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Found 1,310 Skills
Install and configure LLMem for an agent harness. Handles CLI install, plugin deployment, skill registration, and provider setup. Triggers on: "install llmem", "set up memory", "configure memory", "add llmem to harness", "memory setup".
AI autonomous research agent for LLM training optimization using opencode as the agent. The agent autonomously modifies train.py, runs experiments, evaluates val_bpb, and iterates to find the best model. Use when: "run autoresearch", "start experiment", "train model", "autonomous research", "optimize LLM training".
Add real-time voice conversations to a custom LLM, OpenClaw, or similar agent runtime with ElevenLabs Speech Engine. Use when building Speech Engine servers, WebSocket handlers, WebRTC browser clients, conversation token endpoints, interruption-aware streaming responses, or voice-enabled chat agents that connect a developer-owned LLM to ElevenLabs speech-to-text and text-to-speech.
Use when writing or editing a system prompt for any LLM API or SDK (any code passing a `system=` / `system` role parameter, or a `.txt`/`.md` file holding such a prompt). Applies prompt-engineering and prompt-caching best practices.
Initialize, diagnose, or migrate a project into the LLM wiki pattern with AGENTS/CLAUDE instructions, QMD MCP wiring, Claude/Codex/OpenCode hooks/plugins, guardrails, and QMD doctor checks. Use when the user asks to set up wiki infrastructure, check if a project needs migration, install wiki hooks, or validate QMD.
Run an autonomous Humanize-governed SGLang SOTA performance loop for one LLM model: first perform the fixed fair SGLang/vLLM/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches SGLang code, optionally uses ncu-report-skill for kernel evidence, and revalidates until SGLang matches or beats the best observed framework under the same workload and SLA.
Write, push, run, publish, and manage Kaggle Benchmark tasks using the kaggle CLI and the kaggle-benchmarks Python SDK. Use when the user wants to create or push a benchmark task (optionally with attached Kaggle datasets), run benchmarks against LLM models, check task/run status, stream or fetch execution logs, download results and source notebooks, publish a task to make it public, or troubleshoot benchmark workflows.
Router skill for LLMQuant investor-lens workflows. Use when the user wants an investor-style reasoning overlay grounded in LLMQuant Data evidence.
Router skill for LLMQuant crypto workflows. Use when the user needs crypto market regime analysis, token research, perpetual funding, basis, leverage, liquidity, or cross-asset crypto context.
Full three-perspective audit of an existing website from one URL — design (tensions + concrete improvement opportunities), SEO/technical, and LLM/AI-search visibility — plus Core Web Vitals, synthesized into a scored, evidence-bound report. Use when the user asks to "audit this site", "site audit", "design audit", "SEO audit", "why is my site underperforming", "LLM visibility", "how does my site look to AI", or invokes /stardust:audit <url>.
Design MCP resources to expose content for LLM consumption. Use when creating static or dynamic resources in xmcp.
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows