Loading...
Loading...
Found 1,747 Skills
Generates the Somnio HandShake Step 3 - Acknowledgement PDF document from raw evaluation notes. Use this skill whenever an Engineering Manager (EM) wants to create, generate, or produce one or more HandShake Acknowledgement documents, career review PDFs, seniority evaluation documents, or anything related to the Somnio bi-annual performance review process. Trigger even if the user says things like: generar el documento del handshake, armar el PDF de la evaluacion, crear el acknowledgement para un dev, generar el informe del career path, procesar estas fichas, or similar. The skill handles both single and batch (multiple devs) generation: it reads all attached ficha files, pre-fills what it can infer, confirms all data with the EM in a single table, then generates one PDF per dev.
Implement Syncfusion WPF SplitButton (SplitButtonAdv) with dropdown menus, command binding, and data binding. This skill applies whenever a user mentions WPF splitbutton, dropdown buttons with menus, the SplitButtonAdv control, implementing splitbutton patterns, button–menu combinations, or scenarios that require a button with both a default action and a dropdown menu. It is also relevant for WPF command-enabled splitbutton, MVVM-based splitbutton implementations, data‑bound dropdown menus, or any situation involving buttons that offer selectable dropdown items.
Make sure to use this skill whenever the user mentions anything related to Danish property data, housing prices, real estate statistics, sold homes, property history, BBR data, or the Danish housing market — even if they don't mention boliga.dk explicitly. Also invoke this skill for questions about specific Danish addresses, zip codes, or municipalities in a housing context. Trigger phrases include: danish property, danish real estate, danish housing market, boliga, bolig til salg, solgte boliger, boligpriser, ejendomspriser, ejendom, ejerlejlighed, villa, rækkehus, sommerhus, fritidshus, andelsbolig, helårsgrund, landejendom, BBR data, bygningsregistret, property for sale denmark, sold homes denmark, house prices denmark, apartment prices copenhagen, aarhus housing, odense real estate, housing statistics denmark, quarterly price index denmark, most viewed properties denmark, property valuation denmark, ejendomsvurdering, salgspris, kvadratmeterpris, days on market, dage til salg, boligsøgning, address lookup denmark, property history denmark.
Use when you need to add or evaluate Maven dependencies that improve code quality — including nullness annotations (JSpecify), static analysis (Error Prone + NullAway), functional programming (VAVR), or architecture testing (ArchUnit) — and want a consultative, question-driven approach that adds only what you actually need. Part of the skills-for-java project
Apply Edward de Bono's parallel thinking framework (1985) to make better decisions by examining ideas from six distinct perspectives systematically. Use when: **Making complex decisions** that require multiple perspectives; **Evaluating new products, offers, or strategies** before launch; **Breaking out of analysis paralysis** with structured thinking; **Running productive meetings** where everyone thinks in the same direction; **Balancing optimism with caution** in strategic planning
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Analyzes living systems and biological phenomena through biological lens using evolution, molecular biology, ecology, and systems biology frameworks. Provides insights on mechanisms, adaptations, interactions, and life processes. Use when: Biological systems, health issues, evolutionary questions, ecological problems, biotechnology. Evaluates: Function, structure, heredity, evolution, interactions, molecular mechanisms.
Guide users through defining their pricing strategy for an AI product or SaaS. Covers billing model selection (usage-based, subscription, hybrid), subscription tier pricing, credit/overage costs, real-time vs invoice billing trade-offs, existing PSP integration, custom currency vs fiat, and pricing dimensions. Ends with a personalised pricing strategy summary, MRR projection, visual output (HTML or PDF), and tool recommendations. Use when a user wants to define their pricing, figure out how to charge for their AI product, decide between billing models, understand the real-time vs invoice billing trade-off, or evaluate what tools to use for monetisation.
Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.
Assess whether a project is ready for cloud-native deployment. Evaluates statelessness, config, scalability, and produces a readiness score (0-12). Use when user asks about containerization readiness, Docker/Kubernetes compatibility, deployment feasibility, whether their app can run in containers or the cloud, or wants a pre-deployment assessment. Also triggers on "/cloud-native-readiness".
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
Use when reporting progress in autonomous loop iterations. Triggers at the end of every autonomous loop iteration, when the autonomous-loop skill completes a BUILD phase, when progress reporting is needed for monitoring or exit evaluation, or when producing machine-parseable RALPH_STATUS blocks with exit signal protocol.