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Found 50 Skills
Guides LLM agents through large-scale coding tasks using a spec-driven, phase-by-phase methodology covering requirement definition, planning, algorithm design, and implementation with OOP principles and language-specific coding standards. Use when starting a new software project, implementing a complex feature, refactoring existing code, or when you need a disciplined step-by-step approach to any non-trivial coding task.
Claude Code skill (trtllm-agent-toolkit): implement or extend TensorRT-LLM AutoDeploy fusion transforms under transform/library/ in a TensorRT-LLM checkout. Prefer existing kernels and custom ops; use Triton only when no viable existing-kernel path exists. Use ad-graph-dump for AD_DUMP_GRAPHS_DIR workflows. Covers TRT-LLM paths, registry, default.yaml registration, graph validation, tests, and a review checklist — without prescribing profiling tools or throughput targets.
Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produce independent implementation plans, anonymize and randomize them, then judge and merge into one final plan. Use when you need a robust, bias-resistant planning workflow, structured JSON outputs, retries, and failure handling across multiple CLI agents.
Execute deterministic, event-sourced security audits using ESAA-Security's LLM-based agent architecture with 95 checks across 16 security domains
Design and build reactive, type-safe, production-grade backends on Convex. Covers schema, queries/mutations/actions, indexes, auth, file storage, scheduling, real-time multiplayer, mobile backends, and LLM/agent workflows on Convex's one-platform stack.
Comprehensive guide to Harper's Model Context Protocol (MCP) interface, covering server setup, client connection, automatic and custom tools, prompts, resources, rate limiting, durable quotas, and the security model. Triggers on tasks involving MCP servers on Harper, AI-client integration, and exposing Harper data or behavior to LLM agents.
Guide for designing effective MCP servers with agent-friendly tools. Use when creating a new MCP server, designing MCP tools, or improving existing MCP server architecture.
Build new agent skills. Use when creating diagnostic frameworks, CLI tools, or data-driven generators that follow the established skill patterns.
Tool and function calling patterns with LangChain4j. Define tools, handle function calls, and integrate with LLM agents. Use when building agentic applications that interact with tools.
Build AI phone agents with AgentPhone API. Use when the user wants to make phone calls, send/receive SMS, manage phone numbers, create voice agents, set up webhooks, or check usage — anything related to telephony, phone numbers, or voice AI.
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.
Integration patterns and best practices for adding persistent memory to LLM agents using the Letta Learning SDK