[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADXRBP775CB9KCVANHMKFX":3,"skill-same:01M6ADXRBP775CB9KCVANHMKFX":107},{"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":26,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":69,"commitSha":70,"skillDirectory":71,"forksCount":72,"repositoryHtmlUrl":73,"repositoryHomepageUrl":74,"ownerHtmlUrl":75,"ownerBlogUrl":76,"ownerDescription":77,"updatedAt":78,"install":79,"resources":92},"01M6ADXRBP775CB9KCVANHMKFX","prompt-engineering-guidance",null,"Control LLM output with regex and grammars, guarantee valid JSON\u002FXML\u002Fcode generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-guidance\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"guidance","engineering","prompt engineering guidance","prompt engineering","prompt","Transformers","orchestra","formats","JSON Validation","Constrained Generation","structured output","regex","enforce","enforcement","grammar","Code Generation","xml","framework","validation","code","workflows","microsoft","research","valid","2026-11-10 10:12:30",false,"---\nname: prompt-engineering-guidance\ndescription: Control LLM output with regex and grammars, guarantee valid JSON\u002FXML\u002Fcode generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Prompt Engineering, Guidance, Constrained Generation, Structured Output, JSON Validation, Grammar, Microsoft Research, Format Enforcement, Multi-Step Workflows]\n  dependencies: [guidance, transformers]\n---\n\n# Guidance: Constrained LLM Generation\n\n## When to Use This Skill\n\nUse Guidance when you need to:\n- **Control LLM output syntax** with regex or grammars\n- **Guarantee valid JSON\u002FXML\u002Fcode** generation\n- **Reduce latency** vs traditional prompting approaches\n- **Enforce structured formats** (dates, emails, IDs, etc.)\n- **Build multi-step workflows** with Pythonic control flow\n- **Prevent invalid outputs** through grammatical constraints\n\n**GitHub Stars**: 18,000+ | **From**: Microsoft Research\n\n## Installation\n\n```bash\n# Base installation\npip install guidance\n\n# With specific backends\npip install guidance[transformers]  # Hugging Face models\npip install guidance[llama_cpp]     # llama.cpp models\n```\n\n## Quick Start\n\n### Basic Example: Structured Generation\n\n```python\nfrom guidance import models, gen\n\n# Load model (supports OpenAI, Transformers, llama.cpp)\nlm = models.OpenAI(\"gpt-4\")\n\n# Generate with constraints\nresult = lm + \"The capital of France is \" + gen(\"capital\", max_tokens=5)\n\nprint(result[\"capital\"])  # \"Paris\"\n```\n\n### With Anthropic Claude\n\n```python\nfrom guidance import models, gen, system, user, assistant\n\n# Configure Claude\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Use context managers for chat format\nwith system():\n    lm += \"You are a helpful assistant.\"\n\nwith user():\n    lm += \"What is the capital of France?\"\n\nwith assistant():\n    lm += gen(max_tokens=20)\n```\n\n## Core Concepts\n\n### 1. Context Managers\n\nGuidance uses Pythonic context managers for chat-style interactions.\n\n```python\nfrom guidance import system, user, assistant, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# System message\nwith system():\n    lm += \"You are a JSON generation expert.\"\n\n# User message\nwith user():\n    lm += \"Generate a person object with name and age.\"\n\n# Assistant response\nwith assistant():\n    lm += gen(\"response\", max_tokens=100)\n\nprint(lm[\"response\"])\n```\n\n**Benefits:**\n- Natural chat flow\n- Clear role separation\n- Easy to read and maintain\n\n### 2. Constrained Generation\n\nGuidance ensures outputs match specified patterns using regex or grammars.\n\n#### Regex Constraints\n\n```python\nfrom guidance import models, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Constrain to valid email format\nlm += \"Email: \" + gen(\"email\", regex=r\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\")\n\n# Constrain to date format (YYYY-MM-DD)\nlm += \"Date: \" + gen(\"date\", regex=r\"\\d{4}-\\d{2}-\\d{2}\")\n\n# Constrain to phone number\nlm += \"Phone: \" + gen(\"phone\", regex=r\"\\d{3}-\\d{3}-\\d{4}\")\n\nprint(lm[\"email\"])  # Guaranteed valid email\nprint(lm[\"date\"])   # Guaranteed YYYY-MM-DD format\n```\n\n**How it works:**\n- Regex converted to grammar at token level\n- Invalid tokens filtered during generation\n- Model can only produce matching outputs\n\n#### Selection Constraints\n\n```python\nfrom guidance import models, gen, select\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Constrain to specific choices\nlm += \"Sentiment: \" + select([\"positive\", \"negative\", \"neutral\"], name=\"sentiment\")\n\n# Multiple-choice selection\nlm += \"Best answer: \" + select(\n    [\"A) Paris\", \"B) London\", \"C) Berlin\", \"D) Madrid\"],\n    name=\"answer\"\n)\n\nprint(lm[\"sentiment\"])  # One of: positive, negative, neutral\nprint(lm[\"answer\"])     # One of: A, B, C, or D\n```\n\n### 3. Token Healing\n\nGuidance automatically \"heals\" token boundaries between prompt and generation.\n\n**Problem:** Tokenization creates unnatural boundaries.\n\n```python\n# Without token healing\nprompt = \"The capital of France is \"\n# Last token: \" is \"\n# First generated token might be \" Par\" (with leading space)\n# Result: \"The capital of France is  Paris\" (double space!)\n```\n\n**Solution:** Guidance backs up one token and regenerates.\n\n```python\nfrom guidance import models, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Token healing enabled by default\nlm += \"The capital of France is \" + gen(\"capital\", max_tokens=5)\n# Result: \"The capital of France is Paris\" (correct spacing)\n```\n\n**Benefits:**\n- Natural text boundaries\n- No awkward spacing issues\n- Better model performance (sees natural token sequences)\n\n### 4. Grammar-Based Generation\n\nDefine complex structures using context-free grammars.\n\n```python\nfrom guidance import models, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# JSON grammar (simplified)\njson_grammar = \"\"\"\n{\n    \"name\": \u003Cgen name regex=\"[A-Za-z ]+\" max_tokens=20>,\n    \"age\": \u003Cgen age regex=\"[0-9]+\" max_tokens=3>,\n    \"email\": \u003Cgen email regex=\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\\\.[a-zA-Z]{2,}\" max_tokens=50>\n}\n\"\"\"\n\n# Generate valid JSON\nlm += gen(\"person\", grammar=json_grammar)\n\nprint(lm[\"person\"])  # Guaranteed valid JSON structure\n```\n\n**Use cases:**\n- Complex structured outputs\n- Nested data structures\n- Programming language syntax\n- Domain-specific languages\n\n### 5. Guidance Functions\n\nCreate reusable generation patterns with the `@guidance` decorator.\n\n```python\nfrom guidance import guidance, gen, models\n\n@guidance\ndef generate_person(lm):\n    \"\"\"Generate a person with name and age.\"\"\"\n    lm += \"Name: \" + gen(\"name\", max_tokens=20, stop=\"\\n\")\n    lm += \"\\nAge: \" + gen(\"age\", regex=r\"[0-9]+\", max_tokens=3)\n    return lm\n\n# Use the function\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = generate_person(lm)\n\nprint(lm[\"name\"])\nprint(lm[\"age\"])\n```\n\n**Stateful Functions:**\n\n```python\n@guidance(stateless=False)\ndef react_agent(lm, question, tools, max_rounds=5):\n    \"\"\"ReAct agent with tool use.