'file processing' 검색 결과

Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Upload files or directories to TOS-compatible object storage for Volcano Engine or BytePlus and download files from URLs. Use this skill when (1) Upload Agent-generated files or directories for sharing, (2) Download files from URLs before Agent processing.

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Video/audio/image processing with FFmpeg and ImageMagick. Tools: FFmpeg (video/audio), ImageMagick (images). Capabilities: format conversion, encoding (H.264/H.265/VP9/AV1), streaming (HLS/DASH), filters, effects, thumbnails, watermarks, batch processing, hardware acceleration (NVENC/QSV). Actions: convert, encode, resize, crop, compress, extract, merge, stream, transcode media. Keywords: FFmpeg, ImageMagick, video encoding, audio extraction, image resize, thumbnail, watermark, HLS, DASH, H.264, H.265, VP9, AV1, codec, bitrate, framerate, resolution, aspect ratio, filter, overlay, concat, trim, fade, batch processing. Use when: converting video/audio formats, encoding with specific codecs, generating thumbnails, creating streaming manifests, extracting audio from video, batch processing images, adding watermarks, optimizing file sizes.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Use this skill when a company needs its record of processing activities for the first time, or has one that is really a list of tools, and asks "build our RoPA", "records of processing", "data map for GDPR", "Article 30 record", "what personal data do we process and why", "data inventory for privacy" or "our DPO needs a processing register". Drafts the record by processing activity from systems, vendors and team interviews, with purposes, data and subject categories, proposed legal bases, recipients, transfers and concrete retention, plus a separate processor record when the company processes data for customers, and a notes file of open questions per owner.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration), ImageMagick (image manipulation, format conversion, batch processing, effects, composition), and RMBG (AI-powered background removal). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, removing backgrounds from images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Validate particle data and run RELION refinement for single-particle cryo-electron microscopy. Check independently refined half maps, calculate diagnostic map correlations, and perform soft-mask postprocessing while preserving execution logs and restart information.

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• Check particle metadata and image-stack consistency
• Run refinement on a selected particle population
• Inspect agreement between independent half maps

Analyze executable files and libraries with IDA Pro. Inspect pseudocode, strings, function calls, and data flow, then add meaningful names and comments and produce a Markdown report documenting findings and analysis steps.

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• Investigating executable files and libraries
• Tracing processing from strings and function calls
• Organizing analysis with comments and meaningful names

Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

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• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Launch an SGLang server, run a quick accuracy check, and capture an execution trace for performance inspection. Return the trace location and generated files, ready to open in Perfetto or Chrome tracing.

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• Capture an execution trace of SGLang inference
• Check basic accuracy before profiling
• Record input processing and token generation separately

Build an automated workflow that collects suspicious files, checks known malware records, and submits unknown samples to isolated analysis environments. Generate verdicts and indicators of compromise, then send results to security monitoring systems.

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• Automating analysis of quarantined email attachments
• Batch-processing files quarantined by endpoint security
• Extracting threat indicators during incident investigations

Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.

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• Extracting errors from large logs
• Summarizing test failures and build warnings
• Inspecting JSON and CSV data

Continue Python tasks after repeated sandbox failures by saving code as a script and running it through the shell, a command interface. The workflow covers execution checks, result retrieval, and optional cleanup.

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• Continuing data processing after repeated sandbox errors
• Saving execution results to files for later retrieval
• Checking script errors and exit codes

When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.

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• Retry Python work through the shell after repeated sandbox failures
• Run longer tasks with an adjusted timeout
• Execute Python work that needs multiple files or system utilities

Diagnose slow MSBuild startup by measuring project evaluation, the work done before compilation. Inspect logs for costly file searches, imported configuration, and repeated evaluations, then identify targeted changes supported by timing evidence.

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• Investigating delays before compilation starts
• Checking file searches across large directories
• Analyzing deep import chains

Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.

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MS-DIAL-based metabolomics preprocessing as alternative to XCMS. Covers peak detection, alignment, annotation, and export for downstream analysis. Use when processing MS-DIAL output files for R/Python analysis or when preferring GUI-based preprocessing.

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Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

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Converts and transcodes audio file formats and encoding parameters using Volcengine LAS. Audio format conversion between wav, mp3, flac, m4a, ogg, aac and other audio formats. Adjusts sample rate (resample, downsample, upsample), bit rate (bitrate), channels (mono, stereo, channel mixing), audio compression, and audio quality settings via ffmpeg parameters. Supports TOS cloud storage paths and local file upload. Use this skill when the user wants to convert audio format (wav/mp3/flac/m4a/ogg/aac), transcode or re-encode audio files, adjust audio sample rate, bitrate, channels, compress audio files, resample or downsample audio, prepare audio for downstream tasks, or do any audio preprocessing.

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Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.

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CRITICAL: Never modify files in content-addressable storage systems where the filename IS the content hash (SHA256, IPFS CID, etc.). Use when: (1) Working with Blossom protocol, IPFS, or any CAS system, (2) Considering "optimizing" stored files (faststart, compression), (3) Implementing transcoding or processing pipelines for hash-identified content, (4) Building on top of ProofMode or any cryptographic verification system. Modifying files in place breaks hash verification and content integrity.

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Track exact provenance for every operation on ENCODE data — tool versions, reference files, scripts, parameters, and timestamps — to enable publication-ready methods writing. Use when the user processes ENCODE files, runs any bioinformatics tool, creates filtered/merged datasets, runs pipelines, performs liftover, uses R/Python/Bash for analysis, or needs to document their analysis chain for reproducibility and publication. Also use when the user says "write me methods" to auto-generate methods sections from the provenance log. This skill implements comprehensive provenance documentation: every tool, every version, every reference file, every parameter, every accession — no shortcuts. Use this skill for ANY processing step, ANY file transformation, ANY analysis operation on ENCODE data.

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Process many sequence files in batch (count, merge, split, convert, summarize) with memory-safe streaming and on-disk indexing using Biopython, pysam, or pyfastx. Use when iterating over a directory of FASTA/FASTQ files, merging or splitting datasets, building random access across many or huge files, or automating per-file operations without exhausting RAM.

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