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Found 9,759 Skills
Use this skill when migrating applications, examples, integrations, documentation, manifests, or repository code from NeMo Flow to NeMo Relay across Python, Rust, Node.js, Go, C FFI, CLI, configuration, and observability surfaces.
Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when creating new skills, editing existing skills, or verifying skills work before deployment - applies TDD to process documentation by testing with subagents before writing, iterating until bulletproof against rationalization
When a founder needs to write partnership or BD emails, craft integration pitches, or create co-marketing proposals. Activate when the user mentions partnerships, business development, integration proposals, co-marketing, channel partnerships, or strategic alliances.
Guidance for detection engineering in Microsoft Sentinel — building, testing, deploying, and maintaining analytics rules, hunting queries, and SOAR automation. Covers the Content Hub solution model, MITRE ATT&CK mapping, scheduled vs near-real-time (NRT) vs Fusion vs anomalies analytics, KQL detection patterns (joins, summarize, bin, materialize), entity mapping and incident enrichment, custom detections from Defender XDR vs Sentinel-only, automation rules, playbooks (Logic Apps), watchlists, threat intel matching, content as code with Azure DevOps / GitHub repositories integration, and detection lifecycle (validate → tune → version). WHEN: Sentinel analytics rule, KQL detection, MITRE mapping, Sentinel content hub, scheduled analytics, NRT rule, hunting query, Sentinel automation rule, Logic App playbook, custom detection, repositories Sentinel CI/CD, detection-as-code, watchlist, threat intel matching analytics, fusion alerts, anomalies, incident enrichment, entity mapping. DO NOT USE for Sentinel architecture/onboarding (use sentinel), Defender XDR custom detections only (overlap—use the side that owns the data), or generic KQL training.
Guide for Unreal Engine 5.x PCG (Procedural Content Generation) Python integration. Covers the PCGPythonInterop plugin, the Execute Python Script node, PCG Python API (PCGComponent, PCGBlueprintElement, PCGSpatialData, PCGPointData), editor automation, custom PCG nodes via Python, and known limitations. Use when the user asks about PCG Python, PCGPythonInterop, Execute Python Script node, Python scripting for procedural generation, automating PCG graphs with Python, or creating custom PCG nodes with Python/Blueprint.
Use test-driven development for behavior-changing feature or fix work, and whenever the user mentions TDD, test-first, red-green-refactor, tracer bullets, integration tests, or public-interface behavior tests. Skip for docs-only, path-only rename, formatting-only, or purely mechanical chores unless explicitly requested.
Turn a settled business-logic spec (from `feature-discovery` or a freshly-described feature in chat) into a detailed, code-grounded implementation plan saved as `docs/<feature-slug>-plan.md`, ready for `plan-task-breakdown` to convert into checkboxes. Verifies feasibility against the actual codebase, names the specific files/types/actions/components to touch, identifies test cases at every layer that fits (unit, integration, and — for user-visible flows — a Playwright happy-path e2e test), and asks the user clarifying questions for implementation-level ambiguities only — never re-litigates business logic. Use this skill whenever a feature's *what* is clear and the next move is mapping it to *how*. Trigger on phrases like "make a plan", "write the implementation plan", "plan this out", "design the build", "turn this spec into a plan", "how would we implement this", "scope out how to build X", "draft a plan for <feature>", or whenever the conversation just produced a business spec and the natural next step is implementation design — even if the user doesn't say "implementation plan" explicitly.
POD integration — Printful, Printify, mockups, niche selection, margin optimization
Professor's one-time clinic setup — practice areas, jurisdiction, supervision style (formal review queue / configurable flags / lighter-touch), and handbook/rules upload. Writes CLAUDE.md so every other skill and every student who runs /ramp reads from the same clinic context. Use on fresh install, when CLAUDE.md has placeholders, when re-doing setup with --redo, or when re-checking integrations with --check-integrations.
Secure environment variable management with Varlock. Use when handling secrets, API keys, credentials, or any sensitive configuration. Ensures secrets are never exposed in terminal, logs, or LLM context. Provides guidance around integrating varlock into a project, reading/editing .env.schema and other .env files, using the varlock CLI, adding plugins and framework integrations. Trigger phrases include "environment variable", "env var", "secrets", ".env", "API key", "credentials", "sensitive", "varlock"
🇺🇸 GitHub README Writing System — Craft a README that converts visitors to stars in <3 seconds. Proven structure from AFFiNE's 0→60K star journey: tagline engineering, first-screen law, section-by-section copywriting guide, Claude Code integration section, anti-patterns, and a pre-publish checklist. Use when you need to write or rewrite a specific README file. 🇨🇳 GitHub README 写作系统 — 打造 3 秒内把访客转化为 star 的 README。来自 AFFiNE 0→60K star 实战：tagline 工程、首屏法则、逐节文案指南、Claude Code 集成板块、反模式、发布前自检清单。需要写或改一个具体 README 文件时使用。 🇯🇵 GitHub README 作成システム — 3秒以内にビジターをスターに変えるREADMEを作る。AFFiNE 0→60Kスター実績から: タグライン設計、ファーストスクリーン法則、セクション別ライティングガイド、Claude Codeインテグレーション、アンチパターン、公開前チェックリスト。 🇰🇷 GitHub README 작성 시스템 — 3초 안에 방문자를 스타로 전환하는 README 작성법. AFFiNE 0→60K 스타 실전: 태그라인 설계, 첫 화면 법칙, 섹션별 카피라이팅 가이드, Claude Code 통합 섹션, 안티패턴, 게시 전 체크리스트. Triggers: "write README" | "README template" | "GitHub README" | "project description" | "tagline" | "open source README" | "README structure" | "README review" | "fix README" | "rewrite README" | "README写作" | "写README" | "README模板" | "项目介绍" | "开源项目文案" | "改README" | "README检查" | "README 구조" | "README 작성" | "README 검토" | "READMEの書き方" | "READMEレビュー"
Refactor PyTorch code to improve maintainability, readability, and adherence to best practices. Identifies and fixes DRY violations, long functions, deep nesting, SRP violations, and opportunities for modular components. Applies PyTorch 2.x patterns including torch.compile optimization, Automatic Mixed Precision (AMP), optimized DataLoader configuration, modular nn.Module design, gradient checkpointing, CUDA memory management, PyTorch Lightning integration, custom Dataset classes, model factory patterns, weight initialization, and reproducibility patterns.