Owns architecture, engineering delivery, infrastructure, data platform, and internal systems. Use this for build-versus-buy calls, technology selection, architectural direction, engineering capacity and delivery risk, technical debt tradeoffs, platform and tooling decisions, or when a technical choice has business consequences that need stating in business terms. Also use to judge whether a technical plan is sound before it is committed to.
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。
cbrock84/headcount☆ 2,0592026年9月18日 更新
Designs system structure and makes architectural decisions defensible — boundaries, coupling, trade-offs, and recording why. Use this to design a new system or major component, choose between architectural options, review an existing design, decide where a boundary belongs, or document an architectural decision so it survives the people who made it.
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。
cbrock84/headcount☆ 2,0592026年9月18日 更新
Owns the security posture of the organization — architecture, program strategy, risk acceptance, incident command, and the authority to stop work that creates unacceptable exposure. Use this for a security strategy or program decision, when a technical choice creates security risk that needs a verdict, when deciding whether to accept or block a risk, when standing up a security function, or when security and delivery priorities conflict and someone has to decide.
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。
cbrock84/headcount☆ 2,0592026年9月18日 更新
Audits and improves organic search performance — technical health, site architecture, internal linking, structured data, and the content decisions that determine what can rank. Use this to run an SEO audit, diagnose why pages are not ranking or were deindexed, plan a site's URL and navigation structure, add structured data, or prioritize which SEO fixes are worth doing.
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。
cbrock84/headcount☆ 2,0592026年9月18日 更新
Writing and maintaining lat.md documentation files — structured markdown that describes a project's architecture, design decisions, and test specs. Use when creating, editing, or reviewing files in the lat.md/ directory.
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。
vercel-labs/lat.md☆ 2,0232026年10月15日 更新
Generate professional Mermaid diagrams with multiple themes, SVG/ASCII output, batch rendering, and a built-in template library for common diagram patterns. Supports flowcharts, sequence diagrams, class diagrams, state diagrams, ER diagrams, C4 architecture, and Gantt charts.
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。
openakita/openakita☆ 1,9972026年9月24日 更新
Guide AI to write Chinese novels and fiction with structured character creation, plot architecture (起承转合), dialog writing, scene description, and genre-specific techniques for 玄幻, 都市, 悬疑, and 言情 genres.
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。
openakita/openakita☆ 1,9972026年9月24日 更新
DingTalk Workspace CLI (dws) - officially open-sourced cross-platform CLI tool from DingTalk. Provides 86 commands across 12 products: Contact, Chat, Bot, Calendar, Todo, Approval, Attendance, Ding, Report, AITable, Workbench, DevDoc. Built in Go with zero-trust security architecture. Use when user wants to operate DingTalk resources.
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。
openakita/openakita☆ 1,9972026年9月24日 更新
Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.
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。
xu-xiang/everything-claude-code-zh☆ 1,9922026年3月5日 更新
Find the mistakes agents keep repeating in this repo and make each one impossible. Try architecture first, then types, then a lint whose error names the fix, then a test, and write docs last. Prove each check fails on a real past mistake. Repeat this each time the operator corrects you. Use for /correct.
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。
backnotprop/pstack☆ 1,9592026年10月15日 更新
Apply during planned rewrites and migrations with explicit phase boundaries. Converge on the target architecture; don't preserve smooth intermediate states with throwaway compatibility code.
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。
backnotprop/pstack☆ 1,9592026年10月15日 更新
Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which package owns this", "is this the right layer"). Explains subsystem architecture, runtime flow, onboarding mental models. Use why for motivation.
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。
backnotprop/pstack☆ 1,9592026年10月15日 更新
Designs QTL colocalization studies that connect eQTL, pQTL, sQTL, or related molecular QTL signals with GWAS loci. Always use this skill whenever a user wants to plan, scope, or structure a locus-level study asking whether a GWAS association and a molecular QTL association may reflect the same underlying causal signal. Covers locus definition, QTL/GWAS source architecture, ancestry and LD alignment, single-locus vs multi-locus strategy, candidate-gene prioritization, optional fine-mapping, linked MR/SMR follow-up, and functional annotation. Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, evidence hierarchy, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references. Never equate colocalization with causality proof, mediation proof, or automatic target validation. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets, repositories, consortia, or public resources.
