1294 Claude Code skills tagged Llm. Browse all AI provider, model, or runtime-related skills in the open ClaudSkills registry — free to install, one-click via the desktop app.
Showing top 200 of 1294 skills, ranked by quality score.
Применять при переписывании текстов, сгенерированных LLM-агентами (отчеты, README, доки, письма, посты), в живой человеческий стиль. Триггеры - пользователь пишет «убери AI-стиль»,
general
Use when wiring a repo to maintained DETERMINISTIC scanner gates (SAST, dependency-CVE/SBOM, secret-history, IaC/container, mutation, fuzz) that produce ground-truth observables —
security
Keyword discovery en ideation vanuit seed keywords. Haalt suggestions, related keywords, zoekvolume en difficulty op via DataForSEO. Classificeert intent, berekent opportunity scor
growth
ATC (Air Traffic Control) — the persistent orchestrating brain of the fleet. A plain `ainb` Claude session running a generated CLAUDE.md policy, woken on an OS-timer heartbeat (def
general
Amazon Bedrock Model Customization with fine-tuning, continued pre-training, reinforcement fine-tuning (NEW 2025 - 66% accuracy gains), and distillation. Create customization jobs,
security
Prompt optimization for LLMs. Trigger when the user wants to improve a prompt, add examples, or structure instructions.
general
Call Exa Contents (POST /contents) for LLM-ready extraction from known URLs: text, highlights, summaries, links, image links, subpages, freshness-controlled crawl. Use when the age
general
Hugging Face transformer model fine-tuning and inference for intent classification
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Reproduce the Artificial Analysis (AA) language-model performance workload shapes against an OpenAI-compatible chat endpoint using NVIDIA AIPerf. Drives the three AA text shapes (1
general
Produce quantized inference weights from a BF16/FP8 base checkpoint via a post-training-quantization (PTQ) pipeline -- instead of only ever pulling NVFP4 weights pre-quantized. A c
science
Execute markdown validation with taxonomy-based classification and custom rules. Use when validating markdown compliance with LLM-facing writing standards or when generating struct
general
LLM content governance and compliance standards. Use when llm governance guidance is required.
general
LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local
engineering
LLM integration patterns for Claude, GPT, Gemini, and Ollama. Activate for AI API integration, prompt engineering, token management, extended thinking, and multi-model orchestratio
general
Helpt bij het implementeren van LLM-specifieke beveiligingscontrols voor overheidstoepassingen, gebaseerd op de OWASP LLM Top 10, BIO2, NIS2 en AVG. Biedt prompt injection detectie
security
Standard change process for the LLM Wiki engine. Every feature change follows 5 phases: Concept → Verify → Implement → Document → Visualize. Use for any new feature, pipeline chang
general
Designs and optimizes prompts for large language models including system prompts, agent signals, and few-shot examples. Use for instruction design, prompt security, chain-of-though
security
Personality modeling system based on Timothy Leary's 1985 software + The Sims traits
general
Configure multi-machine LAN mesh for swarm-llm (netllm). Use when the user asks to set up a swarm, connect multiple machines (macOS, Linux, Windows), enable LAN routing, find peers
general
RAG 시스템 품질 평가 및 개선을 위한 스킬입니다. RAGAS 기반 LLM-as-Judge 평가, 사용자 페르소나 시뮬레이션, 합성 데이터 생성, 평가 결과 저장 및 분석 기능을 제공합니다. — from LucasSantana-Dev/sharekit
general
RAG 시스템 품질 평가 및 개선을 위한 스킬입니다. RAGAS 기반 LLM-as-Judge 평가, 사용자 페르소나 시뮬레이션, 합성 데이터 생성, 평가 결과 저장 및 분석 기능을 제공합니다. — from majiayu000/claude-skill-registry
general
A multimodal LLM-based AI agent for deep spatial transcriptomics research, capable of dynamic code generation, visual reasoning, and literature retrieval.
content
Discover and use a locally-available LLM-wiki through its `wiki` CLI — from any project, not just inside the vault. Read the knowledge base to ground answers in the operator's own
general
Adds a new LLM provider to the multi-provider rotation system. Use when the user wants to add a new AI provider like OpenAI, Together, Fireworks, etc. Don't use for Groq — Groq is
general
Comprime documentos grandes para formato LLM-optimal. Mantem toda informacao em menos tokens. Para TOTVS KB, Design Library, SPECs grandes, docs de referencia. Inspirado no BMAD di
general
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/Al — from Yu
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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/Al — from ma
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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/Al — from ma
general
Zero-assumption API design review. Uses the API as a consumer first, then audits contracts, error shapes, auth model, pagination, versioning, idempotency, and rate limiting. Every
general
Lossless LLM-optimized compression of source documents. Use when the user requests to 'distill documents' or 'create a distillate'.
