[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADRFCKF7DP3RVVHM29V722":3,"skill-same:01M6ADRFCKF7DP3RVVHM29V722":79},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6ADRFCKF7DP3RVVHM29V722","llm-app-patterns",null,"Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fllm-app-patterns\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"llm app patterns","app","patterns","up","when","for","implementing","production-ready","building agents","LLM Applications","architectures","rag","ready","use","prompt","applications","Production","monitoring","llmops","observability","2026-11-10 10:12:30",false,"---\nname: llm-app-patterns\ndescription: \"Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.\"\n---\n\n# ? LLM Application Patterns\n\n> Production-ready patterns for building LLM applications, inspired by [Dify](https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify) and industry best practices.\n\n## When to Use This Skill\n\nUse this skill when:\n\n- Designing LLM-powered applications\n- Implementing RAG (Retrieval-Augmented Generation)\n- Building AI agents with tools\n- Setting up LLMOps monitoring\n- Choosing between agent architectures\n\n---\n\n## 1. RAG Pipeline Architecture\n\n### Overview\n\nRAG (Retrieval-Augmented Generation) grounds LLM responses in your data.\n\n```\n┌─────────────┐     ┌─────────────┐     ┌─────────────┐\n│   Ingest    │────▶│   Retrieve  │────▶│   Generate  │\n│  Documents  │     │   Context   │     │   Response  │\n└─────────────┘     └─────────────┘     └─────────────┘\n      │                   │                   │\n      ▼                   ▼                   ▼\n ┌─────────┐       ┌───────────┐       ┌───────────┐\n │ Chunking│       │  Vector   │       │    LLM    │\n │Embedding│       │  Search   │       │  + Context│\n └─────────┘       └───────────┘       └───────────┘\n```\n\n### 1.1 Document Ingestion\n\n```python\n# Chunking strategies\nclass ChunkingStrategy:\n    # Fixed-size chunks (simple but may break context)\n    FIXED_SIZE = \"fixed_size\"  # e.g., 512 tokens\n\n    # Semantic chunking (preserves meaning)\n    SEMANTIC = \"semantic\"      # Split on paragraphs\u002Fsections\n\n    # Recursive splitting (tries multiple separators)\n    RECURSIVE = \"recursive\"    # [\"\\n\\n\", \"\\n\", \" \", \"\"]\n\n    # Document-aware (respects structure)\n    DOCUMENT_AWARE = \"document_aware\"  # Headers, lists, etc.\n\n# Recommended settings\nCHUNK_CONFIG = {\n    \"chunk_size\": 512,       # tokens\n    \"chunk_overlap\": 50,     # token overlap between chunks\n    \"separators\": [\"\\n\\n\", \"\\n\", \". \", \" \"],\n}\n```\n\n### 1.2 Embedding & Storage\n\n```python\n# Vector database selection\nVECTOR_DB_OPTIONS = {\n    \"pinecone\": {\n        \"use_case\": \"Production, managed service\",\n        \"scale\": \"Billions of vectors\",\n        \"features\": [\"Hybrid search\", \"Metadata filtering\"]\n    },\n    \"weaviate\": {\n        \"use_case\": \"Self-hosted, multi-modal\",\n        \"scale\": \"Millions of vectors\",\n        \"features\": [\"GraphQL API\", \"Modules\"]\n    },\n    \"chromadb\": {\n        \"use_case\": \"Development, prototyping\",\n        \"scale\": \"Thousands of vectors\",\n        \"features\": [\"Simple API\", \"In-memory option\"]\n    },\n    \"pgvector\": {\n        \"use_case\": \"Existing Postgres infrastructure\",\n        \"scale\": \"Millions of vectors\",\n        \"features\": [\"SQL integration\", \"ACID compliance\"]\n    }\n}\n\n# Embedding model selection\nEMBEDDING_MODELS = {\n    \"openai\u002Ftext-embedding-3-small\": {\n        \"dimensions\": 1536,\n        \"cost\": \"$0.02\u002F1M tokens\",\n        \"quality\": \"Good for most use cases\"\n    },\n    \"openai\u002Ftext-embedding-3-large\": {\n        \"dimensions\": 3072,\n        \"cost\": \"$0.13\u002F1M tokens\",\n        \"quality\": \"Best for complex queries\"\n    },\n    \"local\u002Fbge-large\": {\n        \"dimensions\": 1024,\n        \"cost\": \"Free (compute only)\",\n        \"quality\": \"Comparable to OpenAI small\"\n    }\n}\n```\n\n### 1.3 Retrieval Strategies\n\n```python\n# Basic semantic search\ndef semantic_search(query: str, top_k: int = 5):\n    query_embedding = embed(query)\n    results = vector_db.similarity_search(\n        query_embedding,\n        top_k=top_k\n    )\n    return results\n\n# Hybrid search (semantic + keyword)\ndef hybrid_search(query: str, top_k: int = 5, alpha: float = 0.5):\n    \"\"\"\n    alpha=1.0: Pure semantic\n    alpha=0.0: Pure keyword (BM25)\n    alpha=0.5: Balanced\n    \"\"\"\n    semantic_results = vector_db.similarity_search(query)\n    keyword_results = bm25_search(query)\n\n    # Reciprocal Rank Fusion\n    return rrf_merge(semantic_results, keyword_results, alpha)\n\n# Multi-query retrieval\ndef multi_query_retrieval(query: str):\n    \"\"\"Generate multiple query variations for better recall\"\"\"\n    queries = llm.generate_query_variations(query, n=3)\n    all_results = []\n    for q in queries:\n        all_results.extend(semantic_search(q))\n    return deduplicate(all_results)\n\n# Contextual compression\ndef compressed_retrieval(query: str):\n    \"\"\"Retrieve then compress to relevant parts only\"\"\"\n    docs = semantic_search(query, top_k=10)\n    compressed = llm.extract_relevant_parts(docs, query)\n    return compressed\n```\n\n### 1.4 Generation with Context\n\n```python\nRAG_PROMPT_TEMPLATE = \"\"\"\nAnswer the user's question based ONLY on the following context.\nIf the context doesn't contain enough information, say \"I don't have enough information to answer that.\"\n\nContext:\n{context}\n\nQuestion: {question}\n\nAnswer:\"\"\"\n\ndef generate_with_rag(question: str):\n    # Retrieve\n    context_docs = hybrid_search(question, top_k=5)\n    context = \"\\n\\n\".join([doc.content for doc in context_docs])\n\n    # Generate\n    prompt = RAG_PROMPT_TEMPLATE.format(\n        context=context,\n        question=question\n    )\n\n    response = llm.generate(prompt)\n\n    # Return with citations\n    return {\n        \"answer\": response,\n        \"sources\": [doc.metadata for doc in context_docs]\n    }\n```\n\n---\n\n## 2. Agent Architectures\n\n### 2.1 ReAct Pattern (Reasoning + Acting)\n\n```\nThought: I need to search for information about X\nAction: search(\"X\")\nObservation: [search results]\nThought: Based on the results, I should...