\"\"\"\n    lm += f\"Question: {question}\\n\\n\"\n\n    for i in range(max_rounds):\n        # Thought\n        lm += f\"Thought {i+1}: \" + gen(\"thought\", stop=\"\\n\")\n\n        # Action\n        lm += \"\\nAction: \" + select(list(tools.keys()), name=\"action\")\n\n        # Execute tool\n        tool_result = tools[lm[\"action\"]]()\n        lm += f\"\\nObservation: {tool_result}\\n\\n\"\n\n        # Check if done\n        lm += \"Done? \" + select([\"Yes\", \"No\"], name=\"done\")\n        if lm[\"done\"] == \"Yes\":\n            break\n\n    # Final answer\n    lm += \"\\nFinal Answer: \" + gen(\"answer\", max_tokens=100)\n    return lm\n```\n\n## Backend Configuration\n\n### Anthropic Claude\n\n```python\nfrom guidance import models\n\nlm = models.Anthropic(\n    model=\"claude-sonnet-4-5-20250929\",\n    api_key=\"your-api-key\"  # Or set ANTHROPIC_API_KEY env var\n)\n```\n\n### OpenAI\n\n```python\nlm = models.OpenAI(\n    model=\"gpt-4o-mini\",\n    api_key=\"your-api-key\"  # Or set OPENAI_API_KEY env var\n)\n```\n\n### Local Models (Transformers)\n\n```python\nfrom guidance.models import Transformers\n\nlm = Transformers(\n    \"microsoft\u002FPhi-4-mini-instruct\",\n    device=\"cuda\"  # Or \"cpu\"\n)\n```\n\n### Local Models (llama.cpp)\n\n```python\nfrom guidance.models import LlamaCpp\n\nlm = LlamaCpp(\n    model_path=\"\u002Fpath\u002Fto\u002Fmodel.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35\n)\n```\n\n## Common Patterns\n\n### Pattern 1: JSON Generation\n\n```python\nfrom guidance import models, gen, system, user, assistant\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\nwith system():\n    lm += \"You generate valid JSON.\"\n\nwith user():\n    lm += \"Generate a user profile with name, age, and email.\"\n\nwith assistant():\n    lm += \"\"\"{\n    \"name\": \"\"\" + gen(\"name\", regex=r'\"[A-Za-z ]+\"', max_tokens=30) + \"\"\",\n    \"age\": \"\"\" + gen(\"age\", regex=r\"[0-9]+\", max_tokens=3) + \"\"\",\n    \"email\": \"\"\" + gen(\"email\", regex=r'\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\"', max_tokens=50) + \"\"\"\n}\"\"\"\n\nprint(lm)  # Valid JSON guaranteed\n```\n\n### Pattern 2: Classification\n\n```python\nfrom guidance import models, gen, select\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\ntext = \"This product is amazing! I love it.\"\n\nlm += f\"Text: {text}\\n\"\nlm += \"Sentiment: \" + select([\"positive\", \"negative\", \"neutral\"], name=\"sentiment\")\nlm += \"\\nConfidence: \" + gen(\"confidence\", regex=r\"[0-9]+\", max_tokens=3) + \"%\"\n\nprint(f\"Sentiment: {lm['sentiment']}\")\nprint(f\"Confidence: {lm['confidence']}%\")\n```\n\n### Pattern 3: Multi-Step Reasoning\n\n```python\nfrom guidance import models, gen, guidance\n\n@guidance\ndef chain_of_thought(lm, question):\n    \"\"\"Generate answer with step-by-step reasoning.\"\"\"\n    lm += f\"Question: {question}\\n\\n\"\n\n    # Generate multiple reasoning steps\n    for i in range(3):\n        lm += f\"Step {i+1}: \" + gen(f\"step_{i+1}\", stop=\"\\n\", max_tokens=100) + \"\\n\"\n\n    # Final answer\n    lm += \"\\nTherefore, the answer is: \" + gen(\"answer\", max_tokens=50)\n\n    return lm\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = chain_of_thought(lm, \"What is 15% of 200?\")\n\nprint(lm[\"answer\"])\n```\n\n### Pattern 4: ReAct Agent\n\n```python\nfrom guidance import models, gen, select, guidance\n\n@guidance(stateless=False)\ndef react_agent(lm, question):\n    \"\"\"ReAct agent with tool use.\"\"\"\n    tools = {\n        \"calculator\": lambda expr: eval(expr),\n        \"search\": lambda query: f\"Search results for: {query}\",\n    }\n\n    lm += f\"Question: {question}\\n\\n\"\n\n    for round in range(5):\n        # Thought\n        lm += f\"Thought: \" + gen(\"thought\", stop=\"\\n\") + \"\\n\"\n\n        # Action selection\n        lm += \"Action: \" + select([\"calculator\", \"search\", \"answer\"], name=\"action\")\n\n        if lm[\"action\"] == \"answer\":\n            lm += \"\\nFinal Answer: \" + gen(\"answer\", max_tokens=100)\n            break\n\n        # Action input\n        lm += \"\\nAction Input: \" + gen(\"action_input\", stop=\"\\n\") + \"\\n\"\n\n        # Execute tool\n        if lm[\"action\"] in tools:\n            result = tools[lm[\"action\"]](lm[\"action_input\"])\n            lm += f\"Observation: {result}\\n\\n\"\n\n    return lm\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = react_agent(lm, \"What is 25 * 4 + 10?