general
Review a PR against the top 20 Tier 2 LLM-enforceable best practices from 35 seminal software engineering books (Code Complete, Clean Code, A Philosophy of Software Design, Refacto
engineering
Run metric-driven iterative optimization loops. Define a measurable goal, build measurement scaffolding, then run parallel experiments that try many approaches, measure e — from Ev
science
**DEFAULT for ROUTING AMBIGUITY — interactive picker that surfaces top skill candidates + 2 LLM-rewritten prompt variants via AskUserQuestion with previews, then dispatches the cho
general
Deep Corefall BP-LEVEL closure review (BP0..BP12) with T-CAPTURE evidence, grading.json LLM-graded verdicts, Self-Play Validation Matrix, AI-Agent Self-Test Report, Universal Enhan
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Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop
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Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection, quality-focused prompting, and meta-judge → LLM-as-a-judge verific
general
Execute complex tasks through sequential sub-agent orchestration with intelligent model selection, meta-judge → LLM-as-a-judge verification — from NeoLabHQ/context-engineering-kit
general
This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inf — from st
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This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inf — from ma
general
Extract clean markdown from any URL, including JavaScript-rendered SPAs. Use this skill whenever the user provides a URL and wants its content, says "scrape", "grab", "fetch", "pul
engineering
Run metric-driven iterative optimization loops. Define a measurable goal, build measurement scaffolding, then run parallel experiments that try many approaches, measure e — from wa
science
Run `@metaharness/darwin security bench` (upstream "Darwin Shield" / ADR-155) — evolves a champion security-detection harness against a 10-vuln / 9-decoy corpus and grades it on TP
security
Use when transforming existing code into agent-consumable surfaces — CLI (npm-publishable), MCP server (stdio/SSE/Streamable HTTP), and a companion skill. Triggers on 'agentize', '
tools
Cross-model benchmark for the Karvey method. Side-by-side comparison of models (e.g. Claude vs GPT vs Gemini) on a skill or task — latency, tokens, cost, and optional LLM-judged qu
tools
Run metric-driven iterative optimization loops -- define a measurable goal, run parallel experiments, measure each against hard gates or LLM-as-judge scores, keep improvements, and
science
LLM token logprobs and calibration. Per-decision confidence, ECE, Brier, reliability diagrams, low-confidence triage.
general
**DEFAULT for LLM/agent eval design — dispatches evaluator for AI/LLM-specific evaluation design (offline + online metrics, groundedness, hallucination, drift, cost, latency).**
general
Evaluate LLM models for cost/performance ratio. Fetches current pricing and recommends optimal model for your use case. Use during project init or when optimizing costs.
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CI hook that refuses to ship if prompt-eval golden set regresses past threshold or prompt-injection-test fails on HIGH severity
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Selects the optimal LLM model and provider for each task based on complexity, cost budget, and capability requirements. Routes cheap tasks to Haiku/GPT-4o-mini and complex tasks to
general
Reviews LLM-powered applications against the OWASP Top 10 for Large Language Model Applications (2025 edition). Auto-invoked when reviewing code that integrates LLM APIs, builds RA
security
Generate an llms.txt file for any project or website following the llmstxt.org specification. Use when asked to create llms.txt, generate LLM-friendly documentation, make a project
general
Wrap an MCP server as a yakOS agent so tool-side and LLM-side specialists share the same dispatch surface
general
Foundation-portable, source-agnostic transcript ingestor. Consumes a transcript file path (Otter VTT, Word, Zoom, generic LLM-export, or Granola JSON) and emits a structured meetin
content
Execute complex tasks through sequential sub-agent orchestration with intelligent model selection, meta-judge → LLM-as-a-judge verification — from TuYv/ccpm
engineering
Brutally honest incident postmortem. Reconstructs what happened from git, deploys, and logs — not from memory. Questions the process that allowed this to ship, not just the code th
general
**DEFAULT for PRD FOR AN AI/LLM/AGENT FEATURE — model selection rationale, eval plan, safety boundaries, cost envelope, failure-mode map: PRD covering AI-specific sections (model s
product
Brutally honest engineering retrospective. Analyzes git history for what actually happened — not what people think happened. Gives blunt, specific, data-backed feedback per contrib
general
Turn unstructured text into validated, structured variables at corpus scale with LLMs: codebook design, dev/gold-set construction, DSPy prompt optimization, a hard reliability gate
general
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says \"modal — from Yu
engineering
