Building an organized knowledge base often means dealing with content from many sources. From videos to PDFs, getting these diverse formats into a consistent, analyzable structure can be time-consuming. This is where the AI agent skill media-ingest comes in. It helps users by converting raw media and text into structured brain pages, making information readily accessible and interconnected. Developers and researchers who need to integrate various content types into their knowledge systems will find this tool useful.
How media-ingest Works: A Five-Phase Workflow
The skill operates through a defined, five-phase workflow to ensure content is processed thoroughly and accurately. This structured approach helps maintain consistency across different types of input.
- Identify Content Format: The first step is to accurately identify the format of the content. This could be anything from a video file to a GitHub repository or a PDF document.
- Fetch and Transcribe/Extract Text: Once the format is known, the skill fetches the content. For audio and video, it transcribes the speech to text. For documents and other text-based formats, it extracts the relevant text. For images, it performs optical character recognition (OCR).
- Upload Raw File for Provenance: To maintain a clear record, the original raw file is uploaded. This ensures provenance and allows for future reference to the source material.
- Create Brain Page with Summaries and Highlights: The extracted text and analysis are then used to create a new brain page. This page includes concise summaries and key highlights, providing an immediate overview of the content.
- Extract Entities and Backlink Mentioned People and Companies: The final phase involves deeper analysis. The tool extracts entities such as names of people and companies mentioned within the content. These entities are then cross-linked to existing brain pages, building a network of interconnected information.
Designed for
Accuracy and Utility
The goal of this skill is to provide accurate and useful insights, not just raw data dumps. This means it follows specific constraints to ensure quality:
- Accuracy Over Completeness: When speech-to-text processing encounters issues, the skill avoids fabricating transcripts. Instead, it prioritizes accuracy by flagging areas where transcription might be incomplete or uncertain.
- OCR Quality Checks: For documents requiring OCR, the skill flags any content where the OCR quality falls below an 80% readability threshold. This prevents poor quality text from being integrated as reliable data.
The design principles also guide what the tool does not do. It is not for dumping unanalyzed transcripts directly into your system. It does not skip entity extraction, as this is crucial for building interconnected knowledge. Furthermore, it avoids filing content by its media format (e.g., all videos in one folder) in favor of organizing by subject matter, which promotes a more useful knowledge structure.
Inputs for the skill include a source URL or a file path. Users can also provide optional title or slug overrides if they want to customize the brain page's identifier. The skill responds to clear triggers like 'watch this video', 'ingest this PDF', or 'check out this repo', making it intuitive to use.
Practical
Examples and Internal Tools
Consider a scenario where you've found a research paper as a PDF and a related conference talk as a YouTube video. Instead of manually summarizing both and trying to link the concepts, you can use the media-ingest skill. Triggering it with 'ingest this PDF' for the paper and 'watch this video' for the talk will result in two structured brain pages. Each page will have summaries, highlights, and cross-linked entities for authors, organizations, and key concepts, effectively connecting the information within your brain. For instance, if both sources mention a specific company, that company will be backlinked across both brain pages.
Internally, the skill uses a suite of tools to perform its functions. These include capabilities for searching existing knowledge, querying for specific information, creating and retrieving new brain pages, establishing links between pages, creating timeline entries for events or data points, and managing file uploads to store source content.
Frequently Asked Questions
Q: What content formats can media-ingest process? A: It processes a wide range, including videos, audio files, PDFs, books, screenshots, and GitHub repositories.
Q: What is the primary output of the skill? A: The primary output is a structured brain page for each ingested item, complete with analysis, transcripts (where applicable), summaries, highlights, and entity cross-linking.
Q: Does it prioritize speed over accuracy in its processing? A: No, the skill is designed to prioritize accuracy over completeness, especially concerning transcripts and OCR quality, to ensure reliable information.
This skill offers a methodical way to bring diverse information into a unified, structured knowledge base. It focuses on delivering analyzed, interconnected content, supporting a more robust personal or team knowledge system.
