Dispatch bulk RNA-seq requests to FASTQ-to-count QC or count-matrix differential-expression skills using nf-core/rnaseq, STAR, Salmon, featureCounts, MultiQC, and R/Bioconductor workflows.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill as the bulk RNA-seq dispatcher. Route FASTQ/BAM processing to count-generation QC, and route count-matrix statistical analysis to differential-expression guidance.
Confirm:
ngs-bulk-rnaseq-counts-qcngs-bulk-rnaseq-differential-expressionIf the user asks for both, run count-generation planning first and start differential expression only after the raw count matrix, sample metadata, replicates, design formula, and contrasts are confirmed.
Prefer nf-core/rnaseq for standardized processing when a stable container or HPC runtime is available. Use the local_light Snakemake/Salmon path when Docker, registry egress, or Nextflow process containers are unavailable and a compact local run is appropriate.
Use the counts/QC runner for local FASTQ-to-matrix execution:
python plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \
--sample-sheet samplesheet.csv \
--fastq-root path/to/fastqs \
--transcriptome-fasta reference/transcriptome.fasta \
--genome-fasta reference/genome.fa \
--annotation-gtf reference/genes.gtf \
--execute
Use the differential-expression runner when the user already has a count or expression matrix:
python plugins/ngs-analysis/scripts/run_bulk_rnaseq_de.py \
--count-matrix count_matrix.tsv \
--sample-metadata sample_metadata.tsv \
--contrasts contrasts.tsv \
--execute
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_counts_qc --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline bulk_rnaseq_differential_expression --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --profile local_light --emit-install-plan
Preflight run:
nextflow run nf-core/rnaseq \
-profile test,docker \
--outdir results/rnaseq_test
Real run skeleton:
nextflow run nf-core/rnaseq \
-profile docker \
--input samplesheet.csv \
--outdir results/rnaseq \
--genome GRCh38 \
--aligner star_salmon
If strandedness is unknown, run inference or use the pipeline's strandedness detection before committing to final counts.
Local execution run:
python plugins/ngs-analysis/scripts/run_bulk_rnaseq_counts_qc.py \
--sample-sheet samplesheet.csv \
--fastq-root path/to/fastqs \
--transcriptome-fasta reference/transcriptome.fasta
The local runners create a standard run envelope with run_manifest.json, config.json, validation/, logs/, versions/, artifact_index.json, and summary.md. Do not depend on development-only eval harness paths in a shared package.
Only start DESeq2/edgeR/limma analysis after confirming biological replicates, design formula, and contrasts. Preserve the raw count matrix and sample metadata.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.
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 consistent photo adjustments across a set of images so they look like they were edited together. Use this skill whenever the user says "make my photos look cohesive", "give all these the same style", "apply a warm and golden feel to all of these", "make this cinematic", "match the look across my photos", "edit all my travel photos the same way", "batch edit these", "make these consistent", "fix my phone photos", or uploads a folder of photos and wants a unified, polished result. Also triggers for requests like "apply a preset to all of these", "make these look professional", or "they were shot in mixed lighting — can you fix them all". Outputs direct final image URLs plus an in-chat preview grid and optional Firefly Board link. Access: 🔐 Signed-In required | Gen AI: ❌
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 user wants to see their logo, design, or sketch on a product or scene mockup — mugs, t-shirts, business cards, hats, phone screens, posters, billboards, or similar. Triggers on "create mockups", "show my logo on products", or any logo upload with a request to visualize it on items. Access: 🔐 Signed-In required | Gen AI: ✅ Adobe Firefly via `image_generate` used for design creation, sketch polishing, and mockup scene generation
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。
Resize, crop, or export any image or video into platform-ready social media assets using Adobe Creative Cloud tools. Use this skill when a user wants to prepare a photo, image, or video for one or more social platforms — Instagram, TikTok, LinkedIn, Facebook, YouTube, Snapchat, Pinterest, Threads, or X/Twitter. Triggers on: "prepare my image for Instagram", "resize for TikTok", "get this ready to post", "make versions for all platforms", "social media sizes", "crop for stories", "export for LinkedIn", "resize my video for social", "make social media assets", or any request to adapt a photo or video for specific platforms. Handles subject-aware cropping, AI canvas expansion, test previews before full runs, and same-ratio video resizing.
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。
Create any visual design using Adobe Express templates — flyers, posters, social media posts (Instagram, Facebook, LinkedIn), business cards, invitations, greeting cards, resumes, cover letters, brochures, newsletters, certificates, presentations, YouTube thumbnails, email headers, logos, menus, and labels. Use this skill whenever the user wants to make, design, or build any visual — even if they just say "make me a flyer", "design a poster", "I need something for Instagram", "create an event invite", or "make a business card". Also handles browsing templates, editing text, replacing images, changing backgrounds, animating, and exporting designs. Access: 🔐 Signed-In required | Gen AI: ❌ by default — image replacement only where the surface permits generative AI (e.g. Codex); none on Claude
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。
Create a punchy sizzle reel from a video using Adobe Quick Cut. Use this skill whenever a user wants to cut, trim, or shorten a video into highlights — including phrases like "make a sizzle reel", "make a highlight reel", "quick cut this", "cut the best parts", "shorten this video", "make a highlight clip", "summarize this video visually", or any request to produce a shorter edited version of a video. Use this skill for Quick Cut requests before suggesting manual editing in Premiere. Requires the user to upload a video file.
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。