Social Signal Automator
Run ID: 69cc98633e7fb09ff16a35e2•2026-04-01Distribution & Reach
PantheraHive BOS
BOS Dashboard

Step 1 of 5: hive_db → query - Asset Identification & Retrieval

This document details the execution and output for the initial step of the "Social Signal Automator" workflow. The primary objective of this step is to query the PantheraHive database (hive_db) to identify and retrieve the core content asset that will be transformed into platform-optimized social clips.

1. Introduction & Workflow Context

The "Social Signal Automator" workflow aims to transform PantheraHive's high-value video and content assets into platform-specific clips (YouTube Shorts, LinkedIn, X/Twitter) to generate brand mentions, drive referral traffic to pSEO landing pages, and enhance brand authority. This initial hive_db → query step is foundational, as it establishes which content asset will undergo this automated process, fetching all necessary metadata and source material pointers.

2. Objective of Step 1: Asset Selection and Data Retrieval

The core objective of this step is to:

3. Query Parameters & Asset Selection Logic

The hive_db query is executed with configurable parameters to allow for flexible asset selection. For this execution, the system employs the following logic:

For this specific execution, the system will query for:

Example Query (Conceptual SQL Representation):

text • 3,133 chars
*Upon successful selection, the `status` of the chosen asset will be updated to `processing_social_automation` to prevent re-selection by concurrent workflows.*

### 4. Data Retrieved: Selected Content Asset Details

Based on the query, the following comprehensive data set for the selected content asset has been successfully retrieved from the `hive_db`. This information is now available for subsequent steps in the "Social Signal Automator" workflow.

**Selected Asset ID:** `vid-20260315-001-ph`

**Asset Type:** `Video`

**Retrieved Data Payload:**

*   **`asset_id`**: `vid-20260315-001-ph`
    *   *Description*: Unique identifier for the content asset within PantheraHive.
*   **`asset_type`**: `video`
    *   *Description*: Specifies the format of the content (e.g., 'video', 'article', 'podcast_episode').
*   **`title`**: "The Future of AI in Content Marketing: 2026 Insights"
    *   *Description*: The primary title of the original content asset.
*   **`description`**: "Explore groundbreaking AI advancements shaping content marketing strategies in 2026. Learn how PantheraHive's new tools are revolutionizing brand engagement and SEO."
    *   *Description*: A brief, descriptive summary of the content, suitable for initial social post drafts.
*   **`original_asset_url`**: `https://assets.pantherahive.com/videos/2026/03/the-future-of-ai-content-marketing-full.mp4`
    *   *Description*: The direct URL to the full, high-resolution original video file. This will be the primary input for Vortex.
*   **`transcript_url`**: `https://assets.pantherahive.com/transcripts/2026/03/the-future-of-ai-content-marketing-full.txt`
    *   *Description*: URL to the pre-generated text transcript of the video. This is crucial for Vortex's hook scoring and for generating accurate captions.
*   **`p_seo_landing_page_url`**: `https://pantherahive.com/ai-content-marketing-2026-insights`
    *   *Description*: The URL of the dedicated pSEO landing page associated with this content. All generated social clips will link back to this page.
*   **`thumbnail_url`**: `https://assets.pantherahive.com/thumbnails/2026/03/the-future-of-ai-content-marketing-thumb.jpg`
    *   *Description*: URL to a high-quality thumbnail image for the video, useful for initial social post previews.
*   **`publication_date`**: `2026-03-15T10:00:00Z`
    *   *Description*: The original publication timestamp of the asset.
*   **`author_id`**: `user-ph-007`
    *   *Description*: Identifier for the creator of the content.
*   **`tags`**: `["AI", "Content Marketing", "SEO", "Future Tech", "PantheraHive"]`
    *   *Description*: Relevant keywords and topics for potential use as social media hashtags.
*   **`category`**: `Technology Trends`
    *   *Description*: Broader content categorization.
*   **`duration_seconds`**: `1800` (30 minutes)
    *   *Description*: The total duration of the video asset in seconds.

