Results for “beeminder”

Use when running HMMER master or worker daemon services that front `phmmer`, `hmmsearch`, and `hmmscan` against cached databases.

Sets up BMad Builder module in a project. Use when the user requests to 'install bmb module', 'configure BMad Builder', or 'setup BMad Builder'.

Sets up BMad Builder module in a project. Use when the user requests to 'install bmb module', 'configure BMad Builder', or 'setup BMad Builder'.

Trains, evaluates, and runs inference for BEVFusion multi-sensor 3D object detection models that fuse LiDAR and camera data in bird's-eye-view space for autonomous driving.

Migrates AEM Cloud Service code from legacy AssetManager APIs (createAssetForBinary, getAssetForBinary, removeAssetForBinary, client-facing createAsset) to Direct Binary Access or in-JVM resolver patterns, covering IMS authentication and service-user setup.

Packet-first BMAD/BMM front door for idea notes, product briefs, PRDs, architecture drafts, review feedback, existing repo state, and milestone pressure. Use when the user wants to know what BMAD phase or artifact comes next, or needs a portable BMAD entrypoint before routing review, execution slicing, runtime setup, or game-production work outward.

Fetches current weather data for Belgian locations using the KMI/IRM meteo.be API.

Bedside prescribing reference for Bemdec (bempedoic acid / NEXLETOL) — indication check, dose, statin co-prescribing safety caps, monitoring plan, warnings for hyperuricemia and tendon rupture, and special population guidance. Use when a clinician asks "can I start bempedoic acid", "Bemdec indication", "is bempedoic acid safe with my statin", "patient on Nexletol has joint pain or high uric acid", "bempedoic acid in CKD or liver disease or pregnancy", or any prescribing or monitoring question about bempedoic acid.

Sets up BMad Builder module in a project. Use when the user requests to 'install bmb module', 'configure BMad Builder', or 'setup BMad Builder'.

Essa habilidade deve ser usada ao trabalhar com dados de intervalos genômicos (arquivos BED) para tarefas de machine learning. Use para treinar embeddings de regiões (Region2Vec, BEDspace), análise de scATAC-seq de célula única (scEmbed), construir picos consensuais (universos), ou qualquer análise baseada em ML de regiões genômicas. Aplica-se a coleções de arquivos BED, dados scATAC-seq, conjuntos de dados de acessibilidade de cromatina e aprendizado de recursos genômicos baseado em regiões.

Query Belgian weather data from the KMI/IRM meteo.be API, including current conditions, forecasts, radar, warnings, and UV index for Belgian locations.

Generate cinematic short-form video with ByteDance Seedance 2.0 Pro on RunComfy. Documents Seedance 2.0 Pro's strengths (multi-modal references — up to 9 images, 3 videos, 3 audio — synchronized in-pass audio with natural lip-sync, cinematic motion refinement), the 4–15s duration schema, and when to route to HappyHorse 1.0 / Wan 2.7 / Kling instead. Calls `runcomfy run bytedance/seedance-v2/pro` through the local RunComfy CLI. Triggers on "seedance", "seedance 2", "seedance v2", "seedance pro", "bytedance video", or any explicit ask to generate video with this model.

Search meme templates, suggest formats, and generate local or hosted image memes.

Use when sampling synthetic sequences, alignments, or consensus sequences from one or more profile HMMs.

Generate cinematic short-form video with ByteDance Seedance 2.0 Pro on RunComfy. Documents Seedance 2.0 Pro's strengths (multi-modal references — up to 9 images, 3 videos, 3 audio — synchronized in-pass audio with natural lip-sync, cinematic motion refinement), the 4–15s duration schema, and when to route to HappyHorse 1.0 / Wan 2.7 / Kling instead. Calls `runcomfy run bytedance/seedance-v2/pro` through the local RunComfy CLI. Triggers on "seedance", "seedance 2", "seedance v2", "seedance pro", "bytedance video", or any explicit ask to generate video with this model.

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.

Search meme templates, suggest formats, and generate local or hosted image memes.

Embody the medieval Jewish philosopher Maimonides to discuss faith, reason, and theology with rational precision and hierarchical clarity.

Local search/indexing CLI (BM25 + vectors + rerank) with MCP mode.

Indexes local files and searches them with BM25, vector, and hybrid queries, plus MCP mode.

Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.

Generate high-quality sentence and text embeddings for semantic similarity, clustering, and retrieval using 5000+ pre-trained models. Supports multilingual and domain-specific embeddings for RAG and semantic search.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Generate cinematic short-form video with ByteDance Seedance 2.0 Pro on RunComfy. Documents Seedance 2.0 Pro's strengths (multi-modal references — up to 9 images, 3 videos, 3 audio — synchronized in-pass audio with natural lip-sync, cinematic motion refinement), the 4–15s duration schema, and when to route to HappyHorse 1.0 / Wan 2.7 / Kling instead. Calls `runcomfy run bytedance/seedance-v2/pro` through the local RunComfy CLI. Triggers on "seedance", "seedance 2", "seedance v2", "seedance pro", "bytedance video", or any explicit ask to generate video with this model.

Sets up the BMad ML module in a project. Use when the user requests to 'install ML module', 'configure BMad ML', or 'setup BMad ML'.

Implements backend source code from BA, TechLead, and PM artifacts, generating files, .env.example, and pr-description.md while enforcing security rules.

Gemini: A Family of Highly Capable Multimodal Models

Generates OMG-compliant BPMN 2.0 XML and SVG diagrams from natural language process descriptions, with validation, automatic layout, and optional process optimization advisories.

Bidirectional conversion between Elixir and Elm. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elixir↔Elm specific patterns. Use when migrating server-side Elixir logic to frontend applications, translating BEAM concurrency patterns to The Elm Architecture, or refactoring Elixir codebases for browser-based UI. Extends meta-convert-dev with Elixir-to-Elm specific patterns.

Assist with BNB Chain transactions, BEP-20 tokens, gas fees, and cross-chain transfers.

Use when searching one or more protein query sequences against a protein sequence database with HMMER's one-pass sequence-vs-sequence searcher.