\")\nprint(lm[\"answer\"])\n```\n\n### Pattern 5: Data Extraction\n\n```python\nfrom guidance import models, gen, guidance\n\n@guidance\ndef extract_entities(lm, text):\n    \"\"\"Extract structured entities from text.\"\"\"\n    lm += f\"Text: {text}\\n\\n\"\n\n    # Extract person\n    lm += \"Person: \" + gen(\"person\", stop=\"\\n\", max_tokens=30) + \"\\n\"\n\n    # Extract organization\n    lm += \"Organization: \" + gen(\"organization\", stop=\"\\n\", max_tokens=30) + \"\\n\"\n\n    # Extract date\n    lm += \"Date: \" + gen(\"date\", regex=r\"\\d{4}-\\d{2}-\\d{2}\", max_tokens=10) + \"\\n\"\n\n    # Extract location\n    lm += \"Location: \" + gen(\"location\", stop=\"\\n\", max_tokens=30) + \"\\n\"\n\n    return lm\n\ntext = \"Tim Cook announced at Apple Park on 2024-09-15 in Cupertino.\"\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = extract_entities(lm, text)\n\nprint(f\"Person: {lm['person']}\")\nprint(f\"Organization: {lm['organization']}\")\nprint(f\"Date: {lm['date']}\")\nprint(f\"Location: {lm['location']}\")\n```\n\n## Best Practices\n\n### 1. Use Regex for Format Validation\n\n```python\n# ✅ Good: Regex ensures valid format\nlm += \"Email: \" + gen(\"email\", regex=r\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\")\n\n# ❌ Bad: Free generation may produce invalid emails\nlm += \"Email: \" + gen(\"email\", max_tokens=50)\n```\n\n### 2. Use select() for Fixed Categories\n\n```python\n# ✅ Good: Guaranteed valid category\nlm += \"Status: \" + select([\"pending\", \"approved\", \"rejected\"], name=\"status\")\n\n# ❌ Bad: May generate typos or invalid values\nlm += \"Status: \" + gen(\"status\", max_tokens=20)\n```\n\n### 3. Leverage Token Healing\n\n```python\n# Token healing is enabled by default\n# No special action needed - just concatenate naturally\nlm += \"The capital is \" + gen(\"capital\")  # Automatic healing\n```\n\n### 4. Use stop Sequences\n\n```python\n# ✅ Good: Stop at newline for single-line outputs\nlm += \"Name: \" + gen(\"name\", stop=\"\\n\")\n\n# ❌ Bad: May generate multiple lines\nlm += \"Name: \" + gen(\"name\", max_tokens=50)\n```\n\n### 5. Create Reusable Functions\n\n```python\n# ✅ Good: Reusable pattern\n@guidance\ndef generate_person(lm):\n    lm += \"Name: \" + gen(\"name\", stop=\"\\n\")\n    lm += \"\\nAge: \" + gen(\"age\", regex=r\"[0-9]+\")\n    return lm\n\n# Use multiple times\nlm = generate_person(lm)\nlm += \"\\n\\n\"\nlm = generate_person(lm)\n```\n\n### 6. Balance Constraints\n\n```python\n# ✅ Good: Reasonable constraints\nlm += gen(\"name\", regex=r\"[A-Za-z ]+\", max_tokens=30)\n\n# ❌ Too strict: May fail or be very slow\nlm += gen(\"name\", regex=r\"^(John|Jane)$\", max_tokens=10)\n```\n\n## Comparison to Alternatives\n\n| Feature | Guidance | Instructor | Outlines | LMQL |\n|---------|----------|------------|----------|------|\n| Regex Constraints | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |\n| Grammar Support | ✅ CFG | ❌ No | ✅ CFG | ✅ CFG |\n| Pydantic Validation | ❌ No | ✅ Yes | ✅ Yes | ❌ No |\n| Token Healing | ✅ Yes | ❌ No | ✅ Yes | ❌ No |\n| Local Models | ✅ Yes | ⚠️ Limited | ✅ Yes | ✅ Yes |\n| API Models | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes |\n| Pythonic Syntax | ✅ Yes | ✅ Yes | ✅ Yes | ❌ SQL-like |\n| Learning Curve | Low | Low | Medium | High |\n\n**When to choose Guidance:**\n- Need regex\u002Fgrammar constraints\n- Want token healing\n- Building complex workflows with control flow\n- Using local models (Transformers, llama.cpp)\n- Prefer Pythonic syntax\n\n**When to choose alternatives:**\n- Instructor: Need Pydantic validation with automatic retrying\n- Outlines: Need JSON schema validation\n- LMQL: Prefer declarative query syntax\n\n## Performance Characteristics\n\n**Latency Reduction:**\n- 30-50% faster than traditional prompting for constrained outputs\n- Token healing reduces unnecessary regeneration\n- Grammar constraints prevent invalid token generation\n\n**Memory Usage:**\n- Minimal overhead vs unconstrained generation\n- Grammar compilation cached after first use\n- Efficient token filtering at inference time\n\n**Token Efficiency:**\n- Prevents wasted tokens on invalid outputs\n- No need for retry loops\n- Direct path to valid outputs\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fguidance.readthedocs.io\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance (18k+ stars)\n- **Notebooks**: https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance\u002Ftree\u002Fmain\u002Fnotebooks\n- **Discord**: Community support available\n\n## See Also\n\n- `references\u002Fconstraints.md` - Comprehensive regex and grammar patterns\n- `references\u002Fbackends.md` - Backend-specific configuration\n- `references\u002Fexamples.md` - Production-ready examples\n\n\n","# Guidance: Constrained LLM Generation\n\n## When to Use This Skill\n\nUse Guidance when you need to:\n- **Control LLM output syntax** with regex or grammars\n- **Guarantee valid JSON\u002FXML\u002Fcode** generation\n- **Reduce latency** vs traditional prompting approaches\n- **Enforce structured formats** (dates, emails, IDs, etc.)\n- **Build multi-step workflows** with Pythonic control flow\n- **Prevent invalid outputs** through grammatical constraints\n\n**GitHub Stars**: 18,000+ | **From**: Microsoft Research\n\n## Installation\n\n```bash\n# Base installation\npip install guidance\n\n# With specific backends\npip install guidance[transformers]  # Hugging Face models\npip install guidance[llama_cpp]     # llama.cpp models\n```\n\n## Quick Start\n\n### Basic Example: Structured Generation\n\n```python\nfrom guidance import models, gen\n\n# Load model (supports OpenAI, Transformers, llama.cpp)\nlm = models.OpenAI(\"gpt-4\")\n\n# Generate with constraints\nresult = lm + \"The capital of France is \" + gen(\"capital\", max_tokens=5)\n\nprint(result[\"capital\"])  # \"Paris\"\n```\n\n### With Anthropic Claude\n\n```python\nfrom guidance import models, gen, system, user, assistant\n\n# Configure Claude\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Use context managers for chat format\nwith system():\n    lm += \"You are a helpful assistant.\"\n\nwith user():\n    lm += \"What is the capital of France?\"\n\nwith assistant():\n    lm += gen(max_tokens=20)\n```\n\n## Core Concepts\n\n### 1. Context Managers\n\nGuidance uses Pythonic context managers for chat-style interactions.\n\n```python\nfrom guidance import system, user, assistant, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# System message\nwith system():\n    lm += \"You are a JSON generation expert.\"\n\n# User message\nwith user():\n    lm += \"Generate a person object with name and age.\"\n\n# Assistant response\nwith assistant():\n    lm += gen(\"response\", max_tokens=100)\n\nprint(lm[\"response\"])\n```\n\n**Benefits:**\n- Natural chat flow\n- Clear role separation\n- Easy to read and maintain\n\n### 2. Constrained Generation\n\nGuidance ensures outputs match specified patterns using regex or grammars.