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Found 361 Skills
Create production-quality data visualizations including charts, dashboards, and infographics. Use when the user asks to visualize data, create charts, build dashboards, make infographics, plot statistics, or transform datasets into visual representations. Supports React/Recharts artifacts, static images (PNG/PDF via Python), and interactive HTML. Triggers include "visualize this data", "create a chart", "build a dashboard", "make a graph", "plot this", "infographic", or any request to represent data visually.
Use when performing bulk insert, update, or delete operations in Bknd. Covers createMany, updateMany, deleteMany, batch processing with progress, chunking large datasets, error handling strategies, and transaction-like patterns.
Writes Pest feature tests for Laravel HTTP controllers using repeatable controller-test patterns across web/session and API/JSON flows. Activates when creating or updating controller tests, nested resource route tests at any depth, CRUD action tests (create, destroy, edit, index, show, store, update), authorization and route-binding scope checks, validation datasets, transport-specific response assertions, and database persistence assertions.
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Implements efficient API pagination using offset, cursor, and keyset strategies for large datasets. Use when building paginated endpoints, implementing infinite scroll, or optimizing database queries for collections.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Generate realistic synthetic evaluation datasets by analyzing the user's codebase, prompts, production traces, and reference materials. Interactive, consultant-style — asks clarifying questions, proposes a plan, generates a preview for approval, then delivers a complete dataset uploaded to LangWatch. Use when user asks to generate, create, or build a dataset for evaluation, testing, or benchmarking.
Create and run LangWatch experiments for pre-deployment batch testing. Use when the user wants to test an agent against a dataset, compare prompts or models, benchmark quality, detect regressions, or add a CI quality gate. Do not use for production monitoring or guardrails.
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning traces, via VLM/LLM distillation. Use when the user wants to "create video training data", "generate video QA datasets", "build CoT reasoning traces from videos", "auto-label videos", or run the video_reasoning_annotation pipeline. Triggers include "video annotation", "video CoT", "video QA", "chain-of-thought", "video captioning pipeline", "video distillation".
Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, selecting base models from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3 usage. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
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.