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says \"modal — from ma
engineering
Maintain a ranked list of N artifacts (drafts, designs, code variants, research reports, ...) by comparing each new candidate against the current top and bottom of the list, using
science
Build and maintain LLM-curated knowledge wikis for prose domains (legal/regulatory tracking, scientific literature, market intelligence, product taxonomies, personal research notes
science
Audit a repo against the golden-stack canon in llm-wiki-research. Reads the Audit Checklist tables in ideal-tech-setup.md, runs each check against the target repo (file existence,
science
Update the golden-stack docs in llm-wiki-research when a tech decision is made. Appends rows to the decision tree, audit checklist, or AI/agent layers; replaces tools; opens the ta
science
Reviews AI/ML model supply chains for security risks including model provenance verification, training data lineage, fine-tuning pipeline integrity, inference dependency review, an
security
Universal document consistency auditor. Runs deterministic drift checks across docs, code, tests, and CI, resolves conflicts through a per-fact-class authority registry (sot-map.ya
engineering
Import an existing Obsidian vault, markdown folder, or git repo as an llm-wiki vault. Moves content into vaults/, adds missing structure (index, log, CLAUDE.md, frontmatter). Use w
general
Build a Visual Data Dictionary — an interactive qsv viz smart dashboard driven by an LLM-inferred JSON Schema data dictionary, with the dictionary browsable beside the charts. Use
general
Design, deploy, and tune vLLM v0.18.2 inference serving on EKS with PagedAttention v2, Multi-LoRA, FP8 KV Cache, Chunked Prefill, and Continuous Batching. Produces Helm values.yaml
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Use this skill whenever the user needs to perform VMware compliance auditing, baseline checking, or drift detection on vSphere/ESXi/NSX environments. Directly handles: CIS / vSpher
engineering
Fill in the per-paper TODO sections (Problem/Method/Key Results/Limitations/Reusable Ingredients/...) of research-wiki/papers/<slug>.md pages that /research-lit, /arxiv, /alphaxiv,
science
Wake up, orient, and warm the context with foundational MOOLLM knowledge
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Cannabis-specialized bartender — strains, terpenes, edibles, rolling — from majiayu000/claude-skill-registry
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Portable tokens of capability, identity, and access — from majiayu000/claude-skill-registry
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LLM-driven multi-agent framework for automated single-cell analysis.
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Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-drive
engineering
Structured multi-perspective deliberation through adversarial dialogue — from majiayu000/claude-skill-registry
general
Safe referencing of real people's traditions without impersonation — from majiayu000/claude-skill-registry
content
I mine pixels for atoms. Reality is just compressed resources. — from majiayu000/claude-skill-registry
general
LLM-based zero-shot and few-shot classification for flexible intent detection
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Generate or audit an `/llms.txt` file at the site root that makes the site legible to LLMs and AI answer engines at inference time, following the llms.txt proposal (Jeremy Howard,
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Identity layers for characters — WHO they are vs WHAT they do
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Be conservative in what you send, liberal in what you accept.
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Structured research with sources, findings, and decisions.
science
Measurable criteria translating qualitative debate to quantitative scores
general
Missing state triggers repair, not failure.
general
LLM-powered semantic analysis of code diffs to detect business-logic trojans
general
Structure Python so LLMs can understand it in 50 lines. — from majiayu000/claude-skill-registry
engineering
YAML is sheet music. The LLM is the jazz musician. Comments are soul.
general
扮演 AI 3D 模型生成提示詞工程師，精通 Meshy、TripoSR、Rodin、Luma Genie、CSM、Zoo 等 text-to-3D / image-to-3D 模型，熟悉 PBR 材質、拓撲、UV、LOD，能產出遊戲與 3D 列印可用資產的提示詞。適用於遊戲資產、3D 列印、AR/VR 場景、產品概念。當使用者描述 3D 模型需求時啟動。
general
Create your LLMOps data engineering skill in one prompt, then learn to improve it throughout the chapter — from panaversity/claude-code-skills-lab
engineering
Create your llmops-fine-tuner skill from Unsloth documentation before learning fine-tuning theory — from panaversity/skills-lab.git
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Create a reusable skill for evaluating fine-tuned models, benchmarking performance, and detecting quality regressions — from panaversity/skills-lab.git
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Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, op — from Or
general
LiteLLM-RS A2A Protocol Architecture. Covers Agent-to-Agent communication, JSON-RPC 2.0 messaging, multi-provider orchestration, agent registry, and task state management.