\nAction: calculate(...)\nObservation: [calculation result]\nThought: I now have enough information\nAction: final_answer(\"The answer is...\")\n```\n\n```python\nREACT_PROMPT = \"\"\"\nYou are an AI assistant that can use tools to answer questions.\n\nAvailable tools:\n{tools_description}\n\nUse this format:\nThought: [your reasoning about what to do next]\nAction: [tool_name(arguments)]\nObservation: [tool result - this will be filled in]\n... (repeat Thought\u002FAction\u002FObservation as needed)\nThought: I have enough information to answer\nFinal Answer: [your final response]\n\nQuestion: {question}\n\"\"\"\n\nclass ReActAgent:\n    def __init__(self, tools: list, llm):\n        self.tools = {t.name: t for t in tools}\n        self.llm = llm\n        self.max_iterations = 10\n\n    def run(self, question: str) -> str:\n        prompt = REACT_PROMPT.format(\n            tools_description=self._format_tools(),\n            question=question\n        )\n\n        for _ in range(self.max_iterations):\n            response = self.llm.generate(prompt)\n\n            if \"Final Answer:\" in response:\n                return self._extract_final_answer(response)\n\n            action = self._parse_action(response)\n            observation = self._execute_tool(action)\n            prompt += f\"\\nObservation: {observation}\\n\"\n\n        return \"Max iterations reached\"\n```\n\n### 2.2 Function Calling Pattern\n\n```python\n# Define tools as functions with schemas\nTOOLS = [\n    {\n        \"name\": \"search_web\",\n        \"description\": \"Search the web for current information\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"query\": {\n                    \"type\": \"string\",\n                    \"description\": \"Search query\"\n                }\n            },\n            \"required\": [\"query\"]\n        }\n    },\n    {\n        \"name\": \"calculate\",\n        \"description\": \"Perform mathematical calculations\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"expression\": {\n                    \"type\": \"string\",\n                    \"description\": \"Math expression to evaluate\"\n                }\n            },\n            \"required\": [\"expression\"]\n        }\n    }\n]\n\nclass FunctionCallingAgent:\n    def run(self, question: str) -> str:\n        messages = [{\"role\": \"user\", \"content\": question}]\n\n        while True:\n            response = self.llm.chat(\n                messages=messages,\n                tools=TOOLS,\n                tool_choice=\"auto\"\n            )\n\n            if response.tool_calls:\n                for tool_call in response.tool_calls:\n                    result = self._execute_tool(\n                        tool_call.name,\n                        tool_call.arguments\n                    )\n                    messages.append({\n                        \"role\": \"tool\",\n                        \"tool_call_id\": tool_call.id,\n                        \"content\": str(result)\n                    })\n            else:\n                return response.content\n```\n\n### 2.3 Plan-and-Execute Pattern\n\n```python\nclass PlanAndExecuteAgent:\n    \"\"\"\n    1. Create a plan (list of steps)\n    2. Execute each step\n    3. Replan if needed\n    \"\"\"\n\n    def run(self, task: str) -> str:\n        # Planning phase\n        plan = self.planner.create_plan(task)\n        # Returns: [\"Step 1: ...\", \"Step 2: ...\", ...]\n\n        results = []\n        for step in plan:\n            # Execute each step\n            result = self.executor.execute(step, context=results)\n            results.append(result)\n\n            # Check if replan needed\n            if self._needs_replan(task, results):\n                new_plan = self.planner.replan(\n                    task,\n                    completed=results,\n                    remaining=plan[len(results):]\n                )\n                plan = new_plan\n\n        # Synthesize final answer\n        return self.synthesizer.summarize(task, results)\n```\n\n### 2.4 Multi-Agent Collaboration\n\n```python\nclass AgentTeam:\n    \"\"\"\n    Specialized agents collaborating on complex tasks\n    \"\"\"\n\n    def __init__(self):\n        self.agents = {\n            \"researcher\": ResearchAgent(),\n            \"analyst\": AnalystAgent(),\n            \"writer\": WriterAgent(),\n            \"critic\": CriticAgent()\n        }\n        self.coordinator = CoordinatorAgent()\n\n    def solve(self, task: str) -> str:\n        # Coordinator assigns subtasks\n        assignments = self.coordinator.decompose(task)\n\n        results = {}\n        for assignment in assignments:\n            agent = self.agents[assignment.agent]\n            result = agent.execute(\n                assignment.subtask,\n                context=results\n            )\n            results[assignment.id] = result\n\n        # Critic reviews\n        critique = self.agents[\"critic\"].review(results)\n\n        if critique.needs_revision:\n            # Iterate with feedback\n            return self.solve_with_feedback(task, results, critique)\n\n        return self.coordinator.synthesize(results)\n```\n\n---\n\n## 3. Prompt IDE Patterns\n\n### 3.1 Prompt Templates with Variables\n\n```python\nclass PromptTemplate:\n    def __init__(self, template: str, variables: list[str]):\n        self.template = template\n        self.variables = variables\n\n    def format(self, **kwargs) -> str:\n        # Validate all variables provided\n        missing = set(self.variables) - set(kwargs.keys())\n        if missing:\n            raise ValueError(f\"Missing variables: {missing}\")\n\n        return self.template.format(**kwargs)\n\n    def with_examples(self, examples: list[dict]) -> str:\n        \"\"\"Add few-shot examples\"\"\"\n        example_text = \"\\n\\n\".join([\n            f\"Input: {ex['input']}\\nOutput: {ex['output']}\"\n            for ex in examples\n        ])\n        return f\"{example_text}\\n\\n{self.template}\"\n\n# Usage\nsummarizer = PromptTemplate(\n    template=\"Summarize the following text in {style} style:\\n\\n{text}\",\n    variables=[\"style\", \"text\"]\n)\n\nprompt = summarizer.format(\n    style=\"professional\",\n    text=\"Long article content...