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Found 13,637 Skills
Render two or more independent questions from an Agent workflow as a local interactive form, preselect recommended answers, save responses as portable JSON, and return submitted answers directly to the waiting Agent command. Use for grilling, brainstorming, requirement clarification, configuration, planning, or any workflow that needs to ask multiple questions at once. Also use the manual recovery path when the user says “已提交”, “提交好了”, or “答完了” after an active Ask UI round.
Reads a user's Link financial data — transactions, balances, and wallet sources — so agents can answer questions about spending and available source capabilities. Use when the user says "check my balance", "how much did I spend", "show my transactions", "what accounts are connected", "summarize my spending", "recent purchases", or asks about their financial activity, account balances, or linked sources.
Создаёт однозначную спецификацию результата без шагов реализации. Используй, когда пользователь хочет зафиксировать контекст задачи, обоснование правки, желаемый итог, критерии приемки и рамки, оставив технический путь исполняющему агенту. Не используй для выбора решения, составления плана реализации, непосредственной реализации или ревью изменений кода.
Prevent feature creep when building software, apps, and AI-powered products. Use this skill when planning features, reviewing scope, building MVPs, managing backlogs, or when a user says "just one more feature." Helps developers and AI agents stay focused, ship faster, and avoid bloated products.
Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.
Recognize, diagnose, and mitigate patterns of context degradation in agent systems. Use when context grows large, agent performance degrades unexpectedly, or debugging agent failures.
Production voice AI agents with sub-500ms latency. Groq LLM, Deepgram STT, Cartesia TTS, Twilio integration. No OpenAI. Use when: voice agent, phone bot, STT, TTS, Deepgram, Cartesia, Twilio, voice AI, speech to text, IVR, call center, voice latency.
Use this agent when working with prompt injection detection integration tests, including running tests, debugging failures, or adding new test samples.
Beads (bd) distributed git-backed issue tracker for AI agents: hash-based IDs, dependency graphs, worktrees, molecules, sync, GitLab/Linear/Jira. Keywords: bd, beads, issue tracker, git-backed, dependencies, molecules, worktree, sync, AI agents.
Use when creating or improving golden datasets for AI evaluation. Defines quality criteria, curation workflows, and multi-agent analysis patterns for test data.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
Comprehensive guide for building full-stack applications with Convex and TanStack Start. This skill should be used when working on projects that use Convex as the backend database with TanStack Start (React meta-framework). Covers schema design, queries, mutations, actions, authentication with Better Auth, routing, data fetching patterns, SSR, file storage, scheduling, AI agents, and frontend patterns. Use this when implementing features, debugging issues, or needing guidance on Convex + TanStack Start best practices.