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概要と使いどころ
Create new skills, modify and improve existing skills, and measure skill quality. Use when users want to create a skill from scratch, edit or refine an existing skill, test a skill with sample prompts, or optimize a skill's description for better triggering accuracy.
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 creating, modifying, or reviewing any Rembric agent plugin. Triggers on changes under `apps/plugin/`, on new clients added alongside Claude Code / Codex CLI / Hermes Agent / opencode / Pi, on edits to `apps/plugin/mcp-bridge/`, `apps/plugin/bin/rembric-plugin-core.mjs`, per-client manifest changes, or plugin install/uninstall scripts. End-to-end validation against a local dev server (`pnpm run dev`, host Next.js on :3000) is mandatory whenever local testing is feasible.
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 human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG. Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow", "user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation design.
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。
Manage database schema with Drizzle ORM and SQLite migrations. Use when adding tables, modifying columns, creating indexes, or running migrations. Activates for database schema changes, migration generation, and Drizzle query patterns.
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。
Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code 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。
Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.
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。
Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.
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。
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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。
Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.
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。
Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.
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。
Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data 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。
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。
Suggests CSII with good adherence to monitoring and dosing for adults with type 2 diabetes mellitus who have poor glycemic control despite intensive insulin therapy, oral agents, other injectable therapy, and lifestyle modifications. Trigger phrases include 'T2DM on multiple injectables still uncontrolled' and 'Consider pump for insulin-requiring T2DM failing other therapies'.
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。
Adjusts intravenous bisphosphonate dosing and infusion duration for patients with hypercalcemia of malignancy and renal insufficiency (creatinine clearance <60 mL/min). Triggered when a clinician encounters renal impairment and asks how to modify zoledronic acid or pamidronate infusion rates.
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。
Decide when to order coronary artery calcium (CAC) scoring in a primary-prevention patient and interpret the result per the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers the Class 1 indication (men ≥40 / women ≥45, borderline / intermediate PREVENT-ASCVD risk, uncertainty about LLT), CAC score interpretation (0, 1–99, 100–299, ≥300, ≥1000), how CAC modifies LDL and non-HDL goals, when to repeat CAC, and pitfalls (incidental CAC on non-cardiac CT, radiation, cost). Use when a clinician asks should I order a CAC score, CAC indications, what does CAC 150 mean, treatment goals with CAC score, repeat CAC frequency, CAC in a smoker, CAC vs CCTA, is CAC useful in intermediate risk. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.
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。
Guide pre-endoscopy management decisions for patients on GLP-1 receptor agonists (semaglutide, tirzepatide, liraglutide etc.), deciding whether to proceed, delay, or modify the approach based on GI symptoms, fasting status, and indication. Trigger when a clinician asks about GLP-1 RA and endoscopy, semaglutide before colonoscopy, Ozempic before procedure, or whether to hold a GLP-1 agonist before endoscopy.
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。
MANDATORY skill for ALL code generation. Enforces top-down composition, consistent abstraction layers, and proper module hierarchy. Apply every time you write or modify code — in any language — no exceptions.
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。
Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.
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 implementing or modifying NgRx state management in Angular applications, especially state slices, events, reducers, effects, selectors, and store registration.
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。
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
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。
Run and configure oxlint — the high-performance JavaScript/TypeScript linter built on the Oxc compiler stack. Use this skill whenever working in a project that has oxlint installed (check for `oxlint` in package.json devDependencies or an `.oxlintrc.json` / `oxlint.config.ts` config file). This includes when you need to lint code after making changes, fix linting errors, configure oxlint rules/plugins, set up or modify `.oxlintrc.json`, or migrate from ESLint.
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 or modify backend APIs, services, jobs, persistence, and integrations while preserving contracts, authorization, data integrity, and failure behavior. Use for server-side features, database changes, background processing, backend defects, and service refactors; do not use for frontend-only or infrastructure-only work.
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 or modify frontend interfaces using repository-native components while preserving accessibility, responsive behavior, state handling, and visual verification. Use for UI features, design implementation, forms, client-side defects, and frontend refactors; do not use for backend-only or infrastructure-only work.
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。