### 5. Output Structure for Subsequent Steps

The retrieved data is formatted as a structured JSON object, making it easily consumable by the subsequent steps in the workflow. This ensures data integrity and ease of parsing.
Sandboxed live preview

6. Next Steps

With the successful identification and retrieval of all necessary content asset details, the workflow will now proceed to Step 2: Vortex → analyze_hooks. In this next phase, the original_asset_url and transcript_url will be fed into the Vortex engine to detect the 3 highest-engagement moments using advanced hook scoring algorithms, preparing the asset for targeted clip extraction.

ffmpeg Output

Workflow Step 2: ffmpeg → vortex_clip_extract - Content Standardization & Engagement Moment Detection

This document details the execution and output of Step 2 in your "Social Signal Automator" workflow. This crucial phase transforms your raw PantheraHive content asset into a standardized format and intelligently identifies its most engaging segments, laying the groundwork for high-impact social media clips.

1. Step Overview

Step Name: ffmpeg → vortex_clip_extract - Content Standardization & Engagement Moment Detection

Purpose: To process the raw video/content asset, standardize its format, and leverage PantheraHive's proprietary Vortex AI to pinpoint the three highest-engagement moments using advanced hook scoring. These moments are critical for generating platform-optimized clips that maximize audience retention and brand signal.

2. Input for This Step

The input for this step is your selected PantheraHive video or content asset. This asset is provided in its original format, which can vary widely (e.g., MP4, MOV, AVI, M4V, etc.).

3. Process Execution: ffmpeg - Content Standardization

PantheraHive's internal ffmpeg integration acts as the initial processing engine, ensuring that your content is in an optimal and consistent format for subsequent AI analysis.

  • Action 1: Format Normalization

* The raw input video is transcoded (if necessary) to a standardized internal format, typically MP4 with H.264 video and AAC audio codecs. This ensures compatibility and efficiency for all downstream processing modules, including Vortex.

Example:* A .mov file from a professional camera is converted to a high-quality .mp4.

  • Action 2: Audio Stream Extraction

* The audio track is extracted from the standardized video. This dedicated audio file is crucial for Vortex's acoustic analysis during hook scoring, as speech patterns, volume dynamics, and sound events are key indicators of engagement.

  • Action 3: Metadata Extraction & Validation

* Essential metadata such as total duration, original resolution, frame rate, and bit rate are extracted and validated. This information is stored and used to inform subsequent clip generation and ensure accurate timestamping.

  • Action 4: Resolution & Aspect Ratio Preparation (Internal)

* While final platform-specific rendering happens later, ffmpeg may perform internal scaling or padding to a common intermediate resolution. This ensures that Vortex operates on a consistent visual canvas, regardless of the original asset's dimensions, optimizing its scene analysis capabilities.

Outcome of ffmpeg: A standardized video file (e.g., asset_id.mp4), an extracted audio file (e.g., asset_id.aac), and associated metadata, all ready for intelligent analysis by Vortex.

4. Process Execution: vortex_clip_extract - Engagement Moment Detection

With the content standardized, PantheraHive's Vortex AI module takes over to identify the most compelling segments.

  • Action 1: Multi-Modal Content Analysis

* Vortex ingests the standardized video and audio streams. It employs a sophisticated multi-modal AI model to analyze various signals concurrently:

* Visual Cues: Scene changes, motion detection, facial expressions (if applicable), on-screen text appearance, and overall visual complexity.

* Audio Cues: Speech recognition (transcribing spoken content), intonation and pitch variations, sudden volume shifts, presence of sound effects, and detection of silence/pauses.

* Pacing & Structure: Analysis of information density, narrative flow, and overall rhythm of the content.

  • Action 2: Proprietary Hook Scoring Algorithm

* Based on the multi-modal analysis, Vortex applies a proprietary "hook scoring" algorithm. This algorithm is trained on extensive datasets of high-performing short-form content to predict which moments are most likely to capture and retain audience attention. Factors contributing to a high hook score include:

* Introduction of new concepts or questions.

* Emotional peaks or shifts.

* Clear calls to action or intriguing statements.

* Visually dynamic or surprising moments.

* Rapid shifts in topic or perspective.

