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Found 1,573 Skills
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
Design MCP resources to expose content for LLM consumption. Use when creating static or dynamic resources in xmcp.
LLM guardrails with NeMo, Guardrails AI, and OpenAI. Input/output rails, hallucination prevention, fact-checking, toxicity detection, red-teaming patterns. Use when building LLM guardrails, safety checks, or red-team workflows.
Amazon Bedrock Agents for building autonomous AI agents with foundation model orchestration, action groups, knowledge bases, and session management. Use when creating AI agents, orchestrating multi-step workflows, integrating tools with LLMs, building conversational agents, implementing RAG patterns, managing agent sessions, deploying production agents, or connecting knowledge bases to agents.
Use to run day-to-day loyalty reward catalog management and fulfillment.
Emotional reset and loop-breaking skill. Use this skill when: (1) The user expresses frustration, anger, or dissatisfaction with your responses (e.g. cursing, scolding, saying you're useless/wrong/stupid), (2) You detect you've attempted the same approach 3+ times without success, (3) You're stuck in a cycle of repeated failures on the same problem. This skill summarizes the user's overall emotional state from the conversation and fetches a reset methodology from hugllm.com (with emotion context) to help you recalibrate and approach the problem fresh.
Synthesize outputs from multiple AI models into a comprehensive, verified assessment. Use when: (1) User pastes feedback/analysis from multiple LLMs (Claude, GPT, Gemini, etc.) about code or a project, (2) User wants to consolidate model outputs into a single reliable document, (3) User needs conflicting model claims resolved against actual source code. This skill verifies model claims against the codebase, resolves contradictions with evidence, and produces a more reliable assessment than any single model.
Integrate Databuddy analytics into applications using the SDK or REST API. Use when implementing analytics tracking, feature flags, custom events, Web Vitals, error tracking, LLM observability, or querying analytics data programmatically.
Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.
Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
Build a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.