\"\n)\n```\n\n### 3.2 Prompt Versioning & A\u002FB Testing\n\n```python\nclass PromptRegistry:\n    def __init__(self, db):\n        self.db = db\n\n    def register(self, name: str, template: str, version: str):\n        \"\"\"Store prompt with version\"\"\"\n        self.db.save({\n            \"name\": name,\n            \"template\": template,\n            \"version\": version,\n            \"created_at\": datetime.now(),\n            \"metrics\": {}\n        })\n\n    def get(self, name: str, version: str = \"latest\") -> str:\n        \"\"\"Retrieve specific version\"\"\"\n        return self.db.get(name, version)\n\n    def ab_test(self, name: str, user_id: str) -> str:\n        \"\"\"Return variant based on user bucket\"\"\"\n        variants = self.db.get_all_versions(name)\n        bucket = hash(user_id) % len(variants)\n        return variants[bucket]\n\n    def record_outcome(self, prompt_id: str, outcome: dict):\n        \"\"\"Track prompt performance\"\"\"\n        self.db.update_metrics(prompt_id, outcome)\n```\n\n### 3.3 Prompt Chaining\n\n```python\nclass PromptChain:\n    \"\"\"\n    Chain prompts together, passing output as input to next\n    \"\"\"\n\n    def __init__(self, steps: list[dict]):\n        self.steps = steps\n\n    def run(self, initial_input: str) -> dict:\n        context = {\"input\": initial_input}\n        results = []\n\n        for step in self.steps:\n            prompt = step[\"prompt\"].format(**context)\n            output = llm.generate(prompt)\n\n            # Parse output if needed\n            if step.get(\"parser\"):\n                output = step[\"parser\"](output)\n\n            context[step[\"output_key\"]] = output\n            results.append({\n                \"step\": step[\"name\"],\n                \"output\": output\n            })\n\n        return {\n            \"final_output\": context[self.steps[-1][\"output_key\"]],\n            \"intermediate_results\": results\n        }\n\n# Example: Research → Analyze → Summarize\nchain = PromptChain([\n    {\n        \"name\": \"research\",\n        \"prompt\": \"Research the topic: {input}\",\n        \"output_key\": \"research\"\n    },\n    {\n        \"name\": \"analyze\",\n        \"prompt\": \"Analyze these findings:\\n{research}\",\n        \"output_key\": \"analysis\"\n    },\n    {\n        \"name\": \"summarize\",\n        \"prompt\": \"Summarize this analysis in 3 bullet points:\\n{analysis}\",\n        \"output_key\": \"summary\"\n    }\n])\n```\n\n---\n\n## 4. LLMOps & Observability\n\n### 4.1 Metrics to Track\n\n```python\nLLM_METRICS = {\n    # Performance\n    \"latency_p50\": \"50th percentile response time\",\n    \"latency_p99\": \"99th percentile response time\",\n    \"tokens_per_second\": \"Generation speed\",\n\n    # Quality\n    \"user_satisfaction\": \"Thumbs up\u002Fdown ratio\",\n    \"task_completion\": \"% tasks completed successfully\",\n    \"hallucination_rate\": \"% responses with factual errors\",\n\n    # Cost\n    \"cost_per_request\": \"Average $ per API call\",\n    \"tokens_per_request\": \"Average tokens used\",\n    \"cache_hit_rate\": \"% requests served from cache\",\n\n    # Reliability\n    \"error_rate\": \"% failed requests\",\n    \"timeout_rate\": \"% requests that timed out\",\n    \"retry_rate\": \"% requests needing retry\"\n}\n```\n\n### 4.2 Logging & Tracing\n\n```python\nimport logging\nfrom opentelemetry import trace\n\ntracer = trace.get_tracer(__name__)\n\nclass LLMLogger:\n    def log_request(self, request_id: str, data: dict):\n        \"\"\"Log LLM request for debugging and analysis\"\"\"\n        log_entry = {\n            \"request_id\": request_id,\n            \"timestamp\": datetime.now().isoformat(),\n            \"model\": data[\"model\"],\n            \"prompt\": data[\"prompt\"][:500],  # Truncate for storage\n            \"prompt_tokens\": data[\"prompt_tokens\"],\n            \"temperature\": data.get(\"temperature\", 1.0),\n            \"user_id\": data.get(\"user_id\"),\n        }\n        logging.info(f\"LLM_REQUEST: {json.dumps(log_entry)}\")\n\n    def log_response(self, request_id: str, data: dict):\n        \"\"\"Log LLM response\"\"\"\n        log_entry = {\n            \"request_id\": request_id,\n            \"completion_tokens\": data[\"completion_tokens\"],\n            \"total_tokens\": data[\"total_tokens\"],\n            \"latency_ms\": data[\"latency_ms\"],\n            \"finish_reason\": data[\"finish_reason\"],\n            \"cost_usd\": self._calculate_cost(data),\n        }\n        logging.info(f\"LLM_RESPONSE: {json.dumps(log_entry)}\")\n\n# Distributed tracing\n@tracer.start_as_current_span(\"llm_call\")\ndef call_llm(prompt: str) -> str:\n    span = trace.get_current_span()\n    span.set_attribute(\"prompt.length\", len(prompt))\n\n    response = llm.generate(prompt)\n\n    span.set_attribute(\"response.length\", len(response))\n    span.set_attribute(\"tokens.total\", response.usage.total_tokens)\n\n    return response.content\n```\n\n### 4.3 Evaluation Framework\n\n```python\nclass LLMEvaluator:\n    \"\"\"\n    Evaluate LLM outputs for quality\n    \"\"\"\n\n    def evaluate_response(self,\n                          question: str,\n                          response: str,\n                          ground_truth: str = None) -> dict:\n        scores = {}\n\n        # Relevance: Does it answer the question?\n        scores[\"relevance\"] = self._score_relevance(question, response)\n\n        # Coherence: Is it well-structured?