863 件 ・ 関連度順
概要と使いどころ
Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs integration.
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。
Drafts a U.S. C-TPAT Security Profile for CBP submission covering physical, personnel, procedural, conveyance, and IT security domains. Use when preparing C-TPAT enrollment, certification, validation, or recertification profiles, or assembling a CBP-ready security narrative. Trigger: C-TPAT, CBP security profile, supply chain security, trusted trader, customs validation.
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。
Generates a post-judgment asset and enforcement report synthesizing debtor asset investigations, exemption analysis, fraudulent transfer assessment, and prioritized collection strategies. Use when preparing judgment enforcement plans, debtor asset profiles, collection status reports, or post-trial recovery 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。
Target a specific Chrome profile for browser automation via CDP: discovers profiles, launches on a debug port, connects agent-browser. NOT for headless scraping without a profile (mk:agent-browser).
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。
Target a specific Google Chrome user profile for browser automation via CDP. Discovers installed profiles from the Chrome Local State file, launches Chrome with the requested profile attached to a debug port, and connects agent-browser via `agent-browser connect <port>`. Use when "automate using my Work/Personal Chrome", "open site with my saved session", or "use Chrome profile named <name>". NOT for headless scraping without an existing user profile (use mk:agent-browser directly); NOT for importing cookies only (use agent-browser --auto-connect state save).
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。
Configure and diagnose Fusion interoperability, transport selection, scoped profiles and qualification. Use for setup or connection problems, not design 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。
Reddit content discovery skill. Browse subreddits, search posts, view post details, check user profiles. Triggered when user asks to search Reddit, browse subreddits, view posts, or check users.
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 CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry pipelines.
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。
Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).
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。
Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).
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。
Manages agent isolation levels and resource boundaries. Configures strict, moderate, and permissive isolation profiles. Activate on 'isolation level', 'agent isolation', 'resource boundaries', 'sandboxing', 'agent containment'. NOT for permission validation (use dag-permission-validator) or runtime enforcement (use dag-scope-enforcer).
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。
App Store and Google Play submission automator with Fastlane, screenshot automation, metadata management, and TestFlight/internal testing. Activate on: app store submission, Fastlane, TestFlight, Google Play Console, screenshot automation, metadata management, app review, code signing, provisioning profiles. NOT for: CI/CD pipeline setup (use github-actions-pipeline-builder), app architecture (use react-native-architect), analytics (use mobile-analytics-crash-reporting-expert).
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。
Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline; DELFI is a method, not a package. Use when deriving fragment-based signal from cfDNA, choosing a feature family for detection vs subtyping, or diagnosing why a fragmentomic model failed validation.
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。
Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or trimming low-quality bases, generating quality reports, or deciding which FASTQ quality encoding a file uses before parsing.
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。
Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
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。
Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.
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。
Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.
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。
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
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。
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell 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。
Evaluates cardiovascular risk factors in persons receiving hormone therapy by obtaining fasting lipid profiles and diabetes screening. Triggered when a patient is on gender-affirming hormone therapy and due for routine cardiovascular risk assessment.
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。
Guides comorbidity-stratified selection of initial and further glucose-lowering medicines for adults with type 2 diabetes using the NICE NG28 (2026) framework, covering six clinical profiles: no relevant comorbidity, heart failure, atherosclerotic cardiovascular disease, early onset, obesity, chronic kidney disease, and frailty.
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。
Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs integration.
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 for read-first Amazon Sponsored Products optimization, product-aware growth, preflight readiness, and post-change monitoring reviews that can run standalone from static exports or use Rocketcart MCP as an optional Amazon Ads plus product-intelligence layer. Guides Codex to inspect Rocketcart profiles, live SP campaigns, product ads/ASIN context, category rank/BSR movement, competitor signals, product readiness, budget and targeting drift, snapshots/changelogs, and proposed approval-gated action rows without executing writes by default.
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。
Audit social media accounts (TikTok, IG, etc.): scrape profiles, calculate engagement metrics, diagnose performance drops, interpret analytics screenshots.
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。