「model」の検索結果

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Turn an article, doc or release note into a short explainer clip — one person to camera, or a voiceover over illustrative footage. The user supplies the source text and the model generates the speech; audio cannot be uploaded, measured. A 30-second clip holds about eighty spoken words in eight sentences — measured, and well under a tenth of a 1,200-word post — so this skill does not summarise an article, it picks the single idea worth saying and helps choose which one. Use when a user asks to turn writing into a short spoken video, e.g. "make a 30-second explainer from this blog post", "explain this feature in a short video", "turn our changelog into a clip", "a quick video explaining what this paper found". Do not use for a scene between people (see seedance-short-drama), a brand or product ad (see seedance-ad-creative), a handheld creator clip (see ugc-ads), or when the user already has both a portrait and the finished words (see talking-head). Budget sentences as well as words — each sentence boundary costs about 0.7 seconds of silence, so a script with more sentences runs longer at the same word count.

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Requires OFOX_API_KEY — create one at https://app.ofox.ai, plus two images you already have, a start frame and an end frame. Animates the motion between them as one video — both frames go into a single Ofox video job, the clip opens on A, closes on B, and the model fills the middle. Use when a user has two stills and wants the in-between animated, e.g. "here is the before and the after, animate the transition", "make a video that starts on this image and ends on that one", "tween these two frames", or "move the object from where it sits in the first picture to where it sits in the second". Do not use when only one image exists (animating a single frame is seedance-ad-creative or seedance-product-video), or when the pair is two states of a user interface (see product-demo).

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Turn a portrait plus a short script into a clip of one person speaking those words to camera. You supply the text and the model generates the voice — audio cannot be uploaded, measured. Defaults to alibaba/wan-3.0-prime rather than this repo's usual seedance-2.5, because seedance-2.5 refuses a real person's photo at submission. Use when a user has a face and some words and wants the face to say them, e.g. "make this headshot read my intro", "a spokesperson clip from this portrait", "have her say this line to camera", "use this avatar to read the announcement". Do not use for a scene between two or more people (see seedance-short-drama), a polished brand or product ad (see seedance-ad-creative), a handheld creator clip (see ugc-ads), or when the words still have to be pulled out of an article and no particular face is required (see explainer).

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai, plus a publicly reachable reference video the user is authorized to use. Use when a user has a rough 3D white-model, clay, wireframe, blockout, previs, or animatic clip and wants a finished visual treatment that preserves its shot order, cut timing, camera framing, spatial layout, and motion directions. Distinguishes that structure-preserving job from a continuation beginning at the reference's final state. Do not use for two still endpoints (keyframe-animation), for appending and locally joining a new ending (video-extend-edit), or when still-image references must be combined with the video in one request — first/last-frame anchors conflict with video references in the shipped core, while mixed image/video references are untested.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Shared execution layer for the Ofox video generation API (api.ofox.ai) — creates a video job, polls it to completion, downloads the finished mp4 from a persistent CDN URL, and reports the real cost. This is a library skill, not a standalone user-facing one — it is invoked by scenario skills such as seedance-short-drama, seedance-ad-creative, and seedance-product-video, which build model/prompt/resolution choices for a specific use case and then call into this skill's script rather than re-implementing the API calls. Load this skill directly only when a user explicitly names the Ofox video API, asks to call it with specific low-level parameters, or asks to debug/resume a stuck or failed Ofox video job by job id — for a plain scenario request ("make me a short drama scene", "generate a cinematic ad clip"), use the relevant scenario skill instead, which itself depends on this one.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Shared execution layer for the Ofox image API (api.ofox.ai) — validates parameters client-side, sends one synchronous request, base64-decodes the result, saves it to a file, and reports the real usage token counts and the computed dollar cost. Does both text-to-image (generate) and editing an existing image you supply as a local file (edit — change the background, recolour an element, alter a product photo, while leaving the rest of the picture intact). This is a library skill, not a standalone user-facing one — it is meant to be invoked by scenario skills that build model/prompt/size choices for a specific use case and then call into this skill's script rather than re-implementing the API calls — image-edit owns "change this image so that...", product-image owns a set of product images to choose between, and seedance-anime-drama owns character images for a video pipeline. Load this skill directly only when a user explicitly names the Ofox image API, asks to call it with specific low-level parameters, asks to debug a failed Ofox image request, or wants a plain "generate an image of..." from text — which is the one common case no scenario skill covers.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai, plus two real screenshots of one interface in two states. Animates the transition between them as one video — both go into a single Ofox video job, and the model cross-fades the values that changed while the rest of the layout holds. Use when a user wants a short software demo animation out of captures they already have, e.g. "turn these two screenshots into a demo clip", "show the dashboard going from the free plan to the paid one", "animate this settings change for the docs", or "make a clip of the counter going from 3 to 25". Do not use for a pair that is not a user interface (see keyframe-animation), for a screen recording (record it instead), or when only one screenshot exists.