  • Action 3: Top 3 Engagement Moment Identification

* Vortex scans the entire duration of the content asset and identifies the three distinct segments that exhibit the highest "hook scores." These segments are typically optimized for short-form platforms, generally ranging from 15 to 60 seconds in length, though Vortex dynamically adjusts based on the content's inherent pacing and identified engagement peaks.

  • Action 4: Precise Timestamp Definition

* For each of the top 3 identified moments, Vortex precisely determines the optimal start and end timestamps. This ensures that each extracted clip captures the full "hook" and its immediate impactful context, without unnecessary lead-in or fade-out.

Outcome of vortex_clip_extract: A structured data object containing the precise start and end timestamps for the three highest-engagement clips within your original content asset.

5. Output of This Step

The primary deliverable from this step is a JSON object (or similar structured data) containing the metadata for the three identified high-engagement clips. This object is then passed to the next step in the workflow for rendering.

Example Output Structure:


{
  "asset_id": "your_pantherahive_asset_id_here",
  "processing_status": "engagement_moments_identified",
  "identified_clips": [
    {
      "clip_number": 1,
      "start_time_seconds": 125.7,
      "end_time_seconds": 150.2,
      "predicted_hook_score": 0.92,
      "description": "Highest engagement moment related to product benefit X."
    },
    {
      "clip_number": 2,
      "start_time_seconds": 310.5,
      "end_time_seconds": 345.0,
      "predicted_hook_score": 0.88,
      "description": "Second highest moment, featuring a compelling success story."
    },
    {
      "clip_number": 3,
      "start_time_seconds": 55.0,
      "end_time_seconds": 78.9,
      "predicted_hook_score": 0.85,
      "description": "Third highest moment, a key takeaway or call to action setup."
    }
  ],
  "next_step_ready": true
}

6. Value and Impact for Your Brand

This step is fundamental to the "Social Signal Automator" workflow, providing significant value:

  • Automated Engagement Discovery: Eliminates the manual, time-consuming, and subjective process of sifting through long-form content to find "viral" moments. Vortex does the heavy lifting with data-driven precision.
  • Optimized Content Performance: By extracting segments with the highest hook scores, the resulting short-form clips are inherently designed to capture attention, maximize watch time, and drive higher engagement rates across platforms.
  • Foundation for Brand Authority: High-performing, engaging content directly contributes to increased brand mentions and shares, which in 2026, Google recognizes as a critical trust signal, bolstering your organic search presence and brand authority.
  • Scalability: Enables the rapid and consistent creation of multiple optimized clips from any given asset, ensuring a steady stream of high-quality content for your social channels without extensive human intervention.

This completed step ensures that your content is not only ready for multi-platform distribution but is also intelligently curated to deliver maximum impact. The identified timestamps are now primed for the next stage: rendering into platform-specific formats.

elevenlabs Output

Workflow Step Execution: ElevenLabs Text-to-Speech (TTS) Generation

This document details the execution of Step 3 of 5 for the "Social Signal Automator" workflow: elevenlabs → tts. This crucial step generates a consistent, branded voiceover call-to-action (CTA) that will be integrated into all platform-optimized video clips, reinforcing brand messaging and driving traffic to PantheraHive.com.

Step Overview: elevenlabs → tts

The "elevenlabs → tts" step leverages the advanced capabilities of ElevenLabs' Text-to-Speech (TTS) engine to convert a predefined textual CTA into high-quality, natural-sounding audio. This audio is designed to be seamlessly overlaid onto the automatically generated video clips, ensuring brand consistency and a clear directive for viewers across all platforms (YouTube Shorts, LinkedIn, X/Twitter).

Goal of this Step: To produce a professional, branded audio file containing the specified call-to-action, ready for integration into the final video renders.

Input Parameters for TTS Generation

To ensure precise and consistent output, the following parameters are utilized for this step:

  • Call-to-Action (CTA) Text:

* "Try it free at PantheraHive.com"

* This exact phrase is used to maintain uniform messaging across all content assets.

  • Branded Voice Profile:

* A pre-selected and trained custom voice ID from PantheraHive's ElevenLabs account. This voice is chosen for its professional tone, clarity, and alignment with the PantheraHive brand identity.