\n        scores[\"coherence\"] = self._score_coherence(response)\n\n        # Groundedness: Is it based on provided context?\n        scores[\"groundedness\"] = self._score_groundedness(response)\n\n        # Accuracy: Does it match ground truth?\n        if ground_truth:\n            scores[\"accuracy\"] = self._score_accuracy(response, ground_truth)\n\n        # Harmfulness: Is it safe?\n        scores[\"safety\"] = self._score_safety(response)\n\n        return scores\n\n    def run_benchmark(self, test_cases: list[dict]) -> dict:\n        \"\"\"Run evaluation on test set\"\"\"\n        results = []\n        for case in test_cases:\n            response = llm.generate(case[\"prompt\"])\n            scores = self.evaluate_response(\n                question=case[\"prompt\"],\n                response=response,\n                ground_truth=case.get(\"expected\")\n            )\n            results.append(scores)\n\n        return self._aggregate_scores(results)\n```\n\n---\n\n## 5. Production Patterns\n\n### 5.1 Caching Strategy\n\n```python\nimport hashlib\nfrom functools import lru_cache\n\nclass LLMCache:\n    def __init__(self, redis_client, ttl_seconds=3600):\n        self.redis = redis_client\n        self.ttl = ttl_seconds\n\n    def _cache_key(self, prompt: str, model: str, **kwargs) -> str:\n        \"\"\"Generate deterministic cache key\"\"\"\n        content = f\"{model}:{prompt}:{json.dumps(kwargs, sort_keys=True)}\"\n        return hashlib.sha256(content.encode()).hexdigest()\n\n    def get_or_generate(self, prompt: str, model: str, **kwargs) -> str:\n        key = self._cache_key(prompt, model, **kwargs)\n\n        # Check cache\n        cached = self.redis.get(key)\n        if cached:\n            return cached.decode()\n\n        # Generate\n        response = llm.generate(prompt, model=model, **kwargs)\n\n        # Cache (only cache deterministic outputs)\n        if kwargs.get(\"temperature\", 1.0) == 0:\n            self.redis.setex(key, self.ttl, response)\n\n        return response\n```\n\n### 5.2 Rate Limiting & Retry\n\n```python\nimport time\nfrom tenacity import retry, wait_exponential, stop_after_attempt\n\nclass RateLimiter:\n    def __init__(self, requests_per_minute: int):\n        self.rpm = requests_per_minute\n        self.timestamps = []\n\n    def acquire(self):\n        \"\"\"Wait if rate limit would be exceeded\"\"\"\n        now = time.time()\n\n        # Remove old timestamps\n        self.timestamps = [t for t in self.timestamps if now - t \u003C 60]\n\n        if len(self.timestamps) >= self.rpm:\n            sleep_time = 60 - (now - self.timestamps[0])\n            time.sleep(sleep_time)\n\n        self.timestamps.append(time.time())\n\n# Retry with exponential backoff\n@retry(\n    wait=wait_exponential(multiplier=1, min=4, max=60),\n    stop=stop_after_attempt(5)\n)\ndef call_llm_with_retry(prompt: str) -> str:\n    try:\n        return llm.generate(prompt)\n    except RateLimitError:\n        raise  # Will trigger retry\n    except APIError as e:\n        if e.status_code >= 500:\n            raise  # Retry server errors\n        raise  # Don't retry client errors\n```\n\n### 5.3 Fallback Strategy\n\n```python\nclass LLMWithFallback:\n    def __init__(self, primary: str, fallbacks: list[str]):\n        self.primary = primary\n        self.fallbacks = fallbacks\n\n    def generate(self, prompt: str, **kwargs) -> str:\n        models = [self.primary] + self.fallbacks\n\n        for model in models:\n            try:\n                return llm.generate(prompt, model=model, **kwargs)\n            except (RateLimitError, APIError) as e:\n                logging.warning(f\"Model {model} failed: {e}\")\n                continue\n\n        raise AllModelsFailedError(\"All models exhausted\")\n\n# Usage\nllm_client = LLMWithFallback(\n    primary=\"gpt-4-turbo\",\n    fallbacks=[\"gpt-3.5-turbo\", \"claude-3-sonnet\"]\n)\n```\n\n---\n\n## Architecture Decision Matrix\n\n| Pattern              | Use When         | Complexity | Cost      |\n| :------------------- | :--------------- | :--------- | :-------- |\n| **Simple RAG**       | FAQ, docs search | Low        | Low       |\n| **Hybrid RAG**       | Mixed queries    | Medium     | Medium    |\n| **ReAct Agent**      | Multi-step tasks | Medium     | Medium    |\n| **Function Calling** | Structured tools | Low        | Low       |\n| **Plan-Execute**     | Complex tasks    | High       | High      |\n| **Multi-Agent**      | Research tasks   | Very High  | Very High |\n\n---\n\n## Resources\n\n- [Dify Platform](https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify)\n- [LangChain Docs](https:\u002F\u002Fpython.langchain.com\u002F)\n- [LlamaIndex](https:\u002F\u002Fwww.llamaindex.ai\u002F)\n- [Anthropic Cookbook](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fanthropic-cookbook)\n","# ? LLM Application Patterns\n\n> Production-ready patterns for building LLM applications, inspired by [Dify](https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify) and industry best practices.\n\n## When to Use This Skill\n\nUse this skill when:\n\n- Designing LLM-powered applications\n- Implementing RAG (Retrieval-Augmented Generation)\n- Building AI agents with tools\n- Setting up LLMOps monitoring\n- Choosing between agent architectures\n\n---\n\n## 1. RAG Pipeline Architecture\n\n### Overview\n\nRAG (Retrieval-Augmented Generation) grounds LLM responses in your data.\n\n```\n┌─────────────┐     ┌─────────────┐     ┌─────────────┐\n│   Ingest    │────▶│   Retrieve  │────▶│   Generate  │\n│  Documents  │     │   Context   │     │   Response  │\n└─────────────┘     └─────────────┘     └─────────────┘\n      │                   │                   │\n      ▼                   ▼                   ▼\n ┌─────────┐       ┌───────────┐       ┌───────────┐\n │ Chunking│       │  Vector   │       │    LLM    │\n │Embedding│       │  Search   │       │  + Context│\n └─────────┘       └───────────┘       └───────────┘\n```\n\n### 1.1 Document Ingestion\n\n```python\n# Chunking strategies\nclass ChunkingStrategy:\n    # Fixed-size chunks (simple but may break context)\n    FIXED_SIZE = \"fixed_size\"  # e.g., 512 tokens\n\n    # Semantic chunking (preserves meaning)\n    SEMANTIC = \"semantic\"      # Split on paragraphs\u002Fsections\n\n    # Recursive splitting (tries multiple separators)\n    RECURSIVE = \"recursive\"    # [\"\\n\\n\", \"\\n\", \" \", \"\"]\n\n    # Document-aware (respects structure)\n    DOCUMENT_AWARE = \"document_aware\"  # Headers, lists, etc.