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。

Multi-touch attribution analysis across marketing channels with multiple models

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。

S3, multipart uploads, CDN integration, and presigned URLs for file storage. Activate on: file upload, S3, presigned URL, multipart upload, CDN, blob storage, image upload, media storage. NOT for: data warehouse storage (use lakehouse-architect), database design (use dimensional-modeler).

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。

Refute-before-prove research discipline: automated counterexample sweeps before any formalization, meter-integrity audits for information-bound experiments, mutation-tested model checkers, pre-registered commit-or-cut gates for speculative directions, wrong-turn reporting, and numeric-claim provenance. Use when validating any new theorem, bound, equilibrium, or security claim; when building or trusting a checker, simulator, or experiment; or when deciding whether a speculative research direction lives or dies. NOT for exploratory brainstorming, product prioritization, writing style, or literature review.

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Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).

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。

Decision-theoretic framework for agents embedded within the environments they model and act upon

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。

Rigorous benchmarking of multi-agent coordination systems: experiment design, statistical analysis, human evaluation protocols, and reproducible reporting. NOT FOR ML model evaluation (use llm-evaluation-harness), A/B testing for web products, survey design, or general data science.

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。

Secure the path from an inbound event (webhook, email, SMS, GitHub comment, file, cron) to an agent spawn in the Port Daddy fleet. Use when wiring trigger sources to agents, reviewing the io-wiring registry, adding a trust/permission gate, hardening output sinks (SSRF/path traversal), scoping tools by provenance, or proving the spawn path is safe against prompt-injection → tool-abuse. Encodes ADR-0093 and the red-team threat model. NOT for generic Coast Guard sandboxing (ADR-0050), the macaroon push-grant gate alone (ADR-0053), or non-fleet code.

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。

Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.

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。

Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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。

Safe database migration manager for zero-downtime DDL changes and rollback plans. Activate on: database migration, schema change, DDL, rollback plan, zero-downtime migration, Prisma migrate, Drizzle kit, Flyway, column rename, table alter. NOT for: query optimization (use database-optimizer), ORM modeling (use data-pipeline-engineer), backup/restore (use devops-automator).

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Audit or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).

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。

Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.

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。

PgBouncer, connection optimization, and pooling strategies for database performance. Activate on: connection pool, PgBouncer, database connections, pool size, connection limit, Prisma pool, Drizzle pool. NOT for: query optimization (use data-warehouse-optimizer), database schema design (use dimensional-modeler).

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。

Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).

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。

Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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。

Threat-models protocols where authority, scarce resources, bonds, bounties, escrow, reputation, ordering, adjudication, or settlement create strategic incentives. Use to test attack utility, conservation, collusion, Sybil resistance, oracle capture, griefing, custody, liquidity, and residual risk. NOT for choosing payment or bond amounts without evidence, smart-contract code audit, legal or employment classification, DeFi trading, admitting workers, or settling claims.

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。

Render mathematically-faithful Turkish marbling (Ebru) and paper-marbling with closed-form coordinate-deformation maps — a drop is a radial outward push, a comb/tine is a decaying shear, all composed as homeomorphisms over colored region boundaries. No fluid solver, runs 60fps on a 2D canvas. Use when building a marbling/Ebru toy or generative-art renderer, implementing drop/comb/wave/swirl deformations, scripting traditional patterns (Battal, Gel-Git, Şal, Bülbül Yuvası), keeping combed edges crisp via adaptive resampling, modeling ox-gall shouldering for crisp ring-in-ring drops, or exporting high-DPI marbled prints. NOT for real fluid / Navier-Stokes simulation, metaball / alpha-blended "smoke" blob effects, raster image-processing filters, or non-marbling generative art.

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。