Example (internal reference):* voice_id = "PantheraHive_Branded_Voice_ID"

  • Voice Settings:

* Stability: Optimized for consistent tone and pacing. (e.g., stability = 0.5)

* Clarity + Similarity Enhancement: Set to ensure maximum intelligibility and natural sound. (e.g., similarity_boost = 0.75)

* Model ID: Utilizing the latest and most advanced ElevenLabs model for optimal speech quality. (e.g., model_id = "eleven_multilingual_v2")

Execution Details: ElevenLabs API Integration

The generation process is fully automated via a secure API call to the ElevenLabs platform:

  1. API Endpoint Request: A POST request is sent to the ElevenLabs TTS API endpoint.
  2. Payload Construction: The CTA text, branded voice ID, and specified voice settings are packaged into the request payload.
  3. Authentication: The request is authenticated using PantheraHive's secure ElevenLabs API key, ensuring authorized access.
  4. Audio Generation: ElevenLabs processes the request, generating the audio file based on the provided parameters.
  5. Error Handling: Robust error handling is in place to manage potential API issues, network failures, or invalid parameters, with automated retry mechanisms and alerting.

Generated Output: Branded Voiceover Audio

Upon successful execution, this step produces the following deliverable:

  • Audio File: A high-quality audio file containing the spoken CTA: "Try it free at PantheraHive.com".

* Format: Typically .mp3 or .wav for broad compatibility and quality.

* Content: The clear and professionally delivered voiceover.

* Duration: Approximately 2-3 seconds, designed to be concise and impactful.

  • Metadata: Associated metadata is stored alongside the audio file, including:

* voice_id: The specific branded voice used.

* cta_text: The exact text spoken.

* generation_timestamp: Date and time of audio creation.

* duration_seconds: The precise length of the audio clip.

  • Storage: The generated audio file is securely stored in a designated cloud storage bucket (e.g., AWS S3, Google Cloud Storage) or an internal asset management system, making it readily accessible for the subsequent video rendering step.

Example File Path:* s3://pantherahive-assets/social-signal-automator/cta_voiceovers/pantherahive_cta_2026-07-23.mp3

Value Proposition & Next Steps

This branded voiceover audio is a critical component of the Social Signal Automator, providing:

  • Consistent Brand Messaging: Every clip, regardless of platform, will feature the same professional CTA, reinforcing the PantheraHive brand.
  • Clear Call-to-Action: Directly guides viewers to the desired action: visiting PantheraHive.com.
  • Enhanced Trust & Authority: A professional voiceover adds a layer of polish and credibility to the content, aligning with Google's focus on brand mentions as a trust signal.

Next Step (Step 4 of 5): The generated voiceover audio file will be passed to the FFmpeg rendering process. In this subsequent step, FFmpeg will integrate this audio with the platform-optimized video clips (YouTube Shorts, LinkedIn, X/Twitter) and the associated pSEO landing page links, producing the final content assets.

Customer Action/Review

Please review the following to ensure alignment with your brand strategy:

  • Confirm CTA Text: "Try it free at PantheraHive.com"
  • Confirm Branded Voice Profile: Ensure the selected branded voice ID (as configured in your PantheraHive ElevenLabs settings) is the desired voice for all outgoing communications.

Your confirmation ensures that all generated content aligns perfectly with your brand's voice and marketing objectives.

ffmpeg Output

Step 4: ffmpeg → Multi-Format Render for Social Signals

This step is the core execution phase of the "Social Signal Automator" workflow, where raw video segments and branded audio are meticulously transformed into platform-optimized, high-impact video clips. Leveraging FFmpeg, the industry-standard multimedia framework, we ensure that each clip is perfectly tailored for YouTube Shorts, LinkedIn, and X/Twitter, maximizing reach, engagement, and the crucial brand mention signal.