engineering
Style rules for academic prose in ML / computational biology venues. Loaded by draft-writer and prose-polisher to ensure consistent voice and avoid LLM-typical patterns.
science
Use when positioning an ACL submission against the NLP literature, covering ACL Anthology citation practice, arXiv-versus-published version citation, concurrent LLM-era preprints,
product
Use when revising an ACL paper for computational-linguistics house style, covering task-first framing, linguistic examples tied to quantitative error analysis, scoping language cla
general
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
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Add a persistent wiki knowledge base to a NanoClaw group. Based on Karpathy's LLM Wiki pattern. Triggers on "add wiki", "wiki", "knowledge base", "llm wiki", "karpathy wi — from na
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Add a persistent wiki knowledge base to a NanoClaw group. Based on Karpathy's LLM Wiki pattern. Triggers on "add wiki", "wiki", "knowledge base", "llm wiki", "karpathy wi — from ma
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Langfuse OSS LLM-observability conventions — production traces graduate to the next eval dataset, cross-family LLM judges, versioned reproducible datasets, the MCP at /api/public/m
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Use when when you have access to annotated MS/MS spectra from a specific ionization mode (e.g., negative ESI) or adduct class (e.
general
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metr
content
Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, c — from si
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Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, c — from ma
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Expert photography prompt engineer specializing in crafting detailed, evocative prompts for AI image generation. Masters the art of translating visual concepts into preci — from mk
general
Perform 12-Factor Agents compliance analysis on any codebase. Use when evaluating agent architecture, reviewing LLM-powered systems, or auditing agentic applications against the 12
engineering
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and r
engineering
Exposes Hermes self-learning architecture to allow CEO Kit agents to autonomously build new scripts (SKILL.md) and fine-tune their base model weights.
engineering
Use this agent to review, critique, redesign, or author research prompts that will be pasted into frontier LLMs such as Claude.ai Deep Research, Gemini Advanced Deep Research, Perp
science
Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production se — from ma
engineering
Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production se — from ma
engineering
Специализированный скилл для диагностики и исправления зависаний, деградации контекста и нестабильности LLM в агентском режиме (dialogue_node.py + MCP tools). Используй когда: робо
general
Analyze the codebase to create a concise, LLM-optimized structured overview in .agent/map.md. — from general/ai-tooling
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Analyze the codebase to create a concise, LLM-optimized structured overview in .agent/map.md. — from majiayu000/claude-skill-registry
general
Generate narrative summaries from git history for onboarding, retrospectives, changelogs, and exploration. LLM-enhanced when available, works without LLM too.
content
Measure and improve the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset
engineering
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, m
engineering
Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying m
engineering
Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production pro
engineering
Advanced Prompt Engineering IA — Expert en ingénierie de prompts avancée (chain-of-thought, few-shot, system prompts, tool use, structured output)
engineering
Ready-to-use prompt templates for specialized agents. Use when building n8n workflows, AI integrations, or sales materials. Contains structured prompts for automation-architect, ll
sales
Train LLM-based agents with end-to-end RL by extending MDPs to handle tool invocation and environmental stochasticity—enable dense process rewards for intermediate steps and masked
general
Version Prompt Templates and agent topic prompts: source-control shape, change review, model-version pinning, A/B, and rollback. Trigger keywords: prompt template versioning, promp
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Unifying control-systems metalayer for LLM-as-controller agent development. Bootstrap any repository with typed plant/action/trace schemas, safety shield conventions, multi-rate lo
engineering
Patterns for evaluating and improving AI agent outputs through iterative refinement loops. Use when implementing self-critique, building evaluator-optimizer pipelines, creating rub
general
Grafana Labs LLM plugin, Assistant ve HTTP API ayrımını Sentinel CLI bağlamında açıklarken kullan.
engineering
Ollama, LM Studio, vLLM gibi yerel OpenAI uyumlu /v1 uçları için port, model ve tool desteği fallback’ini yapılandırırken kullan.
