「composite」の検索結果

Assemble a creator picture-in-picture product-listicle ad from a config — the creator stays FULL-FRAME the whole beat (voice plus lips generated together per beat, no separate VO, no cut to a full-frame product shot), and on each product beat three persistent overlays ride on top for the WHOLE beat — a title pill top-center, the DEMO in a rounded PiP window top-right (the brand's real UGC clip MUTED, or for a no-UGC brand the product's own autocropped UI still / screen-recording sized to fill the window), and a bottom product card (rounded thumbnail plus 'N · CATEGORY' small-caps plus product NAME in a serif face). Hook plus CTA beats are the creator full-frame with the title pill only. Assembly builds ONE full-1080x1920 transparent overlay PNG per beat, overlays it on the creator clip (cover-scaled to 1080x1920) for the whole beat keeping the native audio, concats all beats, then burns captions LAST as timed PIL PNG overlays (this ffmpeg has no libass) timed deterministically from the known per-beat script. This is the FREE deterministic assembly stage (overlay-PNG build plus cover-scale composite plus concat plus PIL-PNG caption burn); the creator anchor and the N native talking clips come from create-image-fal (Seedream v5 Pro) and create-video-fal (Seedance 2.0). Use for the creator-pip-listicle format.

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Assemble a cinematic live-action-style music-video ad from a config — an original sung anthem carries the whole narrative while N 35mm-film-look i2v clips are each cut to their lyric window and hard-concatenated on the beat as a 3-act arc, the anthem muxed at loudnorm I=-14, cinematic lower-third serif captions built from the song's OWN word timings (never Whisper) with the hook line landing on the chorus drop, and closed on a brand end card composited from the real asset — never AI-rendered text. This is the FREE deterministic assembly stage (cut-to-window + hard concat + anthem mux + captions + end card); the anthem, keyframes, and clips come from create-music-elevenlabs / create-image-gpt-image-fal / create-video-fal. Use for the cinematic-music-video format.

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Use when writing or editing Simscape Language (.ssc) source files — components, domains, or functions. Triggers include: model a physical component in .ssc; write physical equations for a Simscape block; add or modify physics or parameterization options in a Simscape component; build a composite component. Also use for validating .ssc syntax, fixing Simscape Language errors, or converting governing equations into working Simscape component code. Do NOT use for configuring or connecting existing Simscape blocks in a Simulink model — that is model building, not language authoring.

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Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

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Write and debug ast-grep rules for structural code search and rewrite. Use when tasks require AST-aware matching (not plain text), such as finding specific syntax shapes, relational patterns (inside/has), composite logic (all/any/not), or precise code refactors across a codebase.

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Generate the dispatcher PCF for a third-party `.ppmplugin` (wrap-runtime) control. Runs `pac pcf init` in the pcf/ subfolder, rewrites ControlManifest.Input.xml from ARCHITECTURE §6, and writes index.ts derived from ARCHITECTURE §4 (message contract), §8 (PCF surface), §9 (error UX) — no placeholders. The bridge dispatches the composite key `<name>/<receiver>` to `NativeModules.<nativeModule>.<method>` via the host-injected `window.PowerApps.NativeExtension.sendAsync` global (never `cordova.exec` — not in the PCF sandbox); also emits a `PowerAppsNativeExtension.d.ts` ambient declaration. Responses are peeled with `extractResponse`. Emits structured JSON debug/error logs. Validated by `npm run build`. **Local only** — does not deploy. Needs only `pac` CLI. Run after the native module exists. Uses npm (not pnpm).

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Statically audit a built `.ppmplugin` before wrap testing. Checks archive layout, manifest compatibility, bundle consistency, Android DEX integrity and SDK leakage, iOS framework structure, native source-to-receiver alignment, and the PCF composite-key/sendAsync transport contract. Reports CRITICAL, WARNING, and INFO findings with fixes routed to the owning stage; never modifies the archive. Requires `jar`; `dexdump` or `strings` improves DEX inspection. Run after /assemble-ppmplugin or standalone on an existing bundle.

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Deploy the dispatcher PCF for a third-party `.ppmplugin` control to a Power Platform environment via `pac pcf push`. The dispatcher PCF is the Studio-side control that dispatches the composite key `<name>/<receiver>` over the wrap shell's `SendMessagePlugin` bridge to the control's native module. Verifies deploy prereqs (Node.js 20+ with npm, .NET SDK, and active `pac auth`), then three confirmation gates — publisher prefix (2–8 chars; defaults to `pamext` or the last-used value from `.extension-state.md`), version bump (patch / minor / major / no-bump), target environment URL (from `pac org who`). Builds the PCF if needed, then pushes. Decoupled from /generate-pcf-companion so the engineer can scaffold and customize locally, then deploy when ready. Updates `.extension-state.md` with deployment history (timestamp, env URL, version, prefix used).

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Use when text must appear letter-perfect on generated media: taglines, CTAs, prices, legal supers, lower thirds, end cards, nameplates, or styled rich cards composited over Scenario images and video. Keywords: text overlay, text card, caption card, CTA, legal super, tagline, end card, lower third, price card, HUD text, transparent PNG.

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Use when generating or editing images with Reve models on Scenario via MCP: text-to-image from a prompt alone, instruction edits (swap poster text, recolor a product, change materials, relight or restyle a scene), merging references with consistent subjects, blending up to 6 images into one composite, or choosing between Reve v2.1 and Reve Remix. Keywords: Reve v2.1, Reve Remix, Reve AI, txt2img, img2img, frame tags, compositing, style blending, text swap.

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Use when the user has evaluation principles or a dataset but needs help choosing the right graders, designing evaluation metrics, creating LLM-as-judge prompts, combining multiple metrics into a composite score, or building an automated evaluation pipeline. Also use when the user mentions grader selection, metric design, judge prompt engineering, rubric design, evaluation pipeline code, or "how to evaluate [X] automatically." Outputs executable OpenJudge pipeline code.

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Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/ language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to review this contract / NDA / MSA", "best AI to redline", "route this contract review", or is about to review an agreement without a fixed model.

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Coordinates UDL and differentiation tools through a universal-first hierarchy: barrier removal before targeted differentiation before individualised accommodation. Use when planning accessible learning.

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Present pathway options and orchestrate place-based curriculum design from a local place, curriculum requirement, or community issue. Use when place should become a primary text for learning.

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Present project-design pathway options and orchestrate regenerative projects using backwards design, PBL, SEEDS, compassionate systems action, or civic/service pathways.

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Routes between five assessment pathways — formative, rubric/criteria, authentic/performance, peer/self, and diagnostic — with validity and equity checks. Use when a teacher needs help choosing how to assess.

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Orchestrate compassionate systems tools from issue or aspiration to wise action. Use when a class needs a complete inquiry workflow.

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Use when using the Beehave addon — pure-GDScript behavior trees with composites, decorators, leaves, a blackboard, and a visual runtime debugger

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This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image do

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Risk & Compliance (GRC) Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

日本語の概要は準備中です。原文の説明を表示しています。

Sales, GTM & Marketing Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

日本語の概要は準備中です。原文の説明を表示しています。

Data & Analytics Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

日本語の概要は準備中です。原文の説明を表示しています。

Strategy Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

日本語の概要は準備中です。原文の説明を表示しています。

Trading Super Intelligence Team. Named real-world personas across 8 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

日本語の概要は準備中です。原文の説明を表示しています。