SSRF playbook. Use when the server fetches URLs, resolves hostnames, imports remote content, or can be driven toward internal networks, cloud metadata, or secondary protocols.
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。
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SSRF playbook. Use when the server fetches URLs, resolves hostnames, imports remote content, or can be driven toward internal networks, cloud metadata, or secondary protocols.
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。
Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
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 and runs cloud infrastructure — environments, infrastructure as code, networking and isolation, scaling, and cost. Use this to design a cloud environment, control infrastructure spend, set up environment separation, plan for scale or region failure, or review infrastructure someone configured by hand.
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。
The social network for AI agents. Post, comment, upvote, and create communities.
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 when wiring validation, error handling, or framework adapters. Concentrate guards at system boundaries (CLI, config, network, external APIs); trust internal types and keep business logic in pure functions.
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。
Generate Circos configuration files for circular genomics data visualization. Supports genomic variations (SNPs, CNVs, structural variants), cell-cell communication networks, and custom track configurations for publication-ready circular plots.
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。
Draft professional follow-up emails to contacts made at conferences - not too pushy, but memorable.
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 complete phenotype-scoring bioinformatics research designs for any disease context and any user-defined phenotype, pathway, process, signature, or molecular program. Use when a study centers on gene-set or feature-set definition, intersection with DEGs or candidate features, phenotype scoring, feature selection, diagnostic or stratification assessment, immune or cellular-resolution interpretation, network analysis, and optional orthogonal validation. Covers five study patterns (signature discovery, phenotype scoring, feature selection, immune/cellular interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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 complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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 complete comorbidity-oriented shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Use when a study links two clinically related diseases through shared DEGs, enrichment, PPI hub genes, machine-learning feature selection, public diagnostic validation, gene-regulatory networks, immune infiltration, and optional downstream follow-up. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, machine-learning biomarker selection, immune/regulatory interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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 generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.
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 building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.
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 building a ceRNA regulatory network from a key gene list by combining bundled miRNA-mRNA and miRNA-lncRNA database files, with flat-file CSV exports and PDF visualization in a single output directory. NOT for: differential expression, single-cell analysis, enrichment analysis, or workflows without a key gene list.
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 filtering genes with high missingness and then imputing missing values in a bulk expression matrix with group-aware KNN through DMwR2, where donor samples are restricted by one annotation column before imputation. For strata with 10 or fewer samples, the script falls back to row-wise direct filling with mean or median. NOT for: single-cell data, multi-column stratification, non-tabular inputs, network access, or interactive workflows.
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 evaluating Tiger Cub / Chase Coleman-style growth investing: scalable tech platforms, network effects, unit economics, growth duration, and concentrated growth longs.
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 evaluating Hindenburg-style investigative short reports: evidence chains, promoter networks, stock promotion, related parties, disclosure failures, and catalyst-driven short narratives.
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。
Local drop-in API emulator for Vercel, GitHub, Google, Slack, Apple, Microsoft, AWS, Clerk, Linear, Twilio, and other developer APIs. Use when the user needs to start emulated services, configure seed data, write tests against local APIs, set up CI without network access, or work with the emulate CLI or programmatic API. Triggers include "start the emulator", "emulate services", "mock API locally", "create emulator config", "test against local API", "npx emulate", or any task requiring local service emulation.
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 erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building fault-tolerant multi-node applications on the BEAM VM.
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 deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML/SVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, plots, axes/scales, legends, tooltips, or data-driven SVG output.
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。
Exact detection thresholds for identifying malicious network patterns including port scans, DoS attacks, and beaconing behavior.
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。
DC power flow analysis for power systems. Use when computing power flows using DC approximation, building susceptance matrices, calculating line flows and loading percentages, or performing sensitivity analysis on transmission networks.
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。
Power system network data formats and topology. Use when parsing bus, generator, and branch data for power flow 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。
Implement mHC (Manifold-Constrained Hyper-Connections) for stabilizing deep network training. Use when implementing residual connection improvements with doubly stochastic matrices via Sinkhorn-Knopp algorithm. Based on DeepSeek's 2025 paper (arXiv:2512.24880).
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。
Comprehensive Content Security Policy audit for MCP Apps in sandboxed iframes. Discovers all network origins, traces them to source, and generates CSP configuration for registerAppResource.
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。