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Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.

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• Building custom extraction and processing steps
• Saving custom data types to graph and vector databases
• Adding information to an existing graph

Export data from Google Sheets as CSV for local backups or further processing. The recipe covers checking spreadsheet details, exporting through Google Drive, and reading a specified sheet range directly in CSV format.

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• Keeping a local CSV backup of spreadsheet data
• Exporting spreadsheet data for further processing
• Reading a selected sheet range as CSV

Plan a Zoom meeting bot or recorder, including how it joins meetings, handles live audio, video, and transcripts, and connects backend services for storage, post-processing, and event handling.

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• Designing a bot that joins Zoom meetings
• Planning live audio, video, and transcript processing
• Organizing post-meeting storage and retries

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

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• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.

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• Run data processing jobs in parallel
• Share computing resources through job queues
• Configure distributed simulations with MPI

Build Gemini API integrations on Google Cloud’s Agent Platform using the Google Gen AI SDK. Get guidance on authentication, text and media processing, structured JSON responses, function calls, caching, batch processing, and real-time voice or video interactions.

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• Adding Gemini to enterprise applications
• Processing images, audio, and documents
• Generating JSON in a specified format

Deploy and manage applications on Cloud Run using source code or container images. Choose services for web requests, jobs for tasks that finish, or worker pools for continuous background processing, with guidance on execution settings and deployment failures.

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• Deploy a web application from source code or a container
• Create and run batch jobs with parallel tasks
• Deploy continuous queue-processing workers

Receive live Lark/Feishu updates for messages, approvals, tasks, meetings, and more. Stream them as NDJSON, one JSON record per line, with filtering and time or event limits for bots and automated event processing.

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• Receiving new messages for bot processing
• Monitoring approval and task updates
• Receiving meeting lifecycle and minutes events

Turn recorded audio and video into text or timestamped subtitles with Whisper. This skill covers transcription across 99 languages, language detection, translation into English, batch processing, and model selection for available computing resources.

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• Transcribe recorded meetings
• Turn podcasts into text
• Generate SRT or VTT subtitles for videos

Develop Salesforce workflows, interface components, and integrations using Apex and Lightning Web Components. The skill guides database queries, bulk processing, platform resource limits, testing, and deployment through Salesforce DX.

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• Automating CRM tasks with Apex triggers
• Building Lightning Web Components
• Improving bulk processing and database queries

Prepare an internal Privacy Impact Assessment, a review of how a feature uses personal data. Check whether assessment is needed, examine legal grounds and policy commitments, and document specific risks, required actions, and owners in the team’s format.

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• Reviewing personal data use in a new feature
• Checking whether a privacy assessment is needed
• Comparing processing plans with privacy policy commitments

Triage proposed personal data uses to determine whether they can proceed, need a privacy impact assessment, require a legally mandated assessment, or must stop. Results explain policy conflicts, applicable triggers, and conditions to address before proceeding.

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• Checking whether a new feature needs a privacy assessment
• Reviewing proposed data uses involving a vendor
• Finding conflicts with privacy policy commitments

Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.

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• Learn how Pipeline, PCollection, and PTransform fit together
• Understand grouping, aggregation, merging, and splitting
• Study time windows, output timing, and late data

Batch retouch portrait photos with Adobe tools, correcting alignment and tone and adding optional presets, background blur, or cropping. Review a sample before processing the full set, then receive previews and download links.

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• Batch retouching wedding portraits
• Polishing a folder of event photos
• Applying a consistent look to headshots

Make 3D canvas output sharper on Retina and other high-density displays. Coordinate rendering and post-processing resolution, preserve pointer accuracy, and verify fixed 200% rendering or automatic scaling with measured performance and explicit fallbacks.

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• Fix blurry 3D canvas output on Retina displays
• Implement a fixed 200% rendering option
• Keep post-processing and pointer coordinates aligned

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

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• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Build Python machine learning workflows for classification, numerical prediction, and grouping similar records. Use scikit-learn guidance and examples to prepare data, compare algorithms, tune model settings, and evaluate results with repeatable processing steps.

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• Building classification and numerical prediction models
• Grouping customers by similar characteristics
• Preparing data with missing values and categorical fields

Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.

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• Analyzing experimental CSV data and saving plots
• Solving matrix equations and differential equations
• Analyzing signal frequencies and applying filters

Work with microscopy images stored in OMERO through its Python API. Retrieve datasets, analyze pixel values and selected image regions, manage annotations and metadata, and save measurements or automate processing across multiple images.

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• Retrieving microscopy images from OMERO
• Measuring pixel intensity in selected regions
• Saving image tags and analysis results

Design and implement repeatable workflows that clean, encode, transform, and validate raw data for machine learning. Get a preprocessing plan or implementation outline, with clear steps and guidance on where further checks are needed.

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• Preparing raw data for machine learning
• Automating recurring data cleaning
• Planning data extraction, transformation, and loading

Prepare pathology image datasets by detecting tissue in whole slide images and extracting smaller image tiles. Use random, grid, or score-based selection, preview extraction locations, and apply preprocessing filters to review and prepare the resulting tiles.

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• Sampling tissue regions for an initial review
• Extracting tiles across tissue in a grid
• Selecting tiles by nuclei density scores

Develop workflows for satellite imagery and geographic data, using guidance and code examples to calculate vegetation indicators, classify land cover, analyze terrain, and find road routes. Covers GIS, software for working with location-based information, and spatial machine learning.

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• Tracking vegetation changes in satellite imagery
• Classifying land cover with machine learning
• Aggregating location data by region

XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.

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Implement event-driven patterns with Claude API: streaming SSE events, Message Batches callbacks, and async processing architectures. Use when building real-time Claude integrations or processing batch results. Trigger with phrases like "anthropic events", "claude streaming events", "anthropic async processing", "claude batch callbacks".

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