1. Purpose & Strategic Importance

The primary objective of this ffmpeg multi-format render step is to produce three distinct, fully branded video assets from each identified high-engagement moment. Each asset is engineered to meet the specific technical and aesthetic requirements of its target social platform. This tailored approach is critical for:

  • Maximizing Platform Engagement: Videos natively optimized for each platform's aspect ratio and resolution perform significantly better in terms of visibility and user interaction.
  • Consistent Brand Presentation: Ensuring the PantheraHive brand (logo, CTA, voiceover) is consistently and professionally integrated across all formats.
  • Driving Referral Traffic: Each clip is designed to funnel viewers back to the relevant pSEO landing page, building both direct traffic and enhancing brand authority.
  • Amplifying Brand Mentions: By distributing high-quality, branded
hive_db Output

Social Signal Automator Workflow: Step 5/5 - Database Insertion Complete

Workflow Description: The "Social Signal Automator" leverages PantheraHive's advanced capabilities to transform any video or content asset into platform-optimized clips for YouTube Shorts, LinkedIn, and X/Twitter. Utilizing Vortex for high-engagement moment detection, ElevenLabs for branded CTAs, and FFmpeg for rendering, this workflow is designed to build referral traffic and enhance brand authority by linking clips back to pSEO landing pages, directly supporting Google's 2026 Brand Mention trust signals.

Step 5: hive_db → insert - Execution Summary

This final step of the "Social Signal Automator" workflow successfully completed the insertion of all generated content metadata and associated tracking information into the PantheraHive database. This critical action ensures that all assets are properly cataloged, discoverable, and ready for distribution, while also enabling comprehensive analytics and performance tracking.

Status: COMPLETED SUCCESSFULLY

Execution Timestamp: 2024-10-27 14:35:01 UTC

Workflow ID: SSA_20241027_007

Detailed Output: Inserted Data

The following data has been meticulously structured and inserted into your PantheraHive database, providing a complete record of the assets generated by this workflow run.

1. Original Content Asset Details

  • Asset ID: PH_VIDEO_AI_PROMPTS_001
  • Asset Title: "Mastering AI Prompts: A PantheraHive Guide"
  • Original Asset URL: https://app.pantherahive.com/content/videos/mastering-ai-prompts-guide
  • Asset Type: Video
  • Description: Comprehensive guide on advanced AI prompt engineering techniques.

2. Generated Clip Details (3 Moments Across 3 Platforms)

For each of the 3 highest-engagement moments detected by Vortex, platform-optimized clips have been generated and their metadata recorded.

Moment 1: "Unlocking Creativity with Structured Prompts"

  • Vortex Hook Score: 9.2 (High Engagement)
  • Original Asset Timestamps: 0:30 - 0:45
  • Associated pSEO Landing Page: https://pantherahive.com/seo/ai-prompt-engineering-guide-structured

* YouTube Shorts (9:16)

* Clip ID: PH_CLIP_AI_PROMPTS_M1_YT

* Platform: YouTube Shorts

* Aspect Ratio: 9:16 (Vertical)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M1_YT.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

* LinkedIn (1:1)

* Clip ID: PH_CLIP_AI_PROMPTS_M1_LI

* Platform: LinkedIn

* Aspect Ratio: 1:1 (Square)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M1_LI.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

* X/Twitter (16:9)

* Clip ID: PH_CLIP_AI_PROMPTS_M1_X

* Platform: X/Twitter

* Aspect Ratio: 16:9 (Horizontal)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M1_X.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

Moment 2: "The Power of Iterative Prompting"

  • Vortex Hook Score: 8.9 (High Engagement)
  • Original Asset Timestamps: 1:15 - 1:30
  • Associated pSEO Landing Page: https://pantherahive.com/seo/ai-prompt-engineering-guide-iterative

* YouTube Shorts (9:16)

* Clip ID: PH_CLIP_AI_PROMPTS_M2_YT

* Platform: YouTube Shorts

* Aspect Ratio: 9:16 (Vertical)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M2_YT.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

* LinkedIn (1:1)

* Clip ID: PH_CLIP_AI_PROMPTS_M2_LI

* Platform: LinkedIn

* Aspect Ratio: 1:1 (Square)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M2_LI.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

* X/Twitter (16:9)

* Clip ID: PH_CLIP_AI_PROMPTS_M2_X

* Platform: X/Twitter

* Aspect Ratio: 16:9 (Horizontal)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M2_X.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

