Results for “batch”

Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.

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Run evidence-disciplined Xiaohongshu social listening for foreign pharma employee pain points. Use when the user asks to collect and analyze notes/comments about pharma companies, aliases, roles, compliance, medical affairs, or AI topics, then produce a cited and scored sampled-insight report. Includes independent-evidence counting, MediaCrawler batch collection, and CAPTCHA/461 fallback to Agent Reach/OpenCLI.

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Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.

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Use the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.

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Use for deterministic terminal or CI browser automation, repeatable regressions, traces, snapshots, screenshots, and unattended playwright-cli batches. Route interactive or signed-in Chrome work through browser-automation-router first.

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Run authorized Xiaohongshu browser workflows for login checks, creator preview fill, publish blockers, final posting, search, export, or approved engagement. Prefer native signed-in Chrome; keep Python/CDP for deterministic batch fallback.

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Universal SQL performance optimization assistant for comprehensive query tuning, indexing strategies, and database performance analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Provides execution plan analysis, pagination optimization, batch operations, and performance monitoring guidance. Use when a query is slow, an index strategy is unclear, or database performance needs analysis.

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Official GSAP skill for performance — prefer transforms, avoid layout thrashing, will-change, batching. Use when optimizing GSAP animations, reducing jank, or when the user asks about animation performance, FPS, or smooth 60fps.

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Create structured GitHub issues from text, screenshots, or lists, with acceptance criteria and preserved reporter context. Use for filing bugs/features, batch creation, or template cleanup. Don't use for resolving, triaging, or deep issue analysis.

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Process the batch of incoming Bridge Classroom feedback. Pull open `classroom-feedback` issues from the PBS repo, cluster them by defect class, verify each against the actual board, and fix at the right altitude — patch a single board, or (when a class recurs) build a gate, sweep the whole corpus, and regenerate/drop collectively. Engine bugs route to the Classroom repo; bridge-judgment calls go to Rick. Nothing leaves the machine without David's OK. Invoke as `/issue` (optionally with an issue number or scenario to restrict the run).

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Unified document optimization skill that chains docling-converter, imagemagick-expert, and markdown-to-pdf. Automatically detects input format, applies appropriate conversion pipeline, optimizes embedded images, and produces web/print-ready output with configurable quality presets. Use when converting documents through multi-step pipelines, optimizing PDF images, creating print-ready documents from various sources, or batch processing documents with image optimization.

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Generate QR code images from text, URLs, or contact information using Python's qrcode library. Supports customization (size, margin, color, error correction) and batch generation.

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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest/ai-translation-text, @azure-rest/ai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.

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Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization. Triggers: "transcription", "speech to text", "Azure AI Transcription", "TranscriptionClient".

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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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Build automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, schedul...

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Solana development patterns used in the Pump SDK — Anchor IDL-based program interaction, SPL Token and Token-2022 management, transaction construction with instruction composition, RPC batching, and cross-program coordination.

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Production-quality Bash scripts for Solana vanity generation, keypair verification, batch operations, dependency auditing, and test orchestration — with security-hardened patterns including file permissions, input validation, and cleanup traps.

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Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.

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Convert PDF documents to clean structured Markdown for LLM context. Supports two modes: fast (PyMuPDF) and accurate (IBM Docling TableFormer AI). Features aggressive persistent caching, image extraction with metadata, table detection, and batch processing. Use when asked to convert PDFs, extract PDF content, parse documents, or prepare PDF data for AI/LLM consumption.

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Builds DRAFT user personas from the research notes, interview summaries, survey free text and support themes the user provides: for each persona the goals, pains, jobs to be done, context of use, quotes exactly as supplied with their participant codes, and the evidence count behind every attribute, with names, demographics and any detail no participant stated left UNKNOWN rather than invented. Use when the user asks to "build personas from these interviews", "who are our users based on this research", "draft user personas", "turn these research notes into personas" or "update our personas with the new interviews". Do not use for theming a batch of customer feedback into counts, use customer-feedback-theme-synthesis instead; for exit interviews, use exit-interview-synthesis; for the requirements those personas imply, use product-requirements-draft. Drafts for human review; never approves, authorises or signs off.

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Turns one long support ticket thread (helpdesk history, email chain, chat transcript, call notes) into a one-page DRAFT escalation summary: the customer's ask in their own words, account context as stated, a dated history, what was tried and the stated result of each attempt, the current state, and what is needed from whom by when, every line referenced to a message and every missing fact marked UNKNOWN. Use when the user asks to "escalate this ticket", "summarise this thread for tier two", "write the hand-over for this case", "what has been tried so far", "brief engineering on this customer issue" or "prepare the escalation note". Do not use for a batch of tickets, use ticket-triage-pack instead; for the review after an outage, use incident-postmortem-drafter. Drafts for human review; never approves, authorises or signs off.

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