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Found 1,310 Skills
Design WhatsApp LLM chatbots for East African markets: conversation flows, social presence principles, trust-building, local language registers, and human escalation protocols. Invoke when a client wants to automate WhatsApp customer service, sales enquiries, or support using AI.
Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or audit such a base, or initialize a new one. Trigger on phrases like "add this to my wiki", "ingest this paper", "compile this into the knowledge base", "what does my wiki say about X", "lint the wiki", "build a knowledge base from these documents", "research notes", "second brain", "personal knowledge base", or any reference to LLM Wiki / OmegaWiki. Trigger even when the user does not say "wiki" — if they are accumulating sources over time and want them organized, this applies. The skill scales — sharded indexes, atomic pages, YAML frontmatter, and a bundled search script keep the wiki from becoming a context bottleneck at hundreds or thousands of pages.
Grafana Cloud AI and ML features — Grafana Assistant (natural language queries, dashboard generation, incident investigations), Dynamic Alerting (ML forecasting and outlier detection), Sift (automated root cause analysis with 8 analysis types), Knowledge Graph (entity discovery and RCA Workbench), and the LLM Plugin (OpenAI/Anthropic/Azure integration). Use when setting up AI-powered alerting, using natural language to query metrics/logs, automating incident investigation, or integrating LLMs with Grafana panels and workflows.
Generative Engine Optimization review: evaluate your content's visibility to AI-powered search engines — citation-worthiness, content structure, authority signals, llms.txt, entity clarity, and AI retrieval readiness.
Return public original model architecture diagrams for user-specified LLM, VLM, MoE, diffusion, OCR, and SGLang/sgl-cookbook model families. Use when the user asks for a model structure chart, architecture diagram, or rendered image link for a specific model such as DeepSeek, GLM, Qwen, Kimi, MiniMax, Step, Hunyuan, or Qwen3-VL.
Run Claude Code CLI, VS Code, or JetBrains ACP through a local proxy that routes to NVIDIA NIM, Kimi, OpenRouter, DeepSeek, or local LLMs
Local proxy that lets OpenAI Codex CLI/desktop talk to MiMo, DeepSeek, and other LLMs via Responses API translation
Build and maintain a persistent markdown wiki that an LLM updates on the user's behalf, usually inside an Obsidian vault or git-tracked notes repo. Use when raw sources such as web articles, papers, meeting notes, transcripts, screenshots, or past analyses need to be turned into an interlinked knowledge base with immutable source files, LLM-written wiki pages, `index.md`, `log.md`, schema rules in `AGENTS.md` or `CLAUDE.md`, source summaries, query notes, and recurring lint passes. Triggers on: llm-wiki, personal wiki, obsidian wiki, research vault, knowledge base, source ingest, persistent notes, wiki maintenance, source summaries, query filing.
Use whenever the user mentions LLM prompt/prefix cache misses, cached_tokens=0, cache_read_input_tokens/cache_creation_input_tokens, prompt_cache_key, cache_control/cachePoint placement, stable prefixes, tool/schema stability, TTFT/prefill latency, OpenAI/Claude/Bedrock/OpenRouter routing, vLLM/SGLang KV reuse, or LLM cost/speed regressions on repeated long prompts. Use when reviewing LLM request shape changes: prompt text, message order, request builders, tools, schemas, response_format, provider API surface, model/router settings, agent loop structure, context compaction, or inference deployment. Use for speeding up agents only when prompt-cache stability, TTFT, or cache cost is central. Do not use for generic prompt writing, generic RAG design, token counting, or non-LLM performance.
Deploy and operate SecurityClaw, an autonomous SOC agent with RAG-based threat detection, LLM-powered anomaly analysis, and skill-based security automation
Deploy and use an LLM-powered public opinion analytics assistant that crawls 26 hot lists from 15 platforms, performs sentiment analysis, topic clustering, and multi-channel alerting