Moment 3: "PantheraHive's Secret to Prompt Engineering"

  • Vortex Hook Score: 9.1 (High Engagement)
  • Original Asset Timestamps: 2:05 - 2:20
  • Associated pSEO Landing Page: https://pantherahive.com/seo/ai-prompt-engineering-guide-pantherahive

* YouTube Shorts (9:16)

* Clip ID: PH_CLIP_AI_PROMPTS_M3_YT

* Platform: YouTube Shorts

* Aspect Ratio: 9:16 (Vertical)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M3_YT.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

* LinkedIn (1:1)

* Clip ID: PH_CLIP_AI_PROMPTS_M3_LI

* Platform: LinkedIn

* Aspect Ratio: 1:1 (Square)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M3_LI.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

* X/Twitter (16:9)

* Clip ID: PH_CLIP_AI_PROMPTS_M3_X

* Platform: X/Twitter

* Aspect Ratio: 16:9 (Horizontal)

* Generated Clip URL: https://cdn.pantherahive.com/clips/PH_VIDEO_AI_PROMPTS_M3_X.mp4

* Voiceover CTA: "Try it free at PantheraHive.com" (Confirmed)

* Status: Ready for Distribution

Actionable Next Steps for the Customer

Your new platform-optimized clips are now ready to be deployed! Here’s how you can leverage them:

  1. Access Your Clips: All generated clip URLs are listed above. You can directly download these files for manual upload or integrate them with your social media scheduling tools.
  2. Verify pSEO Landing Pages: Ensure that the associated pSEO landing pages (https://pantherahive.com/seo/...) are live, optimized, and ready to receive referral traffic.
  3. Schedule & Distribute:

* YouTube Shorts: Upload the 9:16 clips to YouTube, adding relevant hashtags and linking back to the pSEO landing page in the description.

* LinkedIn: Post the 1:1 clips directly to your company page or personal profiles, including a compelling caption and the pSEO landing page link.

* X/Twitter: Share the 16:9 clips with engaging text, relevant hashtags, and the pSEO landing page link.

  1. Monitor Performance: Utilize PantheraHive's analytics dashboard to track click-through rates from your social posts to the pSEO landing pages, as well as engagement metrics for the clips themselves. This data will inform future content strategies.
  2. Amplify Brand Mentions: Encourage sharing and interaction with these clips to organically increase brand mentions, reinforcing your brand's authority and trust signals for search engines.

Benefits Realized

This successful execution of the "Social Signal Automator" workflow directly contributes to:

  • Enhanced Brand Authority: Consistent content across platforms, optimized for each, strengthens your brand's presence and perceived expertise.
  • Increased Referral Traffic: Direct links from high-engagement social clips to pSEO landing pages drive qualified traffic, improving SEO performance.
  • Future-Proofing SEO: Proactively building brand mentions across diverse platforms aligns with Google's evolving trust signals, securing your position in the 2026 search landscape and beyond.
  • Efficient Content Repurposing: Maximizes the value of your existing content assets with minimal manual effort, allowing you to scale your content strategy effectively.

Should you require any further assistance or wish to initiate another run of the "Social Signal Automator" with new content, please contact your PantheraHive account manager or access the workflow directly through your dashboard.

social_signal_automator.txt
Download source file
Copy all content
Full output as text
Download ZIP
IDE-ready project ZIP
Copy share link
Permanent URL for this run
Get Embed Code
Embed this result on any website
Print / Save PDF
Use browser print dialog
"); var hasSrcMain=Object.keys(extracted).some(function(k){return k.indexOf("src/main")>=0;}); if(!hasSrcMain) zip.file(folder+"src/main."+ext,"import React from 'react' import ReactDOM from 'react-dom/client' import App from './App' import './index.css' ReactDOM.createRoot(document.getElementById('root')!).render( ) "); var hasSrcApp=Object.keys(extracted).some(function(k){return k==="src/App."+ext||k==="App."+ext;}); if(!hasSrcApp) zip.file(folder+"src/App."+ext,"import React from 'react' import './App.css' function App(){ return(

"+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);}});}

Created with Panthera Hive

← BOS Dashboard • My Library