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Found 365 Skills
UI/UX design-system intelligence - 84 styles, 192 palettes, 74 font pairings, 25 charts, 99 UX guidelines across 22 stacks (searchable dataset + CLI). Internal genjutsu module: loaded by /genjutsu:cast and /genjutsu:paint, not invoked directly.
Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup.
Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.
Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.
Build and flash the XIAO ESP32S3 Sense camera web apps in STA (router) mode: a Teachable-Machine-style dataset collector page and a live inference viewer page, reachable at http://<name>.local while every device KEEPS its internet connection. Use this skill whenever the user wants the camera web app on their normal WiFi network — home/office development, "인터넷 안 끊기게", "공유기로", "mDNS", or says "STA 모드". For router-less classroom hotspot deployments use the xiao-webcam-ap skill instead.
Build and flash the XIAO ESP32S3 Sense camera web apps in STANDALONE AP (hotspot) mode: a Teachable-Machine-style dataset collector page and a live inference viewer page served by the board itself at http://192.168.4.1. Use this skill whenever the user wants the camera web app WITHOUT a router — classroom/education deployments, demos with no WiFi, per-student boards, or says "AP 모드", "핫스팟", "공유기 없이". For router (STA) mode use the xiao-webcam-sta skill instead.
Train and deploy a TinyML model for the XIAO ESP32S3 (Sense) using the Edge Impulse REST API only — no edge-impulse-cli needed (its serialport dep fails to build on modern Node/Windows). Covers: dataset upload, impulse creation (audio MFCC / vision transfer-learning), training jobs, downloading the Arduino library, and the on-device fixes required to actually run it on the ESP32-S3. Use this skill whenever the user wants to train/retrain a model ("재훈련", "edge impulse", "TinyML 훈련", "모델 배포"), upload a dataset to Edge Impulse, or gets EI Arduino-library build/runtime errors (mel filterbank, objs.a, tensor arena, EI_MAX_OVERFLOW_BUFFER_COUNT).
Apifox API Test Cases: Query, create, update, delete, categorize and run test-case and test-data; handle test steps, assertions, variable extraction, pre/post processors, datasets, and issues such as 'CLI-created test steps not displaying in frontend'.
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.
Own rendered visuals and visual redesign from supplied content or values: plot, beautify, lay out, generate, reconstruct, and QA paper figures, visual tables, method/architecture diagrams, icons, palettes, reference-guided layout control, and editable SVG/PDF/PPTX. Use for result-table layout, color/readability improvement, visual table redesign without changing numbers, 绘图美化, 排版, 配色, architecture diagrams, GPT Image 2 generation, reference-driven composition, explicit pure SVG, and editable reconstruction. Visual beautification remains here even for experiment results. Do not choose datasets/baselines/metrics, design evidence semantics, invent content, review the paper, rewrite prose, or convert PDFs into writing exemplars.