3,148 件 ・ 関連度順
概要と使いどころ
Subscribe to a skill event. Adds a subscription that injects context before or after a skill runs. Supports optional conditions at both insert-level (inherited by all subscriptions) and subscription-level (AND-stacked). Conditions include fileExists, gitBranch, envSet, envEquals, fileContains (with optional regex).
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 evaluating stocks through Deng Xiaofeng's cycle-and-industry lens: deep industry research, cycle position awareness, supply-demand dynamics, and buying leaders at cycle bottoms.
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 evaluating US stocks through Serenity / @aleabitoreddit's AI and semiconductor supply-chain lens: upstream chokepoints, photonics/CPO bottlenecks, hyperscaler capex, institutional rotation, dilution risk, and thesis validation.
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。
Emulated Slack API for local development and testing. Use when the user needs to interact with Slack API endpoints locally, test Slack integrations, emulate channels/messages/users/views, set up Slack OAuth flows, test incoming webhooks, or work with the Slack Web API without hitting the real Slack API. Triggers include "Slack API", "emulate Slack", "mock Slack", "test Slack OAuth", "Slack bot", "Slack views", "incoming webhook", "local Slack", or any task requiring a local Slack API.
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 this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.
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。
Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.
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 this skill any time a .xlsx file is involved -- as input, output, or both. This includes: creating spreadsheets, financial models, dashboards, or trackers; reading, parsing, or extracting data from any .xlsx file; editing, modifying, or updating existing workbooks; working with formulas, charts, pivot tables, or templates; importing CSV/TSV data into Excel format. Trigger whenever the user mentions 'spreadsheet', 'workbook', 'Excel', 'financial model', 'tracker', 'dashboard', or references a .xlsx/.csv filename.
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 this skill any time a .docx file is involved -- as input, output, or both. This includes: creating Word documents, reports, letters, memos, or proposals; reading, parsing, or extracting text from any .docx file; editing, modifying, or updating existing documents; working with templates, tracked changes, comments, headers/footers, or tables of contents. Trigger whenever the user mentions 'Word doc', 'document', 'report', 'letter', 'memo', or references a .docx filename.
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 this skill any time a .pptx file is involved -- as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file; editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions 'deck', 'slides', 'presentation', 'pitch', or references a .pptx filename.
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。
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
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。
End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI IWR/AWR). Use when Claude needs to parse interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, design HR/BR bandpass filters, pick a peak frequency via PSD, reject the HR second harmonic that often dominates the fundamental, or handle respiration-harmonic leakage on slow breathers. Not for pulse/UWB range gating, MIMO beamforming, Doppler-only gesture radar, arrhythmia detection, or multi-subject source separation.
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。
Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.
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。
Reference for paired-reversal handling on GL batch tapes: an RV row whose ref-trace points at an earlier row cancels BOTH legs (the RV and the row it references) — but ONLY when the two value-dates fall within the shop's reversal settlement window; an RV that references a too-old posting is NOT a cancellation and posts one-sided. Useful when the posting tape carries a reference-trace column alongside reversal codes and the running balance is off by twice the reversal magnitude, or by a whole leg on stale references.
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。
Reference for the EBCDIC "overpunch" / zoned-decimal sign convention where the units position of a numeric field is replaced with a letter that encodes both a digit and a sign. Useful when reading mainframe-style fixed-length tapes whose amount fields appear as digits followed by a letter (e.g. "0000000000123D" or "0000000000045M").
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。
Profile code performance and identify bottlenecks
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。
Prepares meeting materials by gathering context from Notion, enriching with Claude research, and creating both an internal pre-read and external agenda saved to Notion. Helps you arrive prepared with comprehensive background and structured meeting docs.
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。
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
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。
You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur
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 distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
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。
Elite AI-powered customer support specialist mastering conversational AI, automated ticketing, sentiment analysis, and omnichannel support experiences. Integrates modern support tools, chatbot platforms, and CX optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive customer experience management.
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。
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures. Masters SQL/NoSQL/TimeSeries database selection, normalization strategies, migration planning, and performance-first design. Handles both greenfield architectures and re-architecture of existing systems. Use PROACTIVELY for database architecture, technology selection, or data modeling decisions.
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。
Generate comprehensive C4 architecture documentation for an existing repository/codebase using a bottom-up analysis approach.
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。
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
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。
Für Beweismittel-Katalog: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Beweislast- und Substantiierungsmatrix.
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。