데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (9.2천건)
Validate at every layer data passes through to make bugs impossible. Use when invalid data causes failures deep in execution, requiring validation at multiple system layers.
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Evaluate JavaScript at bundle time and inline results. Use when optimizing compile-time code generation, embedding files, inlining environment variables, or executing code during the bundling process.
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Use when a user provides one baseline image and requests game animation frames, sprite sequences, attack/walk/idle/hit/death/casting motion, transparent PNG frames, a spritesheet, keyframe prompts, or consistent whole-character pose animation.
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Use when choosing between engines or models for an agent pipeline and the answer must come from measurement on your own data, not from marketing pages: speech recognition for meeting recordings, the model that turns a transcript into notes, the critic model that checks it. Runs every variant in an isolated container, measures time, cost per hour of input (USD and RUB, with price source and date) and quality (WER/CER and course terms for ASR; code checks plus a judge of a different model for LLM steps), writes results.json/csv and one static analytics page. Triggers on "бенчмарк", "сравни движки", "сколько стоит час записи", "какую модель взять для пайплайна", "benchmark the pipeline", "compare ASR engines".
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Orchestrate iterative visual style searches with branch prompts, decision graphs, feedback loops, and final direction selection.
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Use when user asks for Claude Code usage stats, weekly analytics, project activity summary, or wants to see what projects were worked on. Triggers on "аналитика", "статистика claude", "cc stats", "weekly report", "что делал"
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Enrich CRM records with firmographic and contact data, filling gaps in company and person profiles to improve segmentation, routing, and outreach quality. Use when the user requests crm data enrichment or provides relevant inputs for this workflow.
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Generate comprehensive marketing analytics reports by collecting KPIs, analyzing trends, and delivering actionable insights with attribution modeling and funnel analysis. Use when the user requests analytics reporting or provides relevant inputs for this workflow.
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Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling. Use when a dataset is new, its quality is unknown, or the user requests open-ended profiling; use data-analysis instead for a defined hypothesis or decision question.
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Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.
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Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.
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Write clear, searchable help center articles and FAQ entries based on support data, product documentation, and common customer questions. Use when the user requests knowledge base article writing or provides relevant inputs for this workflow.
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Place trusted contextual information into prompts or agent state using explicit boundaries, provenance, and templates. Use when relevant context has already been selected and must be inserted safely; use context-retrieval to find it or context-optimization to choose and order it.
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Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.
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Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.
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Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.
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Set up and manage data labeling workflows using manual annotation tools, semi-automated pipelines, active learning, and programmatic weak supervision. Use when the user requests data labeling or provides relevant inputs for this workflow.
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Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this workflow.
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Optimize machine learning model hyperparameters using grid search, random search, Bayesian optimization, and Hyperband to maximize model performance within a compute budget. Use when the user requests hyperparameter tuning or provides relevant inputs for this workflow.
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Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.
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Clean and preprocess datasets by handling missing values, removing duplicates, correcting types, resolving outliers, and enforcing validation schemas. Use when the user requests data cleaning or provides relevant inputs for this workflow.
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Turn a text prompt, a photo, or a live video clip into hand-made-looking p5.js animation — pencil handwriting that writes itself, photos repainted as brushstroke art with paint-on animation and camera moves, and short videos (iPhone Live Photos included) repainted frame-by-frame as a living painting. Trigger on: "handwritten text animation", "paint this photo", "painted video", "sketch effect", "turn my live photo into a painting", "write-on effect". Everything is 100% code: p5.brush strokes rendered headlessly, no image models.
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Turn any X (Twitter) profile into a 10.3s animated "POSTING LICENSE" card video — a fake driver's license in X's branding: the card slides in over an animated dot-matrix ground, license text types on letter-by-letter, follower / following / post counters tick up, a diagonal glimmer sweeps as the card tilts in 3D, it flips to a fine-print back with a signature write-on, then crouches and leaps out of frame. Trigger on: "X driver's license", "posting license", "make an X license for @handle", "license card video". The whole composition is a finished template — the agent only supplies profile data and renders.
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Stream call audio in real-time, fork media to external destinations, and transcribe speech live. Use for real-time analytics and AI integrations. This skill provides Go SDK examples.
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