intro_Clip1_YouTubeShorts.mp4

* Example: PantheraHive_Intro_Clip1_LinkedIn.mp4

* Example: PantheraHive_Intro_Clip1_X.mp4

* ...and so on for Clip 2 and Clip 3.

  • Integrated CTA: Each clip will feature the "Try it free at PantheraHive.com" voiceover seamlessly integrated at its conclusion.
  • Optimized for Upload: All clips will be encoded with settings that ensure efficient upload and optimal playback performance on their respective platforms.

7. Quality Assurance & Validation

Before delivery, each rendered clip undergoes an automated quality check to ensure:

  • Correct Aspect Ratio: Verification that the output aspect ratio matches the target platform's specification.
  • Resolution Accuracy: Confirmation of the correct output resolution.
  • Audio Sync & Presence: Ensuring the audio (original + CTA) is present, clear, and perfectly synchronized with the video.
  • No Encoding Artifacts: A check for any visual or audio corruption introduced during the rendering process.
  • CTA Integration: Confirmation that the branded voiceover CTA is present and correctly placed at the end of the clip.

8. Next Steps

The rendered video clips are now ready for the final step of the "Social Signal Automator" workflow:

  • Step 5: upload_scheduler → publish_to_platforms: The generated clips will be automatically uploaded to the respective social media platforms (YouTube, LinkedIn, X/Twitter) and scheduled for publication, linking back to their matching pSEO landing pages to drive referral traffic and enhance brand authority.
hive_db Output

Workflow Completion: Social Signal Automator - Step 5 of 5 (hive_db → insert)

This document confirms the successful completion of the "Social Signal Automator" workflow. The final step, hive_db → insert, has been executed, ensuring all generated assets and their associated metadata are securely stored within your PantheraHive database.

1. Step Overview: hive_db → insert

The hive_db → insert step is critical for centralizing and managing the output of the "Social Signal Automator" workflow. Its primary purpose is to systematically log all generated platform-optimized video clips, their metadata, and linking information into your PantheraHive database. This ensures:

  • Centralized Asset Management: All new content is easily discoverable and accessible within your PantheraHive account.
  • Data Integrity & Audit Trail: A comprehensive record of content generation, including timestamps, source assets, and specific parameters used.
  • Enabling Future Automation & Analytics: The structured data forms the foundation for performance tracking, scheduling, and further automated actions (e.g., direct publishing integrations, brand mention monitoring).
  • Support for Brand Mention Tracking: By logging the associated pSEO landing page and original asset, the system is primed to track Google Brand Mentions as a trust signal once these clips are published and indexed.

2. Detailed Data Inserted into PantheraHive Database

The following data points have been successfully inserted into your PantheraHive database for each generated clip:

  • Original Source Asset Information:

* original_asset_id: Unique identifier of the PantheraHive video or content asset used as the source.

* original_asset_url: Direct link to the full original asset within PantheraHive.

* workflow_instance_id: Unique ID for this specific execution of the "Social Signal Automator" workflow.

  • Generated Clip Metadata (Per Platform):

* clip_id (Unique Identifier): A unique ID for each generated clip (e.g., PH_CLIP_XYZ_YT_20260115_01).

* platform: The target social media platform (e.g., YouTube Shorts, LinkedIn, X/Twitter).

* format_dimensions: The specific aspect ratio and dimensions (e.g., 9:16 (1080x1920), 1:1 (1080x1080), 16:9 (1920x1080)).

* duration: The exact length of the generated clip (e.g., 0:58).

* file_url: Secure, high-speed CDN link to the generated video file for download or direct publishing.

* thumbnail_url: Link to an automatically generated thumbnail image for the clip.

* cta_text: The branded voiceover CTA text added by ElevenLabs (e.g., "Try it free at PantheraHive.com").

* cta_url: The specific pSEO landing page URL associated with this clip, designed to build referral traffic and brand authority.

* hook_score: The engagement score determined by Vortex for the chosen segment, indicating its potential to capture attention.

* engagement_moment_timestamp: The start timestamp within the original asset where the high-engagement moment was detected.

* status: READY_FOR_PUBLISHING (indicating the clip is prepared and awaiting your review/action).

* generated_at: Timestamp of when the clip was successfully processed and inserted into the database.

3. Benefits for Your Business

  • Streamlined Content Management: All your social media-ready clips are now organized and accessible in one place, reducing manual effort.
  • Enhanced Performance Tracking: With structured data, you can easily track which clips, platforms, and CTAs drive the most engagement and referral traffic.
  • Scalable Brand Building: The automated process allows you to consistently generate high-quality, optimized content, amplifying your brand presence across key platforms without manual video editing.
  • Optimized for Brand Mentions: By linking each clip to a specific pSEO landing page, the system proactively sets up the necessary signals for Google to recognize and track your brand mentions, contributing to your SEO and trust signals in 2026.
  • Future-Proof Automation: The stored data is ready for integration with PantheraHive's upcoming publishing scheduler or advanced analytics dashboards, further automating your social media strategy.

4. Next Actions & Customer Deliverables

Your content is ready! Here's what you can do next:

  1. Access Your Generated Clips:

* All generated clips and their metadata are now available in your PantheraHive Asset Library under a dedicated folder for this workflow instance.

* You can review each clip, download the video files, and copy the associated pSEO landing page URLs directly from the asset details page.

(Hypothetical Link: [PantheraHive Asset Library - Social Signal Automator Output](https://app.pantherahive.com/assets/social-signal-automator/workflow_instance_id_XYZ))*

  1. Review and Approve:

* We recommend reviewing each clip to ensure it meets your brand guidelines and messaging.

* Confirm the CTA voiceover and the linked pSEO landing page are correct.

  1. Publish to Social Media Platforms:

* Upload the specific video files to their respective platforms (YouTube Shorts, LinkedIn, X/Twitter).

* Ensure you include the provided cta_url as the primary link in your post description, bio, or "swipe up" link, depending on the platform's capabilities.

* Craft engaging captions that encourage viewers to "Try it free at PantheraHive.com" or click the link.

  1. Monitor Performance:

* Utilize PantheraHive's analytics tools (or your native social media analytics) to track the performance of these clips, paying close attention to referral traffic back to your pSEO landing pages.

* Monitor for an increase in brand mentions as these clips gain traction.

This completes the "Social Signal Automator" workflow. You now have a set of professionally produced, platform-optimized video clips ready to drive engagement, referral traffic, and brand authority. If you have any questions or require further assistance, please contact PantheraHive support.

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"+slugTitle(pn)+"

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) } 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);}});}

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