\n\n#### Regex Constraints\n\n```python\nfrom guidance import models, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Constrain to valid email format\nlm += \"Email: \" + gen(\"email\", regex=r\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\")\n\n# Constrain to date format (YYYY-MM-DD)\nlm += \"Date: \" + gen(\"date\", regex=r\"\\d{4}-\\d{2}-\\d{2}\")\n\n# Constrain to phone number\nlm += \"Phone: \" + gen(\"phone\", regex=r\"\\d{3}-\\d{3}-\\d{4}\")\n\nprint(lm[\"email\"])  # Guaranteed valid email\nprint(lm[\"date\"])   # Guaranteed YYYY-MM-DD format\n```\n\n**How it works:**\n- Regex converted to grammar at token level\n- Invalid tokens filtered during generation\n- Model can only produce matching outputs\n\n#### Selection Constraints\n\n```python\nfrom guidance import models, gen, select\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Constrain to specific choices\nlm += \"Sentiment: \" + select([\"positive\", \"negative\", \"neutral\"], name=\"sentiment\")\n\n# Multiple-choice selection\nlm += \"Best answer: \" + select(\n    [\"A) Paris\", \"B) London\", \"C) Berlin\", \"D) Madrid\"],\n    name=\"answer\"\n)\n\nprint(lm[\"sentiment\"])  # One of: positive, negative, neutral\nprint(lm[\"answer\"])     # One of: A, B, C, or D\n```\n\n### 3. Token Healing\n\nGuidance automatically \"heals\" token boundaries between prompt and generation.\n\n**Problem:** Tokenization creates unnatural boundaries.\n\n```python\n# Without token healing\nprompt = \"The capital of France is \"\n# Last token: \" is \"\n# First generated token might be \" Par\" (with leading space)\n# Result: \"The capital of France is  Paris\" (double space!)\n```\n\n**Solution:** Guidance backs up one token and regenerates.\n\n```python\nfrom guidance import models, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# Token healing enabled by default\nlm += \"The capital of France is \" + gen(\"capital\", max_tokens=5)\n# Result: \"The capital of France is Paris\" (correct spacing)\n```\n\n**Benefits:**\n- Natural text boundaries\n- No awkward spacing issues\n- Better model performance (sees natural token sequences)\n\n### 4. Grammar-Based Generation\n\nDefine complex structures using context-free grammars.\n\n```python\nfrom guidance import models, gen\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\n# JSON grammar (simplified)\njson_grammar = \"\"\"\n{\n    \"name\": \u003Cgen name regex=\"[A-Za-z ]+\" max_tokens=20>,\n    \"age\": \u003Cgen age regex=\"[0-9]+\" max_tokens=3>,\n    \"email\": \u003Cgen email regex=\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\\\.[a-zA-Z]{2,}\" max_tokens=50>\n}\n\"\"\"\n\n# Generate valid JSON\nlm += gen(\"person\", grammar=json_grammar)\n\nprint(lm[\"person\"])  # Guaranteed valid JSON structure\n```\n\n**Use cases:**\n- Complex structured outputs\n- Nested data structures\n- Programming language syntax\n- Domain-specific languages\n\n### 5. Guidance Functions\n\nCreate reusable generation patterns with the `@guidance` decorator.\n\n```python\nfrom guidance import guidance, gen, models\n\n@guidance\ndef generate_person(lm):\n    \"\"\"Generate a person with name and age.\"\"\"\n    lm += \"Name: \" + gen(\"name\", max_tokens=20, stop=\"\\n\")\n    lm += \"\\nAge: \" + gen(\"age\", regex=r\"[0-9]+\", max_tokens=3)\n    return lm\n\n# Use the function\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = generate_person(lm)\n\nprint(lm[\"name\"])\nprint(lm[\"age\"])\n```\n\n**Stateful Functions:**\n\n```python\n@guidance(stateless=False)\ndef react_agent(lm, question, tools, max_rounds=5):\n    \"\"\"ReAct agent with tool use.\"\"\"\n    lm += f\"Question: {question}\\n\\n\"\n\n    for i in range(max_rounds):\n        # Thought\n        lm += f\"Thought {i+1}: \" + gen(\"thought\", stop=\"\\n\")\n\n        # Action\n        lm += \"\\nAction: \" + select(list(tools.keys()), name=\"action\")\n\n        # Execute tool\n        tool_result = tools[lm[\"action\"]]()\n        lm += f\"\\nObservation: {tool_result}\\n\\n\"\n\n        # Check if done\n        lm += \"Done? \" + select([\"Yes\", \"No\"], name=\"done\")\n        if lm[\"done\"] == \"Yes\":\n            break\n\n    # Final answer\n    lm += \"\\nFinal Answer: \" + gen(\"answer\", max_tokens=100)\n    return lm\n```\n\n## Backend Configuration\n\n### Anthropic Claude\n\n```python\nfrom guidance import models\n\nlm = models.Anthropic(\n    model=\"claude-sonnet-4-5-20250929\",\n    api_key=\"your-api-key\"  # Or set ANTHROPIC_API_KEY env var\n)\n```\n\n### OpenAI\n\n```python\nlm = models.OpenAI(\n    model=\"gpt-4o-mini\",\n    api_key=\"your-api-key\"  # Or set OPENAI_API_KEY env var\n)\n```\n\n### Local Models (Transformers)\n\n```python\nfrom guidance.models import Transformers\n\nlm = Transformers(\n    \"microsoft\u002FPhi-4-mini-instruct\",\n    device=\"cuda\"  # Or \"cpu\"\n)\n```\n\n### Local Models (llama.cpp)\n\n```python\nfrom guidance.models import LlamaCpp\n\nlm = LlamaCpp(\n    model_path=\"\u002Fpath\u002Fto\u002Fmodel.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35\n)\n```\n\n## Common Patterns\n\n### Pattern 1: JSON Generation\n\n```python\nfrom guidance import models, gen, system, user, assistant\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\nwith system():\n    lm += \"You generate valid JSON.\"\n\nwith user():\n    lm += \"Generate a user profile with name, age, and email.\"\n\nwith assistant():\n    lm += \"\"\"{\n    \"name\": \"\"\" + gen(\"name\", regex=r'\"[A-Za-z ]+\"', max_tokens=30) + \"\"\",\n    \"age\": \"\"\" + gen(\"age\", regex=r\"[0-9]+\", max_tokens=3) + \"\"\",\n    \"email\": \"\"\" + gen(\"email\", regex=r'\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\"', max_tokens=50) + \"\"\"\n}\"\"\"\n\nprint(lm)  # Valid JSON guaranteed\n```\n\n### Pattern 2: Classification\n\n```python\nfrom guidance import models, gen, select\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\n\ntext = \"This product is amazing! I love it.\"\n\nlm += f\"Text: {text}\\n\"\nlm += \"Sentiment: \" + select([\"positive\", \"negative\", \"neutral\"], name=\"sentiment\")\nlm += \"\\nConfidence: \" + gen(\"confidence\", regex=r\"[0-9]+\", max_tokens=3) + \"%\"\n\nprint(f\"Sentiment: {lm['sentiment']}\")\nprint(f\"Confidence: {lm['confidence']}%\")\n```\n\n### Pattern 3: Multi-Step Reasoning\n\n```python\nfrom guidance import models, gen, guidance\n\n@guidance\ndef chain_of_thought(lm, question):\n    \"\"\"Generate answer with step-by-step reasoning.\"\"\"\n    lm += f\"Question: {question}\\n\\n\"\n\n    # Generate multiple reasoning steps\n    for i in range(3):\n        lm += f\"Step {i+1}: \" + gen(f\"step_{i+1}\", stop=\"\\n\", max_tokens=100) + \"\\n\"\n\n    # Final answer\n    lm += \"\\nTherefore, the answer is: \" + gen(\"answer\", max_tokens=50)\n\n    return lm\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = chain_of_thought(lm, \"What is 15% of 200?\")\n\nprint(lm[\"answer\"])\n```\n\n### Pattern 4: ReAct Agent\n\n```python\nfrom guidance import models, gen, select, guidance\n\n@guidance(stateless=False)\ndef react_agent(lm, question):\n    \"\"\"ReAct agent with tool use.