\n\n# Recommended settings\nCHUNK_CONFIG = {\n    \"chunk_size\": 512,       # tokens\n    \"chunk_overlap\": 50,     # token overlap between chunks\n    \"separators\": [\"\\n\\n\", \"\\n\", \". \", \" \"],\n}\n```\n\n### 1.2 Embedding & Storage\n\n```python\n# Vector database selection\nVECTOR_DB_OPTIONS = {\n    \"pinecone\": {\n        \"use_case\": \"Production, managed service\",\n        \"scale\": \"Billions of vectors\",\n        \"features\": [\"Hybrid search\", \"Metadata filtering\"]\n    },\n    \"weaviate\": {\n        \"use_case\": \"Self-hosted, multi-modal\",\n        \"scale\": \"Millions of vectors\",\n        \"features\": [\"GraphQL API\", \"Modules\"]\n    },\n    \"chromadb\": {\n        \"use_case\": \"Development, prototyping\",\n        \"scale\": \"Thousands of vectors\",\n        \"features\": [\"Simple API\", \"In-memory option\"]\n    },\n    \"pgvector\": {\n        \"use_case\": \"Existing Postgres infrastructure\",\n        \"scale\": \"Millions of vectors\",\n        \"features\": [\"SQL integration\", \"ACID compliance\"]\n    }\n}\n\n# Embedding model selection\nEMBEDDING_MODELS = {\n    \"openai\u002Ftext-embedding-3-small\": {\n        \"dimensions\": 1536,\n        \"cost\": \"$0.02\u002F1M tokens\",\n        \"quality\": \"Good for most use cases\"\n    },\n    \"openai\u002Ftext-embedding-3-large\": {\n        \"dimensions\": 3072,\n        \"cost\": \"$0.13\u002F1M tokens\",\n        \"quality\": \"Best for complex queries\"\n    },\n    \"local\u002Fbge-large\": {\n        \"dimensions\": 1024,\n        \"cost\": \"Free (compute only)\",\n        \"quality\": \"Comparable to OpenAI small\"\n    }\n}\n```\n\n### 1.3 Retrieval Strategies\n\n```python\n# Basic semantic search\ndef semantic_search(query: str, top_k: int = 5):\n    query_embedding = embed(query)\n    results = vector_db.similarity_search(\n        query_embedding,\n        top_k=top_k\n    )\n    return results\n\n# Hybrid search (semantic + keyword)\ndef hybrid_search(query: str, top_k: int = 5, alpha: float = 0.5):\n    \"\"\"\n    alpha=1.0: Pure semantic\n    alpha=0.0: Pure keyword (BM25)\n    alpha=0.5: Balanced\n    \"\"\"\n    semantic_results = vector_db.similarity_search(query)\n    keyword_results = bm25_search(query)\n\n    # Reciprocal Rank Fusion\n    return rrf_merge(semantic_results, keyword_results, alpha)\n\n# Multi-query retrieval\ndef multi_query_retrieval(query: str):\n    \"\"\"Generate multiple query variations for better recall\"\"\"\n    queries = llm.generate_query_variations(query, n=3)\n    all_results = []\n    for q in queries:\n        all_results.extend(semantic_search(q))\n    return deduplicate(all_results)\n\n# Contextual compression\ndef compressed_retrieval(query: str):\n    \"\"\"Retrieve then compress to relevant parts only\"\"\"\n    docs = semantic_search(query, top_k=10)\n    compressed = llm.extract_relevant_parts(docs, query)\n    return compressed\n```\n\n### 1.4 Generation with Context\n\n```python\nRAG_PROMPT_TEMPLATE = \"\"\"\nAnswer the user's question based ONLY on the following context.\nIf the context doesn't contain enough information, say \"I don't have enough information to answer that.\"\n\nContext:\n{context}\n\nQuestion: {question}\n\nAnswer:\"\"\"\n\ndef generate_with_rag(question: str):\n    # Retrieve\n    context_docs = hybrid_search(question, top_k=5)\n    context = \"\\n\\n\".join([doc.content for doc in context_docs])\n\n    # Generate\n    prompt = RAG_PROMPT_TEMPLATE.format(\n        context=context,\n        question=question\n    )\n\n    response = llm.generate(prompt)\n\n    # Return with citations\n    return {\n        \"answer\": response,\n        \"sources\": [doc.metadata for doc in context_docs]\n    }\n```\n\n---\n\n## 2. Agent Architectures\n\n### 2.1 ReAct Pattern (Reasoning + Acting)\n\n```\nThought: I need to search for information about X\nAction: search(\"X\")\nObservation: [search results]\nThought: Based on the results, I should...\nAction: calculate(...)\nObservation: [calculation result]\nThought: I now have enough information\nAction: final_answer(\"The answer is...\")\n```\n\n```python\nREACT_PROMPT = \"\"\"\nYou are an AI assistant that can use tools to answer questions.\n\nAvailable tools:\n{tools_description}\n\nUse this format:\nThought: [your reasoning about what to do next]\nAction: [tool_name(arguments)]\nObservation: [tool result - this will be filled in]\n... (repeat Thought\u002FAction\u002FObservation as needed)\nThought: I have enough information to answer\nFinal Answer: [your final response]\n\nQuestion: {question}\n\"\"\"\n\nclass ReActAgent:\n    def __init__(self, tools: list, llm):\n        self.tools = {t.name: t for t in tools}\n        self.llm = llm\n        self.max_iterations = 10\n\n    def run(self, question: str) -> str:\n        prompt = REACT_PROMPT.format(\n            tools_description=self._format_tools(),\n            question=question\n        )\n\n        for _ in range(self.max_iterations):\n            response = self.llm.generate(prompt)\n\n            if \"Final Answer:\" in response:\n                return self._extract_final_answer(response)\n\n            action = self._parse_action(response)\n            observation = self._execute_tool(action)\n            prompt += f\"\\nObservation: {observation}\\n\"\n\n        return \"Max iterations reached\"\n```\n\n### 2.2 Function Calling Pattern\n\n```python\n# Define tools as functions with schemas\nTOOLS = [\n    {\n        \"name\": \"search_web\",\n        \"description\": \"Search the web for current information\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"query\": {\n                    \"type\": \"string\",\n                    \"description\": \"Search query\"\n                }\n            },\n            \"required\": [\"query\"]\n        }\n    },\n    {\n        \"name\": \"calculate\",\n        \"description\": \"Perform mathematical calculations\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"expression\": {\n                    \"type\": \"string\",\n                    \"description\": \"Math expression to evaluate\"\n                }\n            },\n            \"required\": [\"expression\"]\n        }\n    }\n]\n\nclass FunctionCallingAgent:\n    def run(self, question: str) -> str:\n        messages = [{\"role\": \"user\", \"content\": question}]\n\n        while True:\n            response = self.llm.chat(\n                messages=messages,\n                tools=TOOLS,\n                tool_choice=\"auto\"\n            )\n\n            if response.tool_calls:\n                for tool_call in response.tool_calls:\n                    result = self._execute_tool(\n                        tool_call.name,\n                        tool_call.arguments\n                    )\n                    messages.append({\n                        \"role\": \"tool\",\n                        \"tool_call_id\": tool_call.id,\n                        \"content\": str(result)\n                    })\n            else:\n                return response.content\n```\n\n### 2.3 Plan-and-Execute Pattern\n\n```python\nclass PlanAndExecuteAgent:\n    \"\"\"\n    1. Create a plan (list of steps)\n    2. Execute each step\n    3. Replan if needed\n    \"\"\"\n\n    def run(self, task: str) -> str:\n        # Planning phase\n        plan = self.planner.create_plan(task)\n        # Returns: [\"Step 1: ...\", \"Step 2: ...\", ...]\n\n        results = []\n        for step in plan:\n            # Execute each step\n            result = self.executor.execute(step, context=results)\n            results.append(result)\n\n            # Check if replan needed\n            if self._needs_replan(task, results):\n                new_plan = self.planner.replan(\n                    task,\n                    completed=results,\n                    remaining=plan[len(results):]\n                )\n                plan = new_plan\n\n        # Synthesize final answer\n        return self.synthesizer.summarize(task, results)\n```\n\n### 2.4 Multi-Agent Collaboration\n\n```python\nclass AgentTeam:\n    \"\"\"\n    Specialized agents collaborating on complex tasks\n    \"\"\"\n\n    def __init__(self):\n        self.agents = {\n            \"researcher\": ResearchAgent(),\n            \"analyst\": AnalystAgent(),\n            \"writer\": WriterAgent(),\n            \"critic\": CriticAgent()\n        }\n        self.coordinator = CoordinatorAgent()\n\n    def solve(self, task: str) -> str:\n        # Coordinator assigns subtasks\n        assignments = self.coordinator.decompose(task)\n\n        results = {}\n        for assignment in assignments:\n            agent = self.agents[assignment.agent]\n            result = agent.execute(\n                assignment.subtask,\n                context=results\n            )\n            results[assignment.id] = result\n\n        # Critic reviews\n        critique = self.agents[\"critic\"].review(results)\n\n        if critique.needs_revision:\n            # Iterate with feedback\n            return self.solve_with_feedback(task, results, critique)\n\n        return self.coordinator.synthesize(results)\n```\n\n---\n\n## 3. Prompt IDE Patterns\n\n### 3.1 Prompt Templates with Variables\n\n```python\nclass PromptTemplate:\n    def __init__(self, template: str, variables: list[str]):\n        self.template = template\n        self.variables = variables\n\n    def format(self, **kwargs) -> str:\n        # Validate all variables provided\n        missing = set(self.variables) - set(kwargs.keys())\n        if missing:\n            raise ValueError(f\"Missing variables: {missing}\")\n\n        return self.template.format(**kwargs)\n\n    def with_examples(self, examples: list[dict]) -> str:\n        \"\"\"Add few-shot examples\"\"\"\n        example_text = \"\\n\\n\".join([\n            f\"Input: {ex['input']}\\nOutput: {ex['output']}\"\n            for ex in examples\n        ])\n        return f\"{example_text}\\n\\n{self.template}\"\n\n# Usage\nsummarizer = PromptTemplate(\n    template=\"Summarize the following text in {style} style:\\n\\n{text}\",\n    variables=[\"style\", \"text\"]\n)\n\nprompt = summarizer.format(\n    style=\"professional\",\n    text=\"Long article content...\"\n)\n```\n\n### 3.2 Prompt Versioning & A\u002FB Testing\n\n```python\nclass PromptRegistry:\n    def __init__(self, db):\n        self.db = db\n\n    def register(self, name: str, template: str, version: str):\n        \"\"\"Store prompt with version\"\"\"\n        self.db.save({\n            \"name\": name,\n            \"template\": template,\n            \"version\": version,\n            \"created_at\": datetime.now(),\n            \"metrics\": {}\n        })\n\n    def get(self, name: str, version: str = \"latest\") -> str:\n        \"\"\"Retrieve specific version\"\"\"\n        return self.db.get(name, version)\n\n    def ab_test(self, name: str, user_id: str) -> str:\n        \"\"\"Return variant based on user bucket\"\"\"\n        variants = self.db.get_all_versions(name)\n        bucket = hash(user_id) % len(variants)\n        return variants[bucket]\n\n    def record_outcome(self, prompt_id: str, outcome: dict):\n        \"\"\"Track prompt performance\"\"\"\n        self.db.update_metrics(prompt_id, outcome)\n```\n\n### 3.3 Prompt Chaining\n\n```python\nclass PromptChain:\n    \"\"\"\n    Chain prompts together, passing output as input to next\n    \"\"\"\n\n    def __init__(self, steps: list[dict]):\n        self.steps = steps\n\n    def run(self, initial_input: str) -> dict:\n        