Results for “emg”

Emu: Generative Pretraining in Multimodality

Migrates legacy AEM (6.x, AMS, on-prem) to AEM as a Cloud Service using BPA CSV or CAM/MCP target discovery, with one-pattern-per-session workflow for scheduler, replication, event listener, HTL lint, dialog, and custom widget migration.

Rewrites AEM Edge Delivery Services page content for AI search discoverability (Generative Engine Optimization) by analyzing structure, clarity, and factual density.

Eng manager-mode plan review. (gstack)

Emu2: Generative Multimodal Models are In-Context Learners

Use when converting BLASTN mRNA alignment XML into segmented interval reports and strand-overlap summaries in EDirect pipelines.

Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations. Use when asked to "review the architecture", "engineering review", or "lock in the plan". Proactively suggest when the user has a plan or design doc and is about to start coding — to catch architecture issues before implementation. (gstack) Voice triggers (speech-to-text aliases): "tech review", "technical review", "plan engineering review".

Provides design engineering guidance focused on animation principles, UI review, and interface craftsmanship.

Creates complete AEM components with dialog, HTL template, Sling Model, unit tests, and clientlibs. Supports extending Core Components and project components, and can translate Figma designs into pixel-perfect HTL, CSS, and JS.

Use this skill when the user asks about Environmental Health & Safety (EHS) for trucking — fuel spill response, hazmat release reporting, EPA + state environmental reporting, emergency contact information (CHEMTREC), environmental impairment liability insurance, and how to handle a CMV-related environmental incident. Cite 40 CFR + 49 CFR 174.6.

Execute the next incomplete step (or full phase with --phase), ship the result, and prepare the next step

Enterprise asset management tracker

Maximally Endowed Graph Architecture — λ-calculus over bounded n-SuperHyperGraphs with grounded uncertainty, conditional self-duality, and autopoietic refinement. Use when (1) simple graphs insufficient (η<2), (2) multi-scale reasoning required, (3) uncertainty is structured not stochastic, (4) knowledge must self-refactor. Pareto-governed: complexity added only when simpler structures fail validation.

Conduct authorized red team initial access using EvilGinx3 adversary-in-the-middle phishing to capture session tokens and bypass multi-factor authentication.

ElevenLabs text-to-speech with mac-style say UX.

ElevenLabs text-to-speech with mac-style say UX.

Use when building a cursor trail whose spacing stays constant at any hand speed by emitting motes per distance travelled (not on a timer)—sub-segment placement, ring-buffer ordering, idle breath, 3D screen anchoring, coasting, touch and reduced-motion fallbacks—for wisps, sparks, embers, comet tails, plankton, or dust.

Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.