「agent workflow」の検索結果

Provider-independent production workflow for AI agents creating game trailers and promos, including announcement, gameplay, launch, DLC/update, store-page videos, social cutdowns, and generated/engine-capture hybrid trailers. Use when planning, scripting, capturing, generating, editing, localizing, reviewing, or QAing game marketing videos where gameplay truthfulness, platform/store constraints, ratings, accessibility, claims, CTA metadata, and rights provenance 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 production workflow for AI agents creating brand launch films, product reveal videos, manifesto films, campaign hero films, website hero videos, investor/customer launch assets, and social cutdowns. Use when planning, scripting, generating, editing, reviewing, or delivering launch films that must align strategy, claims, brand voice, visual direction, product proof, accessibility, compliance, and multi-platform 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。

Provider-independent editing and montage direction for AI agents producing generated videos, ads, trailers, social clips, explainers, product videos, documentary-style pieces, and music- or beat-driven cuts. Use when planning, revising, QAing, or handing off an edit: story structure, shot selection, continuity, montage theory, rhythm and pacing, J/L cuts, match cuts, cutaways, transitions, beat sync, platform timing, source/generated asset management, revision workflow, and composition handoff.

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 SaaS and software product demo videos, including app walkthroughs, feature launches, sales demos, onboarding clips, help-center explainers, website hero demos, and PLG assets. Use when an agent must plan, script, capture, synthesize, edit, localize, review, or QA privacy-safe software demo videos with substantiated claims, accessible captions, screen/cursor direction, CTA variants, and stakeholder approval.

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 production workflow for AI agents creating educational animated lessons, classroom explainers, STEM visualizations, history/social-science animations, training modules, microlearning clips, whiteboard-style explainers, diagram-driven videos, and generated or assembled instructional animation assets. Use when planning, scripting, storyboarding, generating, reviewing, localizing, or QAing animation whose primary purpose is learning.

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。

Provider-independent production workflow for AI agents creating music videos, lyric videos, visualizers, performance/narrative/dance videos, social cutdowns, album or single launch assets, and generated-media clips synchronized to songs. Use when planning, directing, prompting, editing, reviewing, or delivering video content built around a recorded song, artist performance, lyrics, beat timing, choreography, platform variants, music rights, likeness consent, accessibility, or synthetic-media disclosure.

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 production workflow for recutting long talking-head, podcast, interview, webinar, panel, lecture, livestream, founder call, or customer-call footage into short clips, highlight reels, trailers, audiograms, captioned vertical videos, and social cutdowns. Use when an agent must ingest source recordings, transcribe and diarize speakers, select quotes without misrepresentation, preserve context, handle consent/rights/disclosures, clean jump cuts and audio, add captions, graphics, lower thirds, b-roll, platform variants, synthetic-media boundaries, review packets, delivery specs, and recut 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 production workflow for AI-assisted fashion campaigns, lookbooks, editorial fashion films, ecommerce/editorial hybrids, social cutdowns, virtual try-on assets, generated model/lifestyle ads, and retouched fashion visuals; use when an agent must plan, prompt, produce, review, localize, or deliver fashion imagery or video while preserving garment truthfulness, model consent, fit representation, claims substantiation, platform compliance, accessibility, and fashion 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 storyboard and previsualization direction for AI agents turning briefs, scripts, treatments, ads, films, social videos, and generated-media concepts into shot-by-shot boards, animatics, previs plans, camera/blocking/movement direction, reference packs, AI image/video prompt plans, review workflows, continuity checks, and production handoffs.

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 obsidian bases as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests obsidian bases behavior or files.

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 obsidian markdown as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests obsidian markdown behavior or files.

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 adhd-friendly communication as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests adhd-friendly communication behavior or files.

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 memory management as a bounded agent workflow while preserving user intent, privacy, and host capabilities. Use when the user explicitly requests memory management behavior or files.

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。

All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.

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 eDNA metabarcoding from raw amplicons to community ecology. Covers QC, primer removal, denoising with OBITools3 or DADA2, contamination filtering, taxonomy assignment, Hill number diversity, and constrained ordination. Use when processing environmental DNA samples for biodiversity assessment or ecological surveys.

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。

Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.

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 CRISPR experiment design from target selection to delivery-ready constructs. Covers guide RNA design, off-target assessment, and specialized editing strategies including knockouts, base editing, and HDR knockins. Use when designing complete CRISPR editing experiments for gene knockout, correction, or tagging.

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。

Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.

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。

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。

End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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 CLIP-seq pipeline from FASTQ to ENCODE-compliant binding sites, single-nucleotide crosslink maps, annotation, motifs, and (optionally) differential binding. Use when running the full Yeo lab eCLIP / iCLIP / iCLIP2 / iCLIP3 / irCLIP / PAR-CLIP analysis with SMInput control, protocol-specific UMI extraction, ENCODE STAR parameters, CLIPper or Skipper peak calling with stringent log2 FC and -log10 p thresholds, IDR rescue and self-consistency QC, and downstream motif registration with mCross or PEKA.

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。

Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.

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。

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

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。