Results for “monolith-decomposition”

Design system architecture, evaluate microservices vs monolith, create architecture diagrams, analyze dependencies, choose databases, and make technical decisions with architecture decision records.

Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces service boundary diagrams and resilience strategies.

Train, evaluate, export, and run inference for monocular depth estimation models using Metric Depth Anything v2 or Relative Depth Anything architectures via the TAO toolkit.

Use when building falling leaves that read as leaves (not confetti): each leaf tumbling on its own axis with sideways slip driven by that tumble. Covers 2D canvas and instanced 3D, recycling, density maths, depth layering, tone-mapped colour, reduced motion, and visibility pausing—for maple, sakura, blossom, ash, or snowfall shapes.

SNLI-VE: Visual Entailment Dataset

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization, embedding generation, or as a preprocessing step before clustering. Part of the AlterLab Academic Skills suite.

Autopsy a stuck project, extract salvageable artifacts, and route to a fresh start via $bootstrap-repo reset or new-repo bootstrap

Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Mosaic Augmentation for Detection and Segmentation

Predict biomolecular complexes with Chai-1, an open AlphaFold3-style model that folds multi-entity assemblies (proteins, ligands, nucleic acids) from a single typed FASTA — strong on antibody–antigen and protein–ligand complexes, with optional MSA and restraint inputs. Use when predicting an antibody–antigen complex, folding a mixed protein/ligand/nucleic-acid assembly described in one FASTA, or generating a complex with experimental restraints. For binding-affinity prediction or a ligand-focused co-fold prefer alterlab-boltz; for protein-only or protein–protein folding prefer alterlab-alphafold; to dock into a fixed receptor prefer alterlab-diffdock. Part of the AlterLab Academic Skills suite.

Manipulate phylogenetic trees, detect evolutionary events, integrate NCBI taxonomy, and create publication-quality visualizations using the ETE toolkit.

Process whole slide images for digital pathology: detect tissue, extract tiles, and prepare datasets for deep learning pipelines.

Extract and preprocess tiles from whole-slide images (WSI) with histolab — OpenSlide-backed slide loading, tissue detection and masks, Random/Grid/Score tile extraction, and image/morphological filters for H&E preprocessing. Use when the user needs lightweight WSI slide preprocessing — building tile datasets for ML training, tissue segmentation, or quick tile-based inspection of histopathology slides. For end-to-end computational-pathology, deep-learning model training, nucleus segmentation, or multiplexed/spatial-proteomics (CODEX, Vectra) pipelines prefer alterlab-pathml instead. Part of the AlterLab Academic Skills suite.

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Architects, reviews, and scales backend systems with structured blueprints, trade-off analysis, and audit reports.

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Deduplicating Training Data Makes Language Models Better

Breaks natural-language problem descriptions into sub-tasks suitable for DAG nodes. The entry point of the meta-DAG. Identifies phases, dependencies, parallelization opportunities, and vague/pluripotent nodes that can't yet be specified. Uses domain meta-skills when available. Activate on "decompose task", "break down problem", "plan workflow", "what are the steps", "sub-tasks", "task breakdown". NOT for executing the decomposed tasks (use dag-runtime), building the DAG structure (use dag-planner), or matching skills to nodes (use dag-skills-matcher).

Decomposes large modules, god files, classes, or packages in reviewable steps while preserving public APIs. Not for routine refactors.

Detect pnpm workspace structure, optional Turborepo overlay, package metadata, and internal dependency graph for monorepo-aware skills

Transform monolithic Terraform configurations into reusable, maintainable modules following HashiCorp's module design principles and community best practices.

Multi-perspective dialectical reasoning with cross-evaluative synthesis. Spawns parallel evaluative lenses (STRUCTURAL, EVIDENTIAL, SCOPE, ADVERSARIAL, PRAGMATIC) that critique thesis AND critique each other's critiques, producing N-squared evaluation matrix before recursive aggregation. Triggers on /critique, /dialectic, /crosseval, requests for thorough analysis, stress-testing arguments, or finding weaknesses. Implements Hegelian refinement enhanced with interleaved multi-domain evaluation and convergent synthesis.

当需要解构一个复杂系统（如品牌、产品、知识体系）以理解其组成部分及结构时

Build custom PCB structures with pcbComponent, shapes, Boolean ops, feeds, and multi-layer stackups for non-catalog geometries. TRIGGER: user asks to build, modify, or customize a pcbComponent — add/remove shapes, edit polygons, place feeds, add metal layers, cut slots, or create non-catalog RF structures. Also when modifying geometry of an existing catalog-designed component (e.g., adding pads, removing elements, editing vertices). Invoke BEFORE writing pcbComponent code — layer/shape/feed API is non-obvious. SKIP: designing catalog components like filters/couplers/txlines (use the specific matlab-design-pcb-* skill), material/stackup definition only (use matlab-manage-pcb-material), EM analysis (use matlab-analyze-em), importing PCB files (use matlab-read-pcb-layout).

Detect monorepo structure, identify shared chokepoints, and generate safe parallel lane specs aligned to package boundaries

Diagnose monogenic and syndromic obesity in children and adolescents using a structured step-by-step algorithm. Use this skill whenever a clinician suspects a genetic cause of obesity, asks about leptin deficiency, MC4R mutation, POMC deficiency, PCSK1 deficiency, leptin receptor deficiency, Bardet-Biedl syndrome, Prader-Willi syndrome, Alström syndrome, or any case of early-onset severe obesity with hyperphagia. Also trigger for questions about targeted pharmacotherapy including setmelanotide or metreleptin, or when to order a genomic obesity panel. Cross-references the NHS Genomic Test Finder skill to surface the relevant R-code once a diagnosis is reached.

Documents MongoDB's visual design system including colors, typography, layout, elevation, and shapes for UI implementation.

Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

Documents the monospaced, manpage-inspired design system of OpenCode's marketing site, including typography, color palette, and spacing tokens.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

Apply-now consolidation of a drifted design system into one token/component SSOT. Use after an approved plan-uiux-unification pass, or when "clean up the design system now", "migrate to one button", or "resolve token conflicts". Plan-only burndown → plan-uiux-unification.

LEMON v2.0 — The Degen Fader. Identifies historically reckless traders (DEGEN activity + CHOPPY consistency) on Hyperliquid, monitors their live positions, and counter-trades them when they're bleeding at high leverage. If a cluster of degens goes max-leverage long on a coin and starts losing, LEMON shorts it — betting on their inevitable liquidation cascade. DSL exit managed by plugin runtime via runtime.yaml.

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.