Generate a voiceover (VO) clip via ElevenLabs text-to-speech, ROUTED THROUGH THE elevenlabs-proxy so it bills the Ads agent. Voice id + script text come from the template recipe. Use for the spoken narration of VO-driven video-ad formats (cgi-app-sizzle, flat-vector-explainer, hypermotion). Never call ElevenLabs directly — the proxy attribution is required.
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☆ 1,2542026年10月17日 更新
Shared helper that routes ALL paid media generation (FAL image/video, ElevenLabs music) through the GooseWorks proxies so every call bills the Ads agent — never a provider SDK's default host. Host-swaps the FAL queue URLs, loads the agent token from the sandbox env (GW_MEDIA_PROXY_TOKEN) or ~/.gooseworks/credentials.json, and returns the result CDN URL. Every video-ad media capability imports this; templates never call a provider directly.
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☆ 1,2542026年10月17日 更新
Generate an instrumental music bed via ElevenLabs Music, ROUTED THROUGH THE elevenlabs-proxy so it bills the Ads agent. Trims any sparse intro, loudnorm, fades the tail. Prompt + length from the template recipe. Use for the music layer of any video-ad 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☆ 1,2542026年10月17日 更新
Image-to-video (or text-to-video) via any FAL video model (Kling, Seedance, Veo), ROUTED THROUGH THE GooseWorks fal-proxy so the call bills the Ads agent. The template recipe names the model + params; image_url inputs must be public URLs (the orchestrator hosts local frames with the MCP media_upload). Returns the result video URL and downloads it. Use for the generative base clip of any video-ad 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☆ 1,2542026年10月17日 更新
Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate 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。
GPTomics/bioSkills☆ 1,2232026年8月15日 更新
Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.
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☆ 1,2232026年8月15日 更新
Discovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-split step, orthogonal CAGE/poly-A/short-read-junction validation, and why long-read isoform quantification needs EM. Use when building a full-length isoform catalog, classifying/filtering long-read transcripts, running Iso-Seq or ONT cDNA/dRNA analysis, or judging novel-isoform reliability.
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☆ 1,2232026年8月15日 更新
Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.
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☆ 1,2232026年8月15日 更新
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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☆ 1,2232026年8月15日 更新
Use when working with camera, photos, audio, haptics, ShazamKit, on-device music analysis, face grouping and video highlights, screen recording, casting to non-AirPlay routes, SharePlay playback, the user's Apple Music library, lock-screen metadata, or CarPlay app design, templates, and navigation. Covers AVCaptureSession, AVFoundation, Core Haptics, MediaPlayer, MusicKit, NowPlaying, ScreenCaptureKit, DockKit.
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。
CharlesWiltgen/Axiom☆ 1,2172026年10月16日 更新
Use when planning many similar pages from a template and a data set: choosing the pattern, deciding whether the data can carry it, and avoiding the thin-content failure that gets page sets deindexed.
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。
fcakyon/claude-codex-settings☆ 1,1792026年10月16日 更新
Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this 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。
fcakyon/claude-codex-settings☆ 1,1792026年10月16日 更新
Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.
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。
fcakyon/claude-codex-settings☆ 1,1792026年10月16日 更新
Find and query PI assets using MATLAB's Industrial Communication Toolbox. Two paths: (1) direct PI tag lookup via piclient + tags when user provides a tag name or description (R2022a+), (2) Asset Framework navigation via afclient when user needs element/attribute hierarchy (R2026a+). Use when working with PI AF servers, PI Data Archive tags, asset hierarchies, element templates, attribute lookup, or historical data from OSIsoft PI systems.
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。
matlab/matlab-agentic-toolkit☆ 1,1752026年10月14日 更新
Shopify/DTC brand protection toolkit. Detect counterfeit stores, unauthorized resellers, and trademark violations. Includes DMCA takedown templates, domain monitoring, and social media infringement detection. No API key required.
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
Walmart brand protection toolkit. Detect unauthorized sellers, counterfeits, and MAP violations. Includes Walmart Brand Portal reporting, WFS seller monitoring, and complaint templates. No API key required.
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
eBay brand protection toolkit. Detect unauthorized sellers, counterfeits, and VeRO violations. Includes price monitoring, trademark abuse detection, VeRO complaint templates, and enforcement guides. No API key required.
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
TikTok Shop brand protection toolkit. Detect unauthorized sellers, counterfeit products, and affiliate abuse. Includes TikTok IP Protection reporting, influencer misuse detection, and complaint templates. No API key required.
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
Amazon brand protection toolkit. Detect hijackers, counterfeits, and unauthorized sellers. Includes MAP violation monitoring, trademark abuse detection, complaint templates for Brand Registry, and test buy evidence collection guides. No API key required.
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
TikTok Shop shipping setup — carrier selection, shipping templates, delivery expectations
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
Fulfillment setup — Fulfilled by TikTok, self-fulfillment, shipping templates, return policies
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
Digital product creation and selling — templates, printables, planners, digital art, delivery setup
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
Custom order management — communication templates, pricing, delivery timelines, return policies
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。
nexscope-ai/eCommerce-Skills☆ 1,1682026年8月26日 更新
Deploy AWS resources with CloudFormation templates. Create stacks, use nested stacks, and implement drift detection. Use when deploying AWS-native IaC.
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。
BagelHole/DevOps-Security-Agent-Skills☆ 1,1642026年5月22日 更新