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概要と使いどころ
Analyze user-provided reference images and reverse-engineer high-fidelity AI image-generation prompts. Use when the user asks to recreate, imitate, reverse-engineer, or extract prompts from photographs, illustrations, 3D renders, products, characters, landscapes, typography, logos, posters, or other visual references. Do not use for requests that only require OCR or an ordinary image description.
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Use when you need to set up Java application profiling to detect and measure performance issues — including trusted preinstalled async-profiler v4.x setup, problem-driven profiling (CPU, memory, threading, GC, I/O), interactive profiling scripts, JFR integration with Java 25 (JEP 518, JEP 520), or collecting profiling data with flamegraphs and JFR recordings. This should trigger for requests such as Improve the code with profiling; Apply Profiling; Refactor the code with profiling; Add profiling support; Collect JFR or async-profiler data for Java performance. Part of Plinth Toolkit
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Use when you need to analyze Java profiling data collected during the detection phase — including interpreting flamegraphs, memory allocation patterns, CPU hotspots, threading issues, systematic problem categorization, evidence documentation with profiling-problem-analysis and profiling-solutions markdown files, or prioritizing fixes using Impact/Effort scoring. This should trigger for requests such as Analyze JFR profile; Analyze the profile; Analyze the performance; Analyze the memory; Analyze the threading; Analyze GC logs from profiling; Prioritize Java profiling bottlenecks by impact. Part of Plinth Toolkit
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。
Extract video transcripts: yt-dlp subtitles to clean paragraphs.
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。
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
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。
Flowcharts are composed of **nodes** (geometric shapes) and **edges** (arrows or lines). The Mermaid code defines how nodes and edges are made and accommodates different arrow types, multi-directional arrows, and any linking to and from subgraphs.
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 analyzing binary structure, disassembling code, or decompiling functions. Deep static analysis via radare2 (r2) and Ghidra headless - function enumeration, cross-references (xrefs), decompilation, control flow graphs. Keywords - "disassemble", "decompile", "what does this function do", "find functions", "analyze code", "r2", "ghidra", "pdg", "afl"
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 charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
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 distinctive Photo Relic editorial artworks from user-provided photographs: preserve the real photo and pair it with a recognizable, artful abstract relic shaped like memory, modern printmaking, quiet Eastern restraint, and source-derived light. Use when the user asks to turn a photo into minimal art, photographic relic posters, abstract editorial photography, gallery-like photo posters, Douyin-ready art series covers, graphic photo abstraction, afterimage compositions, or similar visual treatments while keeping the original photograph truthful.
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。
Digital archiving with AI enrichment and entity extraction. Use when building content archives or knowledge graphs.
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。
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
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。
Database schema patterns for InsForge including social graphs, e-commerce, content publishing, and multi-tenancy with RLS policies. Use when designing data models with relationships, foreign keys, or Row Level Security.
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Track complex, multi-session work with dependency graphs using bd (beads) issue tracker. Use when work spans multiple sessions, has complex dependencies, or requires persistent context across compaction cycles. For simple single-session linear tasks, TodoWrite remains appropriate.
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Retrieve mouse phenotype data from the Jackson Laboratory Mouse Phenome Database (MPD) via its REST API. Browse 520+ projects, look up per-project measure metadata, pull strain-level means (raw or LS-mean adjusted) and per-animal values, find measures by MP/VT ontology terms, and resolve strain nomenclature or gene coordinates. Use for QTL support, cross-strain comparison, mouse model selection, and ontology-driven phenotype discovery. Use monarch-database for disease-gene-phenotype knowledge graphs; ensembl-database for mouse genome annotations.
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Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.
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Graph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.
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。
UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.
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Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting.
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Use this repo skill for Dask, the Python parallel computing library, when working with lazy task graphs, schedulers, Dask Array, Dask DataFrame, Dask Bag/bytes IO, configuration, diagnostics, CLI usage, or contributor validation.
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Use the city2graph 1.0.0 Python package to construct, validate, transform, and troubleshoot geospatial, spatial-topology, morphology, mobility, transportation, heterogeneous, and GNN-ready graphs.
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Use code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations.
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Use AutoKeras for Keras-based AutoML task APIs, custom AutoModel graphs, tuner search, and model export workflows.
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Use Google ADK Python to build agents, Workflow graphs, tools, runtime services, CLI apps, evaluations, deployments, and ADK repository changes.
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POS (Point of Sale) architecture and patterns. Use when writing, editing, exploring, debugging, fixing bugs, implementing features, or reviewing WooPos-prefixed classes or files under ui/woopos/. POS uses a different architecture than the main app — plain ViewModel (not ScopedViewModel), pure Compose (no Fragments), Compose Navigation (no nav graphs), parent-child SharedFlow event bus. Loading this skill prevents applying main-app patterns that would be wrong for POS. For analytics use `pos-analytics`, for tests use `pos-tests`.
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。