「sat」の検索結果

Provider-independent color grading and finishing direction for generated video, ads, trailers, product films, social clips, explainers, and mixed-source edits. Use when planning, directing, reviewing, or QAing color correction, shot matching, exposure, contrast, saturation, skin/product color protection, look development, LUT/reference use, SDR/HDR color management, delivery constraints, generated-media artifacts, editor/compositor handoff, and finishing QC.

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。

Build targeted outreach lists with verified contact information, professional context, and conversation starters. Combines Apollo company data, LinkedIn profiling, and email verification to produce ready-to-send outreach campaigns for sales, PR, and partnership teams.

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。

Research any topic in exhaustive depth by combining web search, academic papers, social discussions, and community conversations. Produces comprehensive briefings with evidence from every available source type, confidence scoring, and identified knowledge gaps.

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。

Research products across Amazon, Google Shopping, and Xiaohongshu to understand pricing, reviews, market positioning, and consumer sentiment. Covers competitive product analysis, pricing benchmarks, feature comparison, and customer satisfaction signals.

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。

Monitor conversations about any topic, brand, or keyword across Twitter, Reddit, Threads, Weibo, and community platforms simultaneously. Aggregates volume, sentiment, key voices, and emerging narratives across the global social web.

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。

Prepare for sales conversations with comprehensive account intelligence — company news, hiring signals, product launches, social activity, and decision-maker profiles. Combines firmographic data, news monitoring, and social signals for sales-ready briefings.

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。

Aggregate and analyze product and business reviews from Google Maps, Amazon, Akta, and Xiaohongshu. Cross-platform review intelligence including rating patterns, common themes, competitive comparison, and customer satisfaction trends.

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。

Research companies with employee reviews, product reviews, industry classification, and competitive landscape through Akta. Covers company enrichment, employee satisfaction signals, product feedback, industry mapping, and news monitoring.

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。

Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.

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。

Assesses genetic health of populations for conservation using effective population size estimation (GONE2 for recent Ne trajectory, NeEstimator for contemporary Ne, Stairway Plot 2 and PSMC for historical Ne), F-statistics (hierfstat), runs of homozygosity (detectRUNS), and genetic diversity metrics. Use when estimating effective population size, detecting inbreeding or bottlenecks, or assessing genetic diversity in threatened species from microsatellite or SNP 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。

End-to-end flow cytometry workflow from FCS files to differential analysis. Orchestrates compensation, transformation, gating/clustering, and statistical testing with CATALYST/diffcyt. Use when processing flow or mass cytometry data end-to-end.

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。

Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.

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。

Bisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.

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。

Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.

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。

Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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 pre-meeting lead intelligence briefs with Firecrawl. Use when the user needs company research, person research, recent news, talking points, pain points, or outreach preparation before a sales call, partnership meeting, investor conversation, or customer interview.

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。

Read incoming retalk messages from this session's user's DESIGNATED sender(s) — one-shot, or as a background --follow reader that surfaces new messages in the session as they arrive (on your next turn). agent-talk only ever receives from specific saved peers, never the whole mailbox (safety). `<user>` is this session's user directory (absolute path; from init). Always renders the conversation (sent + received) as a beautiful chat transcript so the user can track it. Use to check mail or stay reachable.

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。

Send an end-to-end-encrypted message to a peer, autonomously. Use whenever this agent should message another agent or person over retalk. Designed to run with no human supervision — it resolves the recipient from saved contacts; a routine send never stops to ask — but it always renders the conversation (both sent and received) as a beautiful chat transcript so the user can track it. (Recipient/relay are set up once by the init 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。

Use when a conversation begins with a CesiumJS development request - maps the 14 domain skills on offer and explains how passive activation selects between them

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 tutoring a student in MATLAB programming, coaching beginners, explaining MATLAB concepts interactively, or running a conversational AI tutor session.

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。

Execute approved, bounded work in an isolated implementation conversation and return a structured evidence receipt. Use with inherited history or an explicitly identified source; no particular planning, testing, or review skill 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。

Turn the current conversation into a spec and publish it to the project issue tracker: no interview, just synthesis of what you've already discussed.

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。

Break a plan, spec, or the current conversation into a set of tracer-bullet tickets, each declaring its blocking edges, published to the configured tracker (edges as text in one file per ticket locally, or native blocking links on a real tracker).

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。