Plan, write, time, style, and validate motion-graphic beats for long-form talking-head HyperFrames videos. Use when Codex is asked to add or improve visual beats, glassmorphism lower thirds, crumpled-paper full-screen takeovers, transcript-synced callouts, pacing coverage, YouTube retention graphics, or render-ready overlays in an existing HyperFrames video project.
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。
nateherkai/hyperframes-student-kit☆ 1,3672026年9月28日 更新
Composite a generated creator track into a product layer, per beat — in the creator's zone on split beats (either side of a seam), full frame on creator beats, hidden on product beats — with the creator's voice as the audio (loudness -14 LUFS) and an optional ducked music bed, or a music-only or silent master for voiceless formats. Free, local ffmpeg. Pairs with footage-cutlist (the layer) and create-creator-takes-h3 (the creator). Use for split-screen (with an optional zoom and brand-colour seam bar), screen-insert and any creator-over-product format.
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。
gooseworks-ai/goose-skills☆ 1,2452026年10月13日 更新
Selects the active profile from triggering context. Evaluates a rule set against inputs (current time, calendar state, geofence, manual command, custom signals) and returns the profile name that consuming Skills (handoff-trigger-check, critic-gate, future Skills) should apply. Configuration-driven; rule set lives in a router config file conforming to the included schema. Deterministic precedence — manual override beats calendar beats time-window beats default. Required default profile guarantees a routing decision always exists. Use when an orchestrator needs to pick a profile automatically rather than having the caller specify one, when wiring calendar/time/Telegram triggers to gate behavior, or when a Skill's behavior should change with availability mode. Trigger on: "what profile should I use," "route to a profile," "which mode am I in," "auto-select profile," "active mode," "current profile." Do NOT use to evaluate work readiness or review proposed changes. Always returns a profile name.
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。
drayline/rootnode-skills☆ 402026年9月14日 更新
集めた素材から記事の展開を複数提案し、選んだ内容を一段ずつ書き進めます。読者が知っている概念を確認し、必要な説明を先に置きながら文章を組み立てるスキルです。
- 収集済みのメモを記事にしたいとき
- 記事の書き出しを比較したいとき
- 初心者向けに説明順を整えたいとき
mattpocock/skills☆ 28.9万2026年10月12日 更新
Inspect and manage guarded Codex App-native or launchd heartbeats for Codex main control threads. Use when the user wants Codex to periodically continue a specific thread only when it is idle, avoid interrupting active work, keep App-visible interactions, inspect thread status, or enable/disable project autopilot heartbeats.
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。
huangruiteng/CS-Notes☆ 4,0032026年10月13日 更新
Scene and sequence craft (场景/段落/细节/道具/悬念/情趣) — merged from Robert McKee's scene design and five-step scene analysis (value turn, beats, text/subtext, turning points, pacing, image systems), McKee's Dialogue scene types (balanced, comic, asymmetric, indirect, reflexive, minimal), Syd Field's sequence and scene elements, Neill Hicks (less is more, enter late leave early, energy), Richard Walter (action over talk, small gestures, avoid phones/cars/restaurants, list twelve alternative locations), Wendy Henson (action paragraphs, transitions, story-doing), Julian Hoxter (scene beats, objectives and tactics), Mei Feng's value test, and Lu Jun's chapters 意趣要足 (suspense, delay, wit), 细节要妙, 道具要精, 场景要当. Use when writing or fixing a single scene, choosing a location, building a sequence, planting a prop or detail, designing suspense and delay, cutting a scene that "nothing happens" in, or turning talk into action.
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。
jtydhr88/screenwriting-skills☆ 1,6482026年10月3日 更新
Make compound growth actually click — see how small, consistent amounts become large over time, and why starting now beats starting bigger later. Use when asked explain compound interest, how does compounding work, is it worth investing small amounts, or why should I start now. Produces an intuitive explanation of compounding with concrete illustrative examples for your situation, the outsized effect of time (why an early start beats a later larger one), how fees and inflation eat into it, and the honest caveats — turning an abstract concept into the motivation to start now. Educational, not financial advice.
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。
mohitagw15856/pm-claude-skills☆ 1,4412026年10月13日 更新
Final QC gate for a rendered 9:16 video ad, run before it is published. One script checks what a machine can decide — exact 1080x1920 size, a hook that moves and speaks in the first second, no frozen stretches, no dead air, no black frames, the brand's real logo on the end card (never a favicon, never a redrawn or wrong logo), end-card colours near the brand palette — and builds one contact sheet (frames with the TikTok/Reels UI safe zones shaded, beside the logo, product images and a font specimen) for the checks that need eyes — font, product likeness, product consistency across scenes, safe zones. A declared silent-text profile covers silent, text-led formats such as kinetic text (no audio track needed, short blank beats between text beats allowed within fixed bounds). Exit 0 PASS / 2 FAIL / 3 ERROR. Use on every finished video master before pinning it.
