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Found 1,850 Skills
Deep multi-platform intelligence analysis combining LinkedIn (profile, posts, activity), Twitter/X (tweets, engagement), Reddit (discussions, community), web presence (articles, GitHub, blogs), and company intelligence. Use when analyzing people for networking, sales, partnerships, or recruitment. Accepts LinkedIn URL or name+context. Produces comprehensive cross-platform reports with conversation strategies and strategic value assessment for AnySite.
Critically evaluate and enhance app ideas, startup concepts, and product proposals. Use when users ask to "evaluate my idea", "review this concept", "is this a good idea", "validate my startup idea", or want honest feedback on technical feasibility and market viability. Creates/updates idea.md and validate.md and always reports GitHub links to changed files.
Spec-Driven Development (SDD) methodology based on GitHub's SpecKit. Use for structured AI-assisted development with constitutional governance, phased workflows, and multi-agent coordination. Implements 7-phase process from constitution to implementation.
Standardized git commits following Conventional Commits. Supports mapping to GitHub and GitLab.
Provides Better Auth authentication integration patterns for NestJS backend and Next.js frontend with Drizzle ORM and PostgreSQL. Use when implementing authentication - Setting up Better Auth with NestJS backend, Integrating Next.js App Router frontend, Configuring Drizzle ORM schema with PostgreSQL, Implementing social login (GitHub, Google, etc.), Adding plugins (2FA, Organization, SSO, Magic Link, Passkey), Email/password authentication with session management, Creating protected routes and middleware
Convert ambiguous user requests into structured USDM requirements documents. Decomposes requirements into Requirement → Reason → Description → Specification hierarchy. Integrates with GitHub Issues, Asana, and Jira tickets as input sources. Use when: "create requirements", "write requirements document", "USDM", "decompose requirements", "requirements definition", "要件定義", "要件を整理", "要件分解".
Handles Depot CLI installation, authentication, login, project setup, organization management, and API access. Use when installing the Depot CLI, logging in with `depot login`, creating or managing Depot projects, configuring API tokens or OIDC trust relationships, setting up depot.json, managing organizations, resetting build caches, or using the Depot API/SDKs. Also use when the user asks about Depot authentication methods, token types, environment variables, or general Depot platform setup that isn't specific to container builds, GitHub Actions runners, or Depot CI.
Convert GitHub/GitLab/Gitee repositories into comprehensive OpenCode Skills using embedded LLM calls with multiple mirrors and rate limit handling
Deep EVM smart contract security audit system. Use when asked to audit a contract, find vulnerabilities, review code for security issues, or file security issues on a GitHub repo. Covers 500+ non-obvious checklist items across 19 domains via parallel sub-agents. Different from the security skill (which teaches defensive coding) — this is for systematically auditing contracts you didn't write.
Comprehensive security and safety evaluation system for agent skills (.skill files). Use when users provide GitHub URLs, website links, or .skill files for download and request security assessment, safety evaluation, or ask "is this skill safe to use." Evaluates prompt injection risks, malicious code patterns, hidden instructions, data exfiltration attempts, and provides actionable recommendations with risk scoring.
This skill is used when the user requests 'review my prompt', 'analyze my conversation history', 'diagnose my understanding level', or when it is invoked via /prompt-review. It reads past AI Agent conversation histories (Claude Code, GitHub Copilot Chat, Cline, Roo Code, Windsurf, Antigravity), estimates the user's technical understanding level, prompting patterns and AI dependency, then generates a corresponding report.
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).