context = {\"input\": initial_input}\n        results = []\n\n        for step in self.steps:\n            prompt = step[\"prompt\"].format(**context)\n            output = llm.generate(prompt)\n\n            # Parse output if needed\n            if step.get(\"parser\"):\n                output = step[\"parser\"](output)\n\n            context[step[\"output_key\"]] = output\n            results.append({\n                \"step\": step[\"name\"],\n                \"output\": output\n            })\n\n        return {\n            \"final_output\": context[self.steps[-1][\"output_key\"]],\n            \"intermediate_results\": results\n        }\n\n# Example: Research → Analyze → Summarize\nchain = PromptChain([\n    {\n        \"name\": \"research\",\n        \"prompt\": \"Research the topic: {input}\",\n        \"output_key\": \"research\"\n    },\n    {\n        \"name\": \"analyze\",\n        \"prompt\": \"Analyze these findings:\\n{research}\",\n        \"output_key\": \"analysis\"\n    },\n    {\n        \"name\": \"summarize\",\n        \"prompt\": \"Summarize this analysis in 3 bullet points:\\n{analysis}\",\n        \"output_key\": \"summary\"\n    }\n])\n```\n\n---\n\n## 4. LLMOps & Observability\n\n### 4.1 Metrics to Track\n\n```python\nLLM_METRICS = {\n    # Performance\n    \"latency_p50\": \"50th percentile response time\",\n    \"latency_p99\": \"99th percentile response time\",\n    \"tokens_per_second\": \"Generation speed\",\n\n    # Quality\n    \"user_satisfaction\": \"Thumbs up\u002Fdown ratio\",\n    \"task_completion\": \"% tasks completed successfully\",\n    \"hallucination_rate\": \"% responses with factual errors\",\n\n    # Cost\n    \"cost_per_request\": \"Average $ per API call\",\n    \"tokens_per_request\": \"Average tokens used\",\n    \"cache_hit_rate\": \"% requests served from cache\",\n\n    # Reliability\n    \"error_rate\": \"% failed requests\",\n    \"timeout_rate\": \"% requests that timed out\",\n    \"retry_rate\": \"% requests needing retry\"\n}\n```\n\n### 4.2 Logging & Tracing\n\n```python\nimport logging\nfrom opentelemetry import trace\n\ntracer = trace.get_tracer(__name__)\n\nclass LLMLogger:\n    def log_request(self, request_id: str, data: dict):\n        \"\"\"Log LLM request for debugging and analysis\"\"\"\n        log_entry = {\n            \"request_id\": request_id,\n            \"timestamp\": datetime.now().isoformat(),\n            \"model\": data[\"model\"],\n            \"prompt\": data[\"prompt\"][:500],  # Truncate for storage\n            \"prompt_tokens\": data[\"prompt_tokens\"],\n            \"temperature\": data.get(\"temperature\", 1.0),\n            \"user_id\": data.get(\"user_id\"),\n        }\n        logging.info(f\"LLM_REQUEST: {json.dumps(log_entry)}\")\n\n    def log_response(self, request_id: str, data: dict):\n        \"\"\"Log LLM response\"\"\"\n        log_entry = {\n            \"request_id\": request_id,\n            \"completion_tokens\": data[\"completion_tokens\"],\n            \"total_tokens\": data[\"total_tokens\"],\n            \"latency_ms\": data[\"latency_ms\"],\n            \"finish_reason\": data[\"finish_reason\"],\n            \"cost_usd\": self._calculate_cost(data),\n        }\n        logging.info(f\"LLM_RESPONSE: {json.dumps(log_entry)}\")\n\n# Distributed tracing\n@tracer.start_as_current_span(\"llm_call\")\ndef call_llm(prompt: str) -> str:\n    span = trace.get_current_span()\n    span.set_attribute(\"prompt.length\", len(prompt))\n\n    response = llm.generate(prompt)\n\n    span.set_attribute(\"response.length\", len(response))\n    span.set_attribute(\"tokens.total\", response.usage.total_tokens)\n\n    return response.content\n```\n\n### 4.3 Evaluation Framework\n\n```python\nclass LLMEvaluator:\n    \"\"\"\n    Evaluate LLM outputs for quality\n    \"\"\"\n\n    def evaluate_response(self,\n                          question: str,\n                          response: str,\n                          ground_truth: str = None) -> dict:\n        scores = {}\n\n        # Relevance: Does it answer the question?\n        scores[\"relevance\"] = self._score_relevance(question, response)\n\n        # Coherence: Is it well-structured?\n        scores[\"coherence\"] = self._score_coherence(response)\n\n        # Groundedness: Is it based on provided context?\n        scores[\"groundedness\"] = self._score_groundedness(response)\n\n        # Accuracy: Does it match ground truth?\n        if ground_truth:\n            scores[\"accuracy\"] = self._score_accuracy(response, ground_truth)\n\n        # Harmfulness: Is it safe?\n        scores[\"safety\"] = self._score_safety(response)\n\n        return scores\n\n    def run_benchmark(self, test_cases: list[dict]) -> dict:\n        \"\"\"Run evaluation on test set\"\"\"\n        results = []\n        for case in test_cases:\n            response = llm.generate(case[\"prompt\"])\n            scores = self.evaluate_response(\n                question=case[\"prompt\"],\n                response=response,\n                ground_truth=case.get(\"expected\")\n            )\n            results.append(scores)\n\n        return self._aggregate_scores(results)\n```\n\n---\n\n## 5. Production Patterns\n\n### 5.1 Caching Strategy\n\n```python\nimport hashlib\nfrom functools import lru_cache\n\nclass LLMCache:\n    def __init__(self, redis_client, ttl_seconds=3600):\n        self.redis = redis_client\n        self.ttl = ttl_seconds\n\n    def _cache_key(self, prompt: str, model: str, **kwargs) -> str:\n        \"\"\"Generate deterministic cache key\"\"\"\n        content = f\"{model}:{prompt}:{json.dumps(kwargs, sort_keys=True)}\"\n        return hashlib.sha256(content.encode()).hexdigest()\n\n    def get_or_generate(self, prompt: str, model: str, **kwargs) -> str:\n        key = self._cache_key(prompt, model, **kwargs)\n\n        # Check cache\n        cached = self.redis.get(key)\n        if cached:\n            return cached.decode()\n\n        # Generate\n        response = llm.generate(prompt, model=model, **kwargs)\n\n        # Cache (only cache deterministic outputs)\n        if kwargs.get(\"temperature\", 1.0) == 0:\n            self.redis.setex(key, self.ttl, response)\n\n        return response\n```\n\n### 5.2 Rate Limiting & Retry\n\n```python\nimport time\nfrom tenacity import retry, wait_exponential, stop_after_attempt\n\nclass RateLimiter:\n    def __init__(self, requests_per_minute: int):\n        self.rpm = requests_per_minute\n        self.timestamps = []\n\n    def acquire(self):\n        \"\"\"Wait if rate limit would be exceeded\"\"\"\n        now = time.time()\n\n        # Remove old timestamps\n        self.timestamps = [t for t in self.timestamps if now - t \u003C 60]\n\n        if len(self.timestamps) >= self.rpm:\n            sleep_time = 60 - (now - self.timestamps[0])\n            time.sleep(sleep_time)\n\n        self.timestamps.append(time.time())\n\n# Retry with exponential backoff\n@retry(\n    wait=wait_exponential(multiplier=1, min=4, max=60),\n    stop=stop_after_attempt(5)\n)\ndef call_llm_with_retry(prompt: str) -> str:\n    try:\n        return llm.generate(prompt)\n    except RateLimitError:\n        raise  # Will trigger retry\n    except APIError as e:\n        if e.status_code >= 500:\n            raise  # Retry server errors\n        raise  # Don't retry client errors\n```\n\n### 5.3 Fallback Strategy\n\n```python\nclass LLMWithFallback:\n    def __init__(self, primary: str, fallbacks: list[str]):\n        self.primary = primary\n        self.fallbacks = fallbacks\n\n    def generate(self, prompt: str, **kwargs) -> str:\n        models = [self.primary] + self.fallbacks\n\n        for model in models:\n            try:\n                return llm.generate(prompt, model=model, **kwargs)\n            except (RateLimitError, APIError) as e:\n                logging.warning(f\"Model {model} failed: {e}\")\n                continue\n\n        raise AllModelsFailedError(\"All models exhausted\")\n\n# Usage\nllm_client = LLMWithFallback(\n    primary=\"gpt-4-turbo\",\n    fallbacks=[\"gpt-3.5-turbo\", \"claude-3-sonnet\"]\n)\n```\n\n---\n\n## Architecture Decision Matrix\n\n| Pattern              | Use When         | Complexity | Cost      |\n| :------------------- | :--------------- | :--------- | :-------- |\n| **Simple RAG**       | FAQ, docs search | Low        | Low       |\n| **Hybrid RAG**       | Mixed queries    | Medium     | Medium    |\n| **ReAct Agent**      | Multi-step tasks | Medium     | Medium    |\n| **Function Calling** | Structured tools | Low        | Low       |\n| **Plan-Execute**     | Complex tasks    | High       | High      |\n| **Multi-Agent**      | Research tasks   | Very High  | Very High |\n\n---\n\n## Resources\n\n- [Dify Platform](https:\u002F\u002Fgithub.com\u002Flanggenius\u002Fdify)\n- [LangChain Docs](https:\u002F\u002Fpython.langchain.com\u002F)\n- [LlamaIndex](https:\u002F\u002Fwww.llamaindex.ai\u002F)\n- [Anthropic Cookbook](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fanthropic-cookbook)\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fllm-app-patterns",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add davila7\u002Fclaude-code-templates --skill llm-app-patterns",{"tool":65,"command":66},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill llm-app-patterns",{"tool":68,"command":69},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill llm-app-patterns","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fllm-app-patterns\u002FSKILL.md",true,[73],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"package.json","REFERENCE","参考文档",810,"https:\u002F\u002Fgithub.com\u002Fnodetool-ai\u002Fnodetool\u002Fblob\u002Fmain\u002Fpackages\u002Fsandbox-packs\u002Fsandbox-ics\u002Fpackage.json",[80,105,132,165,200,223],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":87,"publishedAt":44},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[86],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[88,89,90,91,92,29,93,94,37,28,95,96,97,98,99,100,101,102,103,104],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":112,"publishedAt":44},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[111],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[113,114,115,116,117,118,119,120,121,122,123,28,124,125,126,127,128,129,130,131],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":147,"publishedAt":44},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[138,141,142],{"categoryNo":139,"categoryName":140,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":143,"categoryName":144,"taxonomyType":145,"taxonomyTypeDesc":146},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[148,149,150,151,152,153,154,29,155,156,157,158,159,160,161,37,162,163,28,164],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":169,"licenseFileUrl":170,"sourceHtmlUrl":171,"categories":172,"keywords":176,"publishedAt":44},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[173],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,29,199],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":169,"licenseFileUrl":204,"sourceHtmlUrl":205,"categories":206,"keywords":211,"publishedAt":44},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[207,210],{"categoryNo":208,"categoryName":209,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},[212,213,214,215,216,178,39,187,188,217,218,219,220,29,221,96,222],"webapp testing","testing","webapp","browser","playwright","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":169,"licenseFileUrl":227,"sourceHtmlUrl":228,"categories":229,"keywords":237,"publishedAt":44},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[230,233,234],{"categoryNo":231,"categoryName":232,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":235,"categoryName":236,"taxonomyType":145,"taxonomyTypeDesc":146},"01JBSEEDCAT000000000000036","内容创作者",[238,239,240,241,242,243,244,245,180,246,190,124,247,248,249,250,27,160,251,28,252,253,254,255,256,102,257],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","invoke","you","analysis","claims","argument","assemble","recommendations"]