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。
gooseworks-ai/goose-skills☆ 1,2452026年10月13日 更新
Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a drawing filling the right, and a payoff that letters the closing line and rings the claim. The board is a PHOTOGRAPH and the ink is multiplied onto it, so the surface sheen and window light come through the strokes; the drawings are generated as line art and traced to ordered strokes, then revealed in drawing order on the word they belong to. The layout is SOLVED from the beats, never hand-placed. FREE assembly (Pillow plus ffmpeg) apart from one board photo and one drawing per subject, both one-time per brand. Use for the whiteboard format.
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。
gooseworks-ai/goose-skills☆ 1,2452026年10月13日 更新
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.
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。
gooseworks-ai/goose-skills☆ 1,2452026年10月13日 更新
Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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。
GPTomics/bioSkills☆ 1,2192026年8月15日 更新
Get a PDF into the model without blowing the context window or losing structure. Native PDF beats OCR-then-text for most cases; extract-then-summarize beats native for very long docs.
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。
Archive228/loopkit☆ 7542026年7月15日 更新
Writes the character-performance layer of a video prompt as behavior under pressure, not displayed emotion — objective, obstacle, tactics, beats, subtext, listening, body/status/proxemics, and mandatory eye life. Produces a reusable 150–220-word acting master profile per character plus a per-scene rewrite of it, and a locked voice prompt. Use whenever a prompt needs acting, performance, or emotion direction; whenever characters read wooden, dead-eyed, or 'AI'; whenever a character must stay themselves across many shots; or when the user asks for character behavior, mannerisms, tics, a gait, or how someone reacts. Pairs with higgsfield-facs (muscle-level AU codes) and higgsfield-seedance (the prompt the paragraph goes into).
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。
OSideMedia/higgsfield-ai-prompt-skill☆ 7332026年9月27日 更新
Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
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。
stevesolun/ctx☆ 5882026年10月4日 更新
Writing, exploit; assemble raw material into a journey of beats, grounding each term before a beat leans on it.
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。
Asymmetric-al/core☆ 3812026年10月12日 更新
Writing, exploit; assemble raw material into a journey of beats, grounding each term before a beat leans on it.
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。
tt-a1i/matt-skills-with-to-goal☆ 1842026年10月10日 更新
Make a short product film (website hero loop, landing section, launch film, feature walkthrough, store-listing promo, social cut, illustrated story film) in which the product appears as its own real UI, drawn frame by frame from an HTML timeline, rendered with Playwright and cut with ffmpeg. Four routes: licensed footage cut against cinematic product beats, product beats only, a pointer-driven walkthrough with captions or text cards (plain or kinetic), or a cut-paper collage in which an illustrated cast tells a story over real UI captured from the code with fictional data. Use when asked for a product film, hero video, promo or demo video, launch film, explainer, walkthrough video, paper or collage film, or to recut one. Not for plain screen recordings or voice-over walkthroughs; laying a supplied music track under a finished cut is in scope.
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。
display-dev/product-film☆ 282026年10月5日 更新
Writing, exploit; assemble raw material into a journey of beats, grounding each term before a beat leans on it.
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。
VoDaiLocz/kilo-kit-mcp☆ 272026年9月13日 更新
Turn any still image into a professional motion graphic through the Novoads API on Seedance 2.5. Covers how to read a reference image, motion vocabulary for any image type — UI, marketing hero, flat-lay, key art, collage, product, character — the prompt clauses that control camera, timing, frozen regions and text fidelity, and the upload mechanics. Use whenever the user supplies an image and wants it animated, moving, or turned into video — including "animate this", "turn this into a motion graphic", "make this move", "この画像動かして", "モーショングラフィックにして", "I want the cards to pop in", "I want the bars to slide up", or when they describe motion beats for a static image. Also use when asked to write a Seedance prompt for an existing image. Do NOT use for text-to-video with no source image, restyling existing footage (this skill takes no video input), cloning a reference video (use clone-video-ad), or a product photo that should seed a new scene rather than move as-is (use novoads-api).
novoads/agent-skills☆ 232026年10月8日 更新
Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
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。
kevinnft/ai-agent-skills☆ 132026年8月1日 更新
Riff visuals, then build the graphics beat table. Use at the beats stage after gate 2, or when the user says "plan the graphics", "beat table", or "what visuals go here".
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。
bmad-code-org/bmad-manticore☆ 72026年9月26日 更新
Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
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。
0xAidan/polymarket-bot-test☆ 42026年9月4日 更新
Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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。
peacezha/TRACE☆ 32026年10月11日 更新