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。
aipoch/medical-research-skills☆ 1,9592026年9月17日 更新
Designs studies for predicting treatment response or resistance in biomedical and clinical research. Always use this skill when the user needs a treatment-response or resistance prediction study blueprint rather than a prognostic biomarker protocol, diagnostic test design, causal treatment-effect estimation, or a completed manuscript. Focus on responder definition, treatment context, baseline comparability, feature integration strategy, model development logic, validation architecture, and interpretation boundaries. Do not invent response rates, cohort size, assay readiness, regimen uniformity, literature support, or validation access.
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。
aipoch/medical-research-skills☆ 1,9592026年9月17日 更新
Get a new developer up and running with the Chops codebase — prerequisites, build, architecture, and common tasks.
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。
Shpigford/chops☆ 1,9472026年8月24日 更新
Generate, extend, or audit design tokens in DTCG format with the 3-tier architecture (primitive → semantic → component). Use when the user wants a color palette, type scale, spacing/shadow/radius/motion tokens, multi-brand theming, or wants to validate token files. Covers colors, typography, spacing, shadows, borders, breakpoints, motion, gradients, opacity, blur, sizing, states, theming.
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。
plugin87/ux-ui-agent-skills☆ 1,9462026年10月11日 更新
Use when the user is designing, prototyping, or rewriting a desktop app that must run on multiple OSes (macOS + Windows, optionally Linux) AND feel indistinguishable from a native app to its users — fast launch, native windowing, native input handling, native materials. Trigger words include "cross-platform desktop", "Electron alternative", "Tauri vs native", "WebView wrapper", "near-native performance", "Raycast architecture", "WebKit/WebView2 quirks", "WKWebView", "system tray app", "global hotkey app", "launcher app". Do NOT trigger this skill for pure web apps, pure mobile apps, or for greenfield projects that have no native-feel requirement.
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。
yetone/native-feel-skill☆ 1,9312026年5月31日 更新
Design any process, idea, problem, or body of knowledge into an ICM (Interpretable Context Methodology) workspace — folder structure as agent architecture — or restructure an existing folder, repo, or vault into one. Use when the user wants to (1) turn a recurring workflow into an agent-runnable folder pipeline, (2) organize scattered notes, files, or knowledge into a library one AI agent can walk, (3) map a team or company as connected context ("context map", "second brain", "team brain", "knowledge base for AI"), (4) audit a codebase or mixed folder into a walkable edit map (objects, processes, change-impact) so later agents can change it without slurping the tree, (5) audit or restructure an existing workspace to ICM conventions, or (6) says "make this an ICM", "ICM this", "map this repo", "audit this folder", "what would a change hit", "build me a workspace", or "structure this for 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。
RinDig/icm-architect☆ 1,9292026年10月14日 更新
Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shards), modern optimizers (Muon, AdamW), mixed precision training, and training loop 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。
benchflow-ai/skillsbench☆ 1,8502026年10月13日 更新
Implement backend async and background processing patterns — event-driven architectures, message queues, async task runners, and worker pools.
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。
a5c-ai/babysitter☆ 1,8472026年9月17日 更新
Drive feature development using Outside-In TDD with Hexagonal Architecture. Design emerges through inline code, in-memory fakes, interface extraction, and deferred I/O. Use when building features, writing tests, or structuring backend services. Triggers on: TDD, outside-in, hexagonal, ports and adapters, emergent design, acceptance test, component test, walking skeleton, in-memory fakes, component, contract test, adapter, fast tests, sub-second feedback. Language-agnostic (Go, Rust, Python, TypeScript, Java, C#).
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。
a5c-ai/babysitter☆ 1,8472026年9月17日 更新
Generate Bubble Tea (Go) TUI application structure with models, commands, and views using the Elm architecture.
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。
a5c-ai/babysitter☆ 1,8472026年9月17日 更新
Design technical architecture, select technology stack, and define implementation strategy from specifications and constitution constraints.
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。
a5c-ai/babysitter☆ 1,8472026年9月17日 更新
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
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。
a5c-ai/babysitter☆ 1,8472026年9月17日 更新