AGENT SKILLS · ミッション装備
実際に使われている Agent Skills
公開リポジトリの検証済み Skills を比較し、機能、内容、導入状況、出典を確認してから追加できます。
- 掲載範囲
- 検証済み
- Skills
- 23,248
- リポジトリ
- 3589
- 最終同期
- 2026/11/04
rohitg00write-agentmemory-skill
The house format and rules for writing or updating an agentmemory skill. Use when adding a new skill, restructuring an existing one, or reviewing a skill contribution for consistency.
- 1時間の導入数
- 4
- GitHub Stars
- 3万
- 更新日
- 10月23日
dgreenheckwebgpu-threejs-tsl
Comprehensive guide for developing WebGPU-enabled Three.js applications using TSL (Three.js Shading Language). Covers WebGPU renderer setup, TSL syntax and node materials, compute shaders, post-processing effects, and WGSL integration. Use this skill when working with Three.js WebGPU, TSL shaders, node materials, or GPU compute in Three.js.
- 1時間の導入数
- 4
- GitHub Stars
- 1257
- 更新日
- 4月10日
ericjujianzourederive
Teaches a codebase by making the user re-derive its design decisions from first principles through Socratic questions, instead of explaining the code. Works on the user's own repo or any open source repo (local path or git URL). Use when the user runs /rederive, wants to explain a project they built with AI, is preparing to talk about a project in an interview, or wants to learn how a well-known project works.
- 1時間の導入数
- 4
- GitHub Stars
- 87
- 更新日
- 10月19日
wshobsonapi-design-principles
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
- 1時間の導入数
- 4
- GitHub Stars
- 4.2万
- 更新日
- 10月19日
affaan-magentic-os
Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mcontinuous-learning
[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1: when continuous learning, session learning, or pattern extraction is requested, route to continuous-learning-v2 instead.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mcpp-testing
Use only when writing/updating/fixing C++ tests, configuring GoogleTest/CTest, diagnosing failing or flaky tests, or adding coverage/sanitizers.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mdjango-tdd
Django testing strategies with pytest-django, TDD methodology, factoryboy, mocking, coverage, and testing Django REST Framework APIs. Use when writing Django or DRF tests with pytest-django, or driving a Django feature test-first.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mhomelab-network-readiness
Readiness checklist for homelab VLAN segmentation, local DNS filtering (Pi-hole, AdGuard Home), and WireGuard-style remote access. Use when planning or reviewing home network changes — splitting a flat network into trusted, IoT, guest, or management VLANs, moving DHCP to a local resolver, or adding VPN access — before changing router, firewall, DHCP, or VPN configuration.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mhomelab-network-setup
Practical home and homelab network planning for gateways, switches, access points, IP ranges, DHCP reservations, DNS, cabling, and common beginner mistakes. Use when planning or fixing a home or homelab network — gateway, switch, AP, IP ranges, DHCP, DNS, or cabling.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mhomelab-pihole-dns
Pi-hole installation, blocklist management, DNS-over-HTTPS setup, DHCP integration, local DNS records, and troubleshooting broken DNS resolution on a home network. Use when the task explicitly involves Pi-hole — installing it, managing blocklists, configuring DoH or DHCP, adding local DNS records, or diagnosing DNS resolution with Pi-hole in the path.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mhomelab-vlan-segmentation
Segmenting home networks into VLANs for IoT, guest, trusted, and server traffic using UniFi, pfSense/OPNsense, and MikroTik — including switch trunk config, firewall rules, and wireless SSID mapping. Use when splitting a home network into IoT, guest, trusted, and server VLANs on UniFi, pfSense/OPNsense, or MikroTik.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mprompt-optimizer
Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never executes the task. Use when the user says 'optimize prompt', 'improve my prompt', 'rewrite this prompt', 'help me prompt', 优化prompt, 改进prompt, 怎么写prompt, or 帮我优化这个指令; not for requests to optimize code or performance.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mreact-performance
React and Next.js performance optimization patterns adapted from Vercel Engineering's React Best Practices (https://github.com/vercel-labs/agent-skills). Organizes 70+ rules across 8 priority categories — waterfalls, bundle size, server-side, client fetching, re-render, rendering, JS micro-perf, advanced. Use when writing, reviewing, or refactoring React/Next.js code for performance.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mcontinuous-agent-loop
Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls. Use when running an agent loop that must self-check, gate on evals, and recover from failures.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
affaan-mmotion-foundations
Motion tokens, spring presets, performance rules, device adaptation, accessibility enforcement, and SSR safety for React / Next.js using motion/react. Foundation layer — all other motion skills depend on this. Use when setting up motion tokens, spring presets, reduced-motion handling, or SSR-safe animation in React or Next.js.
- 1時間の導入数
- 4
- GitHub Stars
- 28.6万
- 更新日
- 10月20日
tradermontycanslim-screener
Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日
tradermontyearnings-calendar
This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desk
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日
tradermontyearnings-trade-analyzer
Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions.
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日
tradermontyexposure-coach
Generate a one-page Market Posture summary with net exposure ceiling, growth-vs-value bias, participation breadth, and new-entry-allowed vs cash-priority recommendation by integrating signals from breadth, regime, and flow analysis skills.
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日
tradermontymacro-regime-detector
Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states. Run when user asks about macro regime, market regime change, structural rotation, or long-term market positioning.
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日
tradermontymarket-news-analyst
This skill should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSear
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日
tradermontyportfolio-manager
Comprehensive portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account.
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日
tradermontytechnical-analyst
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs. Use this skill when the user provides chart images and requests technical analysis, trend identification, support/resistance levels, scenario planning, or probability assessments based purely on chart data without consideration of news or fundamental factors.
- 1時間の導入数
- 4
- GitHub Stars
- 3167
- 更新日
- 10月20日