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Uzak OpenAI uyumlu API ile base_url, api_key, model ve proxy kullanımını yapılandırırken kullan.
general
Identify agentic AI security threats based on OWASP Top 10 for Agentic Applications 2026. Use when analyzing AI agents, LLM-powered applications, chatbots, auto-reply systems, tool
security
Use when reviewing or auditing an existing agent / LLM-pipeline architecture — e.g. 'is my workflow actually decomposed or secretly a mega-agent?', 'are my task boundaries and succ
engineering
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-mak — from it
engineering
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-mak — from ma
engineering
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, to — from la
engineering
Analyze evaluation results for quality improvements — identify LLM-judge evaluators replaceable with deterministic assertions, flag weak/vague assertions, and surface cost/quality
general
Write, edit, review, and validate AgentV EVAL.yaml / .eval.yaml evaluation files. Use when asked to create new eval files, update or fix existing ones, add or remove test cases, co
content
Use when wiring up or switching between China-domestic LLM providers (DeepSeek, Doubao/Volc Ark, Qwen/DashScope, MiniMax). Provides OpenAI-compatible adapter pattern, env-var contr
general
Fine-tune Gemma 4 26B-MoE and Qwen3-VL-30B-A3B with LoRA rank 16 BF16, deploy Qwen3.5-35B-A3B with vLLM on Azure H100 NVL 96GB for AgroSatCopilot. Use when fine-tuning VLMs with Lo
general
Route an AI-agent engineering task to the right skill among 14 meta specialists — planning a multi-session build, decomposing a plan into an agent chain, orchestrating a squad, run
engineering
Evaluate AI capability sourcing options across build, buy, fine-tune, and partner archetypes using a structured decision matrix. Use when deciding whether to build a custom model,
general
Detect AI/LLM-generated text patterns in research writing. Use when: (1) Reviewing manuscript drafts before submission, (2) Pre-commit validation of documentation, (3) Quality assu
science
Develop "quizzes" (evals) to measure model performance on specific tasks. Use these benchmarks to guide fine-tuning, determine product UX patterns, and track performance improvemen
general
Comprehensive AI/LLM evaluation toolkit for production AI systems. Covers LLM output quality, prompt engineering, RAG evaluation, agent performance, hallucination detection, bias a
general
Comprehensive AI/ML expertise covering prompt engineering, LLM architecture, AI agent design, RAG systems, fine-tuning, AI safety, and cutting-edge AI research for building and lev
science
Design an evaluation plan for a product AI feature (LLM- or model-backed output): measurable success criteria, a held-out labeled eval dataset shape, per-criterion grading (code-ba
product
Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference serv
engineering
Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and
security
Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving — from en
engineering
Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving — from ma
engineering
Expert guide for integrating Large Language Models (LLMs), RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, arsitektur RAG, vector database, da
engineering
Enforces safe AI usage practices, prevents prompt injection, and ensures model safety
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Use for AI/LLM security assessments, prompt injection, RAG security, agent/tool permissioning, model supply chain, LLM red teaming, AI governance, eval design, data leakage, jailbr
security
Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering. — from gmh5225/awesome-skills
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Guide for AI Agents and LLM development skills including RAG, multi-agent systems, prompt engineering, memory systems, and context engineering. — from majiayu000/claude-skill-regis
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AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
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Guide pour le fine-tuning de modèles ML/LLM (LoRA, QLoRA, PEFT, datasets, hyperparamètres) — from general/general-misc
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Evaluate and compare LLMs, ML APIs, and fine-tuned models for product fit across quality, latency, cost, compliance, and vendor risk dimensions. Use when selecting an AI model or v
general
Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhan
content
Erstellt eine portable KI-Arbeitsumgebung auf einem USB-Stick oder beliebigem Laufwerk. RAG-Pipeline mit lokalen LLM-Modellen (Ollama), Vektordatenbank (ChromaDB) und vorkonfigurie
general
AI engineering skill for prompt optimization, context inference, and intelligent command routing across different models and use cases
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Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (in
engineering
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed im
engineering
Pattern recognition for LLM-generated resume text — sentence length variance, em-dash density, and generic accomplishment phrasing
general
Use this when: design an AI system, RAG vs fine-tuning, my agent keeps looping, architect a multi-agent system, which LLM should I use, context window keeps overflowing, add guardr
engineering
Patterns in vocabulary, structure, formatting, tone, and citations that are characteristic of LLM-generated text. Avoid these when writing any text.
