7,030 件 ・ 関連度順
Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.
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Audit and sync design tokens between the source of truth (tokens.json, Figma variables export) and the code that should consume them (Tailwind config, CSS custom properties, theme files). Finds drift -- hardcoded values, orphan tokens, out-of-date copies -- and fixes it in the right direction.
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Use before writing code against a library, SDK, API, or framework whose current version you are not certain of — especially fast-moving ones. Trigger when an import fails, a method does not exist, a config key is rejected, a deprecation warning appears, or the user says the code was written against an old version.
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Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Generate a complete, premium design system from a brand brief or an existing site — color tokens, type scale, spacing/radius/elevation, motion language, and component specs — exported as Tailwind config, CSS variables, and a usage doc. Built to look editorial and human-crafted, not template-generated. Use when starting a new product/site, unifying an inconsistent UI, or codifying a brand into reusable tokens.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).
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Databricks documentation reference via llms.txt index. Use when other skills do not cover a topic, looking up unfamiliar Databricks features, or needing authoritative docs on APIs, configurations, or platform capabilities.
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Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.
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Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
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Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
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Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concrete fixes for the serverless Spark Connect architecture and guides the full migration. Not for classic DBR version upgrades or cluster configuration changes within classic compute.
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API reference: TipKit. Tip protocol, TipView, PopoverTipView, Tips.configure, inline and popover tips.
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Continue the current local Codex conversation and coding task in an AgentKit Studio cloud Codex Sandbox using a Studio URL and short one-time pairing code. Use when the user asks to hand off, migrate, or continue a local repository in Studio, especially when a prompt contains a Studio address and pairing code. Export the complete user-visible user and assistant message history from every prior turn, including interrupted turns and progress commentary, create a temporary Studio Sandbox, restore tracked and non-ignored working-tree files plus Git metadata, inject that visible history into the cloud Thread, transfer GitHub CLI authentication separately when needed, and send one final continuation message; never copy system or developer prompts, reasoning, tool logs, SQLite state, global configuration, local skills, or SSH private keys.
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Use when a skill fails for a missing tool or the environment needs checking. Diagnoses Python, R, Node, Claude Code, Git, Zotero and configured MCP servers and prints a pass/fail table with links to the setup docs. Read-only; installs nothing.
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Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
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Converts webm/mp4 video files to optimized GIFs via ffmpeg with configurable quality settings. Use when post-processing recordings into shareable GIFs.
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Python package creation and PyPI distribution via pyproject.toml and entry points. Use when publishing a package or setting up build configuration.
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Provides templates and lifecycle patterns for storage and documentation systems. Use when organizing knowledge storage, config lifecycle, or naming conventions.
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Registers external services with health checks, central config, and unified execution. Use when integrating multiple external services needing coordination.
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Delegates tasks to the OpenAI Codex CLI (codex exec) via delegation-core. Use when delegation-core selects Codex or OpenAI credentials are already configured.
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Configures GitHub Actions CI/CD workflows for testing, linting, and deployment. Use when setting up automation for a Python, Rust, or TypeScript project.
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Scaffolds new projects with git, CI/CD workflows, pre-commit hooks, and build config. Use when starting a new Python, Rust, or TypeScript project from scratch.
日本語の概要は準備中です。原文の説明を表示しています。