291 件 ・ 関連度順
概要と使いどころ
Build, edit, redesign, render, and verify content-led, structurally diverse, clean, editable, reproducible PowerPoint `.pptx` decks. Use for scientific, lab, clinical, consulting, board, investor, editorial, policy, and operational presentations where alignment, readability, contrast, and balanced density matter.
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。
Audit existing model training code — find reproducibility issues, data leakage, and missing best practices. Use when asked to "audit our training code", "is our training reproducible", or "check for training data leakage".
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。
Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for analysis.
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。
Maximum control over AI image generation — write structured VGL (Visual Generation Language) JSON that explicitly controls every visual attribute. Define exact object placement, lighting direction, camera angle, lens focal length, composition, color scheme, and artistic style as deterministic JSON instead of ambiguous natural language. Use this skill when you need reproducible image generation, precise control over scene composition, or want to convert a natural language image request into a structured JSON schema for Bria FIBO models. Triggers on requests for structured prompts, controllable generation, VGL JSON, deterministic image descriptions, or Bria/FIBO structured_prompt format.
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。
Design and format publication-quality figures. Use for chart choice, color, scales, legends, captions, accessibility, and reproducible figure workflows.
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。
Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).
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。
Icarus Figures produces publication-grade figures for scientific papers — reproducible matplotlib/seaborn data figures and original TikZ method/framework/architecture figures, held to a four-axis quality bar (depth · elegance · unimpeachable · visible-gap) and exported as editable PDF+PNG+SVG with a runnable critique gate. Use this whenever a figure is destined for a paper, journal, conference submission, thesis, or report — making a plot/chart publication- or journal-ready, building a method/architecture/framework diagram or 技术路线图 (the hero figure a paper opens with), a schematic or workflow figure, or cleaning up a matplotlib/seaborn/TikZ figure that currently looks like a homework plot. Trigger even when the user never says "publication": if the figure goes into a written scientific document, this skill applies.
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 Retentioneering's Python toolkit, MCP server, and agent skills to run reproducible customer-journey and event-log analytics that agents can validate and cross-check.
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 MCAF developer-experience guidance for onboarding, F5 contract, cross-platform tasks, local inner loop, and reproducible setup. Use when the repo is hard to run, debug, test, or onboard into.
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。
ALIGN-video for Claude Code with independent Codex adversarial review. Create or revise a video through agent-authored code, rendering, editing and fresh-context audiovisual review, without image, video or music generation models. Use for reference-led animation, procedural films, motion design, explainers, construction replays, and continuing an existing coded-video project. Carries the work from reference study and animatic through a finished, reproducible film; supports explicitly requested self-review when appropriate.
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。
ALIGN-video for Codex. Create or revise a video through agent-authored code, rendering, editing and fresh-context audiovisual review, without image, video or music generation models. Use for reference-led animation, procedural films, motion design, explainers, construction replays, and continuing an existing coded-video project. Carries the work from reference study and animatic through a finished, reproducible film; supports explicitly requested self-review when appropriate.
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。
Implement and execute approved research Experiment, Tuning, or Design Cards with reproducible logs, metrics, environment, and artifacts. Use for bounded coding, debugging, and runs; not for changing claims, priorities, budgets, or the Research Spine.
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。
Capture reproducible screenshots from the currently booted iOS Simulator and turn them into three App Store marketing cards with validated dimensions and concise copy. Use after a successful Simulator build, during the two-minute proof lane, or when replacing prototype images with real app captures.
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。
Manage R package dependencies using renv for reproducible environments. Covers initialization, snapshot/restore workflow, troubleshooting common issues, and CI/CD integration. Use when initializing dependency management for a new R project, adding or updating packages, restoring an environment on a new machine, troubleshooting restore failures, or integrating renv with CI/CD pipelines.
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。
Format a Quarto or R Markdown report following APA 7th edition style. Covers apaquarto/papaja packages, title page, abstracts, citations, tables, figures, and reference formatting. Use when writing an academic paper in APA format, creating a psychology or social science research report, generating reproducible manuscripts with embedded analysis, or preparing a thesis or dissertation chapter.
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。
Create a Dockerfile for R projects using rocker base images. Covers system dependency installation, R package installation, renv integration, and optimized layer ordering for fast rebuilds. Use when containerizing an R application or analysis, creating reproducible R environments, deploying R-based services (Shiny, Plumber, MCP server), or setting up consistent development environments across machines.
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。
Containerize an R-based MCP (Model Context Protocol) server using Docker. Covers mcptools integration, port exposure, stdio vs HTTP transport, and connecting Claude Code to the containerized server. Use when deploying an R MCP server without requiring a local R installation, creating a reproducible MCP server environment, running MCP servers alongside other containerized services, or distributing an MCP server to other developers.
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。
Compose 2D graphics programmatically using SVG generation, diagram layout algorithms, image compositing, and batch processing workflows. Use when generating diagrams, flowcharts, or infographics programmatically, creating reproducible scientific figures, automating production of badges or visual assets, building custom chart types not in standard libraries, or batch generating graphics with parameter variations.
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。
Set up a Blender scene programmatically via Python (bpy) with objects, materials, lighting, camera, and environment configuration. Use when creating reproducible 3D visualization scenes, automating product or architectural rendering setup, generating multiple scene variations programmatically, building template scenes for batch rendering workflows, or integrating 3D visualization into data pipelines.
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。
Review one terminal experiment and choose the next step: merge a reproducible winner, close a useful negative or dead end, or request missing evidence.
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 agent strategies need a reproducible finite tournament under a frozen evaluation protocol. Not for open-ended or infinite matchups: use a continuous benchmark.
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。
Reproduce and repair a bounded Proto UI regression whose expected behavior and owning layer are already governed. Use this before domain implementation skills whenever work starts from a reproducible failure, including bug fixes, drift repairs, broken exports, Runtime failures, Adapter parity defects, Prototype defects, or public-projection regressions. Do not use when expected semantics or ownership is unresolved.
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。
Compile, profile, diagnose, optimize, and compare Ascend NPU operators across CANN versions and A2/A3/A5 for Ascend C, CATLASS, Triton-Ascend, TileLang-Ascend, PyPTO, and SHMEM/MC2. Use when an operator already has a runnable implementation and the user asks for msOpProf collection, bottleneck analysis, source-level tuning, or a reproducible before/after performance report. Do not use as the primary workflow for operator creation, migration, or unresolved correctness failures.
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。
MATLAB R2026a data analysis workflow for tables, timetables, statistics, fitting, optimization-assisted analysis, visualization, report artifacts, and reproducible paper figures. Use whenever the user asks MATLAB to analyze data, plot results, fit models, process CSV/Excel/MAT files, or reproduce figures.
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。