データ分析、機械学習、LLMアプリ、プロンプト設計（9,024 件）
Apply sql optimization patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql optimization patterns or its strengths fit the task.
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。
Apply statistical power in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statistical power or its strengths fit the task.
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。
Analyze estimated website traffic, engagement, rankings, channel mix, and geographic distribution through SandBase. Use for domain benchmarking, acquisition-channel analysis, or traffic trend research; do not present third-party estimates as first-party analytics.
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。
Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses hugging face datasets or its strengths fit the task.
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。
Plan, implement, or review ml pipeline work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests ml pipeline work.
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。
Apply data visualization in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data visualization or its strengths fit the task.
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。
Apply data context extractor in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data context extractor or its strengths fit the task.
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。
Use when designing, refactoring, or debugging Formax system prompt assembly, system-reminder injection, or skills/deferred-tool prompt exposure semantics; mandatory for any change involving FORMAX_DEFERRED_TOOL_EXPOSURE and request-payload parity checks.
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Structured persuasion for tech leads, PMs, and founders—not activity logs. Five scenarios (kickoff, status update, wrap-up, investor pitch, solution selling) on one 5-part framework (Hook→Context→Proposal→Evidence→Ask). AI prompts for missing materials and audience context; pre-submit checklist. Claude Code plugin; Cursor, Codex, and chat via prompts.
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Use when a city is setting up its devopsdays event for a new year and needs its data file, pages, redirect, and asset directories created from scratch.
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Use when a devopsdays event has sold or confirmed a sponsorship and the sponsor needs to appear on the event's site - covers both sponsors already in the shared directory and brand-new ones needing a logo and data file.
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Internal — for Boundless team members only. Query Boundless broker telemetry tables on Redshift for prod/staging operational data. Use when the user asks about broker health, request evaluations, request completions, proving times, skip rates, telemetry data, or wants to run SQL against the telemetry database on live networks. Also covers historical telemetry through 2026-04-24 stored as Parquet archives in S3 (queried via DuckDB). Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.
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Create evidence-aware, interactive visual explainers as self-contained offline HTML. Use when a user asks to explain a concept, repository module, engineering tradeoff, or incident with a diagram, walkthrough, ELI5 treatment, data/request trace, failure view, or teach-back. Do not use for a plain short answer, ordinary prose rewrite, generic website implementation, or unsupported claims presented as facts.
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CesiumJS time, properties, and animation - Clock, JulianDate, TimeInterval, Property, SampledProperty, CallbackProperty, PathMode, interval and sampled path materials, interpolation, splines, CZML temporal data. Use when making entity attributes or path materials time-dynamic, configuring the simulation clock, interpolating positions, or working with sampled, interval, or callback properties.
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CesiumJS core utilities and networking - Resource, Color, Event, Request, RequestScheduler, error handling, helper functions, feature detection. Use when fetching remote data, managing HTTP requests, working with colors, handling events, debugging errors, or using utility functions like defined, clone, or buildModuleUrl.
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CesiumJS entities and data sources - Entity, EntityCollection, DataSource, GeoJsonDataSource, KmlDataSource, CzmlDataSource, Graphics types, PathGraphics, PathMode, Visualizers. Use when adding points, labels, models, polygons, polylines, or time-segmented paths, loading GeoJSON/KML/CZML/GPX data, or working with the high-level Entity API.
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Design and debug Elixir persistence with Ecto. Use for schemas, changesets, queries, preloads, transactions, migrations, multi-tenancy, and data access through application contexts. Covers database query performance and connection pools.
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Write and refactor idiomatic Elixir functions, modules, and data structures. Use for pattern matching, control flow, error handling, protocols, behaviours, and deciding whether a process is needed. Use otp for process design and supervision.
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Build and debug Phoenix web interfaces and HTTP endpoints. Use for LiveView lifecycle and data loading, components, forms, routes, controllers, Plug, channels, and PubSub. Use ecto for changesets, queries, and persistence behind those interfaces.
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Use when prompting a learner to complete hands-on MATLAB coding exercises, guided practice, debugging drills, code tracing, small MATLAB projects, or MATLAB-script assessment during tutoring. Use when the tutor should create a complete runnable MATLAB script, execute it through MATLAB tools, compare the produced outputs with expected outputs, and evaluate MATLAB programming style.
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。
Use when an instructor asks for a setup guide, adoption guide, planning guide, implementation checklist, prerequisite check, MATLAB Course Designer course structure plan, Common Cartridge workflow, Simulink starter and solution model planning, LMS import plan, or starter prompt for creating a MATLAB and Simulink enabled course with the matlab-generate-course skill.
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Add fast, local, typed decisions to any project with Laya, an open-source non-generative decision model (pip install laya). It classifies, routes, scores and answers yes/no questions about a piece of text, returning calibrated probabilities in roughly 20-35 ms on a laptop GPU, with no LLM call and no data leaving the machine. Use this skill whenever the user wants to classify or route text, triage tickets or emails, detect spam, phishing, toxicity or intent, put a guardrail in front of an agent or LLM, score something against a rubric, or replace an LLM-based classifier to cut latency or cost. Also use it when they mention Laya, Jev, a "System 1 model", "typed decisions" or a "decision model", even if they never name Laya.
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Craft verifiable, tight goal prompts for AI coding agents (Claude Code /goal, Codex Automations, Pi), either from a standalone task interview or from an already-approved to-goal handoff. Use when the user says "给我一个 goal 提示词", "帮我写个 goal", "设置一个自动化任务", "我想让 agent 自动做 X", or asks to turn a task into a self-running loop. Ensures every goal has a checkable completion criterion so the agent knows when it's done.
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Start long-lived provider LLM teammates as native Claude Code agent-team members (the native Agent tool with a ccf-<provider> type) that you message with SendMessage — multi-turn, collaborative, watchable in tmux panes. Use for sustained parallel build/work ("start 3 glm teammates", N teammates on N files), or when you need a collaborator you message across turns, from a terminal `claude` session. NOT a fire-and-forget one-shot or a flat batch of independent prompts — that is /cc-fleet:subagent. NOT a scripted multi-phase run — that is /cc-fleet:workflow.
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。