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概要と使いどころ
This skill should be used when the user asks to "animate an SVG", "make a line draw itself on", "do a stroke draw-on / signature animation", "morph one shape into another", "move an element along a path", "animate an icon/logo", or "animate an SVG gradient or filter". Covers stroke-dashoffset draw-on, path morphing, motion-along-path, and animated icons/gradients/filters via CSS, SMIL, and GSAP.
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Use when solving CUMCM, MCM, ICM, or other mathematical modeling tasks that need contest problem intake, model selection, Python solving, figures, paper writing, quality audit, and packaged deliverables.
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 EZ_math_model model selection is unclear after the modeling decision tree, the problem spans multiple domains, or modeler needs several candidate approaches before writing modeling_plan.md.
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 developing or extending EZ_math_model itself from a written implementation plan, especially adding algorithms, tool skills, templates, or multi-step repo changes.
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。
Report a problem with the open-pr plugin itself, or ask for a change, on its public issue tracker. Use when a run of one of its commands left something to say. Takes no URL.
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。
Find the most valuable commercial insight and the monetization point closest to lawful revenue for a project, product, service, website, community, content account, open-source tool, or business idea. Use when a user asks how to monetize, who pays, why people do not buy, what to charge, whether to keep building, what to stop, what to do next, or asks to continue or deepen an earlier business analysis. Answer the user's actual question before asking for more information and carry the analysis forward without repeating it. When the buyer, purchased result, or delivery ability is unknown, do not select them from industry stereotypes; use explicit conditional candidates and an evidence-gathering next step. Not for investment advice, income forecasts, get-rich-quick schemes, or guaranteed results.
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 isolated git worktrees with smart directory selection and safety verification
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。
RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, iterate closing loopholes
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 onboarding and activation flows that help users reach value quickly. Use when the user mentions onboarding, getting started, first-time users, activation, aha moments, or new user flows—not when the work is only the empty-state surface itself.
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。
Add moments of joy, personality, and surprise through success states, empty states, loading moments, hover details, and celebratory polish once the fundamentals already work. Use when the user wants delight, charm, personality, or memorable micro-interactions—not when the main need is baseline cleanup or stronger visual impact.
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。
Search and order products online. Compares prices across Polish marketplaces (Allegro, Ceneo, Google Shopping), presents options in a comparison table, and assists with checkout. Handles the full flow: priority selection, research, comparison, cart, and checkout handoff. Triggers on: "order", "buy", "find product", "compare prices", "cheapest", "zamow", "kup", "znajdz produkt", "porownaj ceny", "najtaniej".
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, validate, and convert skills for the agent ecosystem. Enforces standardized structure for consistency. Enables self-evolution by creating new skills on demand, converting MCP servers and codebases to skills.
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。
Self-critique specification documents using extended thinking — surfaces hidden assumptions, contradictions, missing edge cases, and scope creep before implementation
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。
Agent task delegation patterns — spawn protocols, handoff metadata, drain gate, parallel limits, and model selection for multi-agent orchestration
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。
Verifies repository baseline test passing state and available memory/GPU resources to ensure safe mission execution.
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。
Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.
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 you need to auto-log all tool interactions into a self-organizing vector DB with instant recall. Intercepts interactions, extracts key information, generates embeddings, stores in LanceDB, auto-categorizes, and builds a retrieval index. Inspired by OpenClaw SQLite LCM + summary DAG pattern.
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。
Decision matrix for selecting multi-agent topologies (Supervisor, Swarm, Hierarchical, Conductor) with token economics, failure modes, and escalation paths
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。
This rule enforces the Don't Repeat Yourself principle to avoid code duplication and improve maintainability.
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。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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。
Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.
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。
Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.
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。
Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.
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 small interfering RNA and antisense oligonucleotide sequences against a transcript, and screen them for the failure modes specific to nucleic-acid drugs. Use this skill to tile a target transcript, apply positional and thermodynamic selection rules including duplex asymmetry and nearest-neighbour melting temperature, scan candidates for seed-region complementarity to off-target transcripts, and lay out a chemical modification pattern — gapmer architecture, 2'-O-methyl and 2'-MOE wings, locked nucleic acid, and phosphorothioate placement. Also trigger on siRNA, antisense oligonucleotide, ASO, gapmer, RNase H, seed region, duplex asymmetry, 2'-MOE, locked nucleic acid, phosphorothioate, or GalNAc conjugate.
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。