general
AIChat is a comprehensive LLM command-line tool written in Rust that combines chat-REPL, shell command generation, RAG, AI tools, and multi-provider support into a single binary. I
tools
AI-Driven Development — методология и принципы написания документации для проектов с LLM-агентом. Используй когда: AIDD, AI-driven, планирование проекта, idea.md, vision.md, workfl
general
Website Audit mit 230+ Rules für SEO, Performance, Security, Technical und Content Issues. LLM-optimierte Reports mit Health Scores und Handlungsempfehlungen.
security
Build agentic LLM-driven robotic manipulation pipelines using the ALRM framework pattern: a ReAct-style reasoning loop with dual execution modes (Code-as-Policy for direct code gen
engineering
Runs automated LLM-driven hypothesis generation and testing on tabular datasets with HypoGeniC, combining literature insights with data-driven testing. Use when systematically expl
science
Use this umbrella skill when the request spans multiple AltLLM Portal CLI domains, or when you need to navigate the local altllm CLI in this repository across auth, API k — from in
general
Use this umbrella skill when the request spans multiple AltLLM Portal CLI domains, or when you need to navigate the local altllm CLI in this repository across auth, API k — from bg
tools
Ranks candidate skills/agents by task fit using Sonnet LLM-as-judge AND classifies task complexity (model + effort) in same call. Input is union of cheatsheet + FTS5 candidates wit
general
LLM-based architectural analysis that transforms raw project data into meaningful structure
engineering
\"Analyze prompts for clarity, effectiveness, and optimization opportunities. 分析提示之清晰度、有效性及優化機會。 Use when: reviewing existing prompts, identifying issues before deployment, generat
engineering
Generates LLM-optimized code context with function call graphs, side effect detection, and incremental updates. Processes JavaScript/TypeScript codebases to create compact semantic
engineering
Create flexible annotation workflows for AI applications. Contains common tools to explore raw ai agent logs/transcripts, extract out relevant evaluation data, and llm-as-a-judge c
content
Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.
general
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications — from la
engineering
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring — from eng
engineering
Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug age — from si
general
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and OpenAI-comp
engineering
Integrate external REST and GraphQL APIs with proper authentication (Bearer, Basic, OAuth), error handling, retry logic, and JSON schema validation. Use when making API c — from di
engineering
Integrate external REST and GraphQL APIs with proper authentication (Bearer, Basic, OAuth), error handling, retry logic, and JSON schema validation. Use when making API c — from ma
engineering
One repeatable pass that makes a doc read like a person wrote it, not a model. Runs the doc-appeal scorecard (tools/doc_appeal_scorecard.py), turns each HARD defect into a required
general
Apple Silicon (M1–M5) でローカル LLM の推論ランタイムを選ぶ・足す・最適化するときの判断軸。mlx_lm.server は Ollama 比 ~1.8x 速だが生成専用（埋め込み endpoint なし・response_format/JSON schema 拘束なし）なので埋め込みは Ollama に残す2サーバ構成になる点、ユニ
general
Instrument agentic LLM apps built on the Claude Agent SDK (claude-agent-sdk) and/or LangGraph with Arize Phoenix and OpenInference — tracing, evaluation, annotations, experiments,
science
Phase 4 of the aspirations loop: executes a selected goal end-to-end with precondition checks, LLM-driven intelligent retrieval, memory deliberation, subagent delegation, primary e
general
Execute AssemblyAI streaming transcription and LeMUR workflows. Use when implementing real-time speech-to-text, live captions, voice agents, or LLM-powered audio analysis with LeMU
content
Audit websites for SEO, technical, content, and security issues using squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis — from Leo
security
Audit websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with healt
tools
Audit websites for SEO, technical, content, and security issues using squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis — from maj
security
（中文）Audit websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with h
security
LiteLLM-RS Authentication Architecture. Covers JWT + API Key + RBAC multi-method auth, rate limiting with DashMap, middleware pipeline, and secure credential management.
engineering
Iteratively auto-optimize a prompt until no issues remain. Uses prompt-reviewer in a loop, asks user for ambiguities, applies fixes via prompt-engineering skill. Runs until converg
general
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with \"auto review loop llm\" or — from Yul
science
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with \"auto review loop llm\" or — from maj
science
Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check r
general
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