Results for “data processing”

Inspect, extract, OCR, create, merge, split, reorder, rotate, annotate, fill, redact, compress, secure, and verify PDF documents while preserving source files and visual fidelity. Use when working with one or more .pdf files; converting documents to or from PDF; extracting text, tables, images, metadata, forms, or page ranges; applying true redactions or signatures; diagnosing malformed, encrypted, scanned, or inaccessible PDFs; or validating that a PDF transformation preserved the intended content and layout.

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Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

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Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs.

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End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end.

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End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3 (or HOMER), and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.

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Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.

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Parse and extract structured content from PDFs, Word documents, and other file formats. Converts documents into clean, machine-readable text for analysis, summarization, data extraction, and content processing workflows.

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Schedule recurring tasks with cron on Linux/macOS. Use when a user asks to run scripts on a schedule, set up automated backups, schedule data processing, or configure periodic tasks on a server.

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Automate and script DaVinci Resolve workflows. Use when a user asks to script DaVinci Resolve via Python/Lua API, automate color grading, batch render projects, manage timelines programmatically, automate media import, build render queues, create Fusion compositions via script, automate Fairlight audio processing, manage project databases, build custom tool scripts, or integrate Resolve into production pipelines. Covers the Resolve Scripting API (Python/Lua), Fusion scripting, and workflow automation.

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Automate bookkeeping workflows by parsing bank statements (CSV/OFX/QIF), categorizing transactions using rules or AI, reconciling accounts, generating expense reports, and detecting duplicates. Use when: automating transaction categorization, building reconciliation pipelines, processing bank exports, or generating expense summaries without manual data entry.

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Expert guidance for Axiom, the serverless log management and analytics platform that stores and queries unlimited data at fixed cost. Helps developers ingest logs, traces, and events from any source, query them with APL (Axiom Processing Language), build dashboards, and set up alerts — all without managing infrastructure or worrying about per-GB pricing.

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Build algorithmic trading systems — backtesting, strategy development, live execution, and risk management. Use when tasks involve backtesting trading strategies, connecting to exchange APIs (Binance, Alpaca, Interactive Brokers), implementing technical indicators, portfolio optimization, order execution, risk management rules, market data processing, or building trading bots. Covers both crypto and traditional markets.

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Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.

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Comprehensive patterns for AI-powered document understanding including PDF parsing, OCR, invoice/receipt extraction, table extraction, multimodal RAG with vision models, and structured data output. Use when "document parsing, PDF extraction, OCR, invoice processing, receipt extraction, document understanding, LlamaParse, Unstructured, vision document, table extraction, structured output from PDF, " mentioned.

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Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

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Specialized visual and multimedia processing tools. Use this skill whenever a task involves complex visual content — UI mockups, dense screenshots, design images, charts, artwork — where precise details like spacing, hex colors, font sizes, and component hierarchy need to be extracted accurately. Also use for: reviewing or auditing existing UI against designs, comparing screenshots for visual regressions, transcribing audio/video, extracting data from PDFs with complex layouts, and generating images. Trigger whenever the user wants to implement from a design, review or compare UI screenshots, analyze visual details precisely, describe artwork or aesthetic content, or process any media file (audio, video, PDF).

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Fetches YouTube video metadata and transcripts (genesis web_fetch MCP tool first, yt-dlp via Bash as the fallback). Activate when the user shares a YouTube URL (youtube.com, youtu.be), asks to 'fetch this video', 'get the transcript', 'what does this video say', 'summarize this YouTube video', or references video content that needs to be retrieved. Also activate when processing multiple YouTube URLs in batch. Do NOT use for non-YouTube video platforms, local video files, or audio-only podcast URLs.

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Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

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Generate and parse office documents — PDF, DOCX, XLSX, PPTX, CSV. Use when creating reports, exporting tabular data, or processing uploaded office files. NOT for project documentation (use docs).

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Automatically detect and de-identify PII (Personal Identifiable Information) and PHI (Protected Health Information) from clinical/medical text to ensure HIPAA compliance. Trigger when processing medical records, patient data, clinical notes, insurance information, or any healthcare-related text containing potential patient identifiers.

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Security hygiene for GSD's self-modifying skill and agent system. Use this skill whenever: creating, editing, or deleting skill files (.claude/skills/, .claude/commands/), modifying agent definitions (.claude/agents/), working with YAML configuration or chipset files, handling JSONL observation data (.planning/patterns/), processing community-contributed skills or chipsets, any file path operations that could involve user input, or when installing/updating project-claude configuration. Also activates for discussions about skill-creator security, trust models, or content hygiene.

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Load, validate, and use DACP bundles as structured execution context. Detects .bundle/ companion directories, validates integrity (manifest schema, fidelity, provenance), loads typed data, and builds ExecutionContext for receiving agents. Scripts are NEVER auto-executed. Use when processing incoming DACP bundles, interpreting .bundle/ directories alongside .msg files, validating bundle integrity, or building execution context from bundle payloads.

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Advanced Zephyr RTOS kernel services. Covers inter-thread communication using Zbus (pub/sub), behavioral management with the State Machine Framework (SMF), background processing via work queues, and persistent configuration with the Settings subsystem. Trigger when building modular application architectures, complex state-driven logic, or requiring persistent data storage.

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Brain-Computer Interface with colored operad security boundaries. Maximally isolated EEG/neural data processing via VM→Container→Process enclosure with GF(3) conservation for Agent-O-Rama neural pathways.

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