「apt」の検索結果

Provider-independent dialogue editing and ADR direction for AI agents producing generated videos, films, ads, avatar clips, explainers, localization/dubbing, podcasts, recuts, and social content. Use for dialogue prep, repair, room tone, take comping, sync, ADR cueing, dubbing direction, pronunciation, de-essing, plosives, mouth clicks, voice continuity, synthetic voice consent boundaries, captions/transcripts handoff, mix handoff, loudness, intelligibility, accessibility, and QA.

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。

Provider-independent workflow for producing terminal, IDE, documentation, browser, desktop, and application demonstration videos. Use when choosing authentic capture, browser automation, synthetic UI, or hybrid treatment; scripting actions and narration; resetting demo state; protecting secrets and personal data; directing cursor/callouts; creating crop variants; and reviewing workflow truth, readability, accessibility, and provenance.

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。

Provider-independent sound design and Foley direction for generated media. Use when an agent must plan, prompt, source, generate, edit, mix, hand off, repair, or QA sound effects, Foley, ambience, impacts, transitions, UI/product audio, emotional sound design, or accessibility-safe captions for videos, ads, trailers, animation, avatar clips, explainers, games/trailers, product films, and social edits.

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。

Use this skill to turn generated, captured, scanned, or modeled 3D output into production-ready standalone assets for DCC, real-time engine, web, or interchange delivery. It covers requirements, topology repair, scale and axes, UVs, PBR baking, LODs, static and skeletal readiness, rig handoff, optimization, glTF/USD/FBX delivery, validation, QA, and rights/provenance. Do not use it for provider-specific generation prompts, full scene/world building, mocap operation, or exact CAD/manufacturing work.

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。

Provider-independent explainer video production for AI agents creating educational, product, concept, nonprofit/public-service, onboarding, training, animated, mixed-media, and short social explainers. Use when an agent must turn a topic, brief, research corpus, product, process, policy, dataset, or complex idea into a factual, accessible, narrated, captioned, storyboarded, generated-media-ready explainer video with claim review, localization, pacing, variants, and QA.

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。

Provider-independent motion graphics direction for AI agents producing title sequences, lower thirds, explainer graphics, product UI overlays, social ads, kinetic typography, diagrams, data callouts, brand films, and video post. Use when planning, prompting, storyboarding, specifying, implementing, reviewing, or QAing motion graphics across live footage, generated video, UI capture, data visualization, brand systems, typography, accessibility, claim integrity, runtime handoff, and iteration.

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。

Use this skill to plan, direct, troubleshoot, and hand off provider-independent live or hybrid livestream event productions, including run of show, crew, capture, audio, graphics, switching, guests, encoding, ingest, transport choices, latency, redundancy, accessibility, moderation, monitoring, incidents, recording, and delivery.

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。

Diagnose, design, script, produce, adapt, and quality-control product video advertisements from evidence-backed briefs. Use for physical-product, software, app, service, ecommerce, direct-response, brand-response, demonstration, testimonial, launch, and variant ad work where product truth, legibility, claims, platform delivery, and measurable iteration matter.

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。

Provider-independent workflow for AI agents producing documentary-style montages, archive-driven timelines, interview-supported sequences, nonprofit or advocacy shorts, historical explainers, and hybrid generated/archive edits. Use for editorial thesis development, research/source logs, fact-checking, archive rights, synthetic reenactment disclosure, chronology, interview selects, lower thirds, generated b-roll direction, music restraint, captions, sensitive subjects, review escalation, delivery variants, and documentary QA.

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。

Production guidance for reusable layered 2D character rigs in browser-rendered and video media. Use for SVG hierarchies, pivots and constraints, FK/IK planning, pose and facial libraries, acting, cycles, deterministic handoff, crop variants, and rig QA; not for character concept continuity, 3D armatures, or motion-capture solving.

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。

Produce audio-first podcast episodes with generative tools — design the show and episode format (interview, narrative, news brief, two-host conversational), write scripts for the ear, decide between fully-synthetic and hybrid (recorded human + synthetic) production and meet the disclosure duty each triggers, cast and direct multi-voice TTS for consistency and chemistry, build episode structure (cold open, intro/outro, segments, ad slots), assemble and edit, clear music and SFX rights, hit podcast loudness and delivery standards, ship metadata/chapters/transcripts through RSS, and pass platform AI-content policy and QA before publishing. Use this skill whenever the deliverable is a podcast episode or a podcast show bible, or when an agent must make production, rights, disclosure, or delivery decisions for spoken-word audio. Not for music tracks, single-voice notification prompts, or video where picture leads (route audio-for-video to a video skill).

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。

Scrape web pages, crawl sites, extract structured data, and capture screenshots through SandBase. Use when asked to extract content from URLs, crawl websites, get structured data from pages, take screenshots, or convert web pages to markdown.

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。

Search videos, analyze channels, extract captions, read comments, and discover trends on YouTube through SandBase. Use when asked for YouTube research, video discovery, channel analysis, content strategy research, comment sentiment, or video transcript extraction.

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。

Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.

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。

Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.

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。

Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.

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。

Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.

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。

Analyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis.

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。

Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data.

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。

Publication-quality PDF report generation using Typst templates. Produces professional scientific reports with colored section bands, styled tables, figure captions, callout boxes, and page headers/footers.

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。

Use when implementing or modifying behavior that must stay consistent across TUI and Web (mode/input/tool/replay/order). Require canonical semantics first, then TUI/Web adapters, then renderer-specific UI.

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。

Use when we want to turn a just-finished Formax workflow (e.g. commands, overlays, tools, hooks, permissions, UI parity) into a reusable Codex Skill under .codex/skills, including scaffolding, guardrails, and the minimum test checklist.

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。

Add fast, local, typed decisions to any project with Laya, an open-source non-generative decision model (pip install laya). It classifies, routes, scores and answers yes/no questions about a piece of text, returning calibrated probabilities in roughly 20-35 ms on a laptop GPU, with no LLM call and no data leaving the machine. Use this skill whenever the user wants to classify or route text, triage tickets or emails, detect spam, phishing, toxicity or intent, put a guardrail in front of an agent or LLM, score something against a rubric, or replace an LLM-based classifier to cut latency or cost. Also use it when they mention Laya, Jev, a "System 1 model", "typed decisions" or a "decision model", even if they never name Laya.

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。

Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

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。