\"\"\"\n    tools = {\n        \"calculator\": lambda expr: eval(expr),\n        \"search\": lambda query: f\"Search results for: {query}\",\n    }\n\n    lm += f\"Question: {question}\\n\\n\"\n\n    for round in range(5):\n        # Thought\n        lm += f\"Thought: \" + gen(\"thought\", stop=\"\\n\") + \"\\n\"\n\n        # Action selection\n        lm += \"Action: \" + select([\"calculator\", \"search\", \"answer\"], name=\"action\")\n\n        if lm[\"action\"] == \"answer\":\n            lm += \"\\nFinal Answer: \" + gen(\"answer\", max_tokens=100)\n            break\n\n        # Action input\n        lm += \"\\nAction Input: \" + gen(\"action_input\", stop=\"\\n\") + \"\\n\"\n\n        # Execute tool\n        if lm[\"action\"] in tools:\n            result = tools[lm[\"action\"]](lm[\"action_input\"])\n            lm += f\"Observation: {result}\\n\\n\"\n\n    return lm\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = react_agent(lm, \"What is 25 * 4 + 10?\")\nprint(lm[\"answer\"])\n```\n\n### Pattern 5: Data Extraction\n\n```python\nfrom guidance import models, gen, guidance\n\n@guidance\ndef extract_entities(lm, text):\n    \"\"\"Extract structured entities from text.\"\"\"\n    lm += f\"Text: {text}\\n\\n\"\n\n    # Extract person\n    lm += \"Person: \" + gen(\"person\", stop=\"\\n\", max_tokens=30) + \"\\n\"\n\n    # Extract organization\n    lm += \"Organization: \" + gen(\"organization\", stop=\"\\n\", max_tokens=30) + \"\\n\"\n\n    # Extract date\n    lm += \"Date: \" + gen(\"date\", regex=r\"\\d{4}-\\d{2}-\\d{2}\", max_tokens=10) + \"\\n\"\n\n    # Extract location\n    lm += \"Location: \" + gen(\"location\", stop=\"\\n\", max_tokens=30) + \"\\n\"\n\n    return lm\n\ntext = \"Tim Cook announced at Apple Park on 2024-09-15 in Cupertino.\"\n\nlm = models.Anthropic(\"claude-sonnet-4-5-20250929\")\nlm = extract_entities(lm, text)\n\nprint(f\"Person: {lm['person']}\")\nprint(f\"Organization: {lm['organization']}\")\nprint(f\"Date: {lm['date']}\")\nprint(f\"Location: {lm['location']}\")\n```\n\n## Best Practices\n\n### 1. Use Regex for Format Validation\n\n```python\n# ✅ Good: Regex ensures valid format\nlm += \"Email: \" + gen(\"email\", regex=r\"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}\")\n\n# ❌ Bad: Free generation may produce invalid emails\nlm += \"Email: \" + gen(\"email\", max_tokens=50)\n```\n\n### 2. Use select() for Fixed Categories\n\n```python\n# ✅ Good: Guaranteed valid category\nlm += \"Status: \" + select([\"pending\", \"approved\", \"rejected\"], name=\"status\")\n\n# ❌ Bad: May generate typos or invalid values\nlm += \"Status: \" + gen(\"status\", max_tokens=20)\n```\n\n### 3. Leverage Token Healing\n\n```python\n# Token healing is enabled by default\n# No special action needed - just concatenate naturally\nlm += \"The capital is \" + gen(\"capital\")  # Automatic healing\n```\n\n### 4. Use stop Sequences\n\n```python\n# ✅ Good: Stop at newline for single-line outputs\nlm += \"Name: \" + gen(\"name\", stop=\"\\n\")\n\n# ❌ Bad: May generate multiple lines\nlm += \"Name: \" + gen(\"name\", max_tokens=50)\n```\n\n### 5. Create Reusable Functions\n\n```python\n# ✅ Good: Reusable pattern\n@guidance\ndef generate_person(lm):\n    lm += \"Name: \" + gen(\"name\", stop=\"\\n\")\n    lm += \"\\nAge: \" + gen(\"age\", regex=r\"[0-9]+\")\n    return lm\n\n# Use multiple times\nlm = generate_person(lm)\nlm += \"\\n\\n\"\nlm = generate_person(lm)\n```\n\n### 6. Balance Constraints\n\n```python\n# ✅ Good: Reasonable constraints\nlm += gen(\"name\", regex=r\"[A-Za-z ]+\", max_tokens=30)\n\n# ❌ Too strict: May fail or be very slow\nlm += gen(\"name\", regex=r\"^(John|Jane)$\", max_tokens=10)\n```\n\n## Comparison to Alternatives\n\n| Feature | Guidance | Instructor | Outlines | LMQL |\n|---------|----------|------------|----------|------|\n| Regex Constraints | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |\n| Grammar Support | ✅ CFG | ❌ No | ✅ CFG | ✅ CFG |\n| Pydantic Validation | ❌ No | ✅ Yes | ✅ Yes | ❌ No |\n| Token Healing | ✅ Yes | ❌ No | ✅ Yes | ❌ No |\n| Local Models | ✅ Yes | ⚠️ Limited | ✅ Yes | ✅ Yes |\n| API Models | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes |\n| Pythonic Syntax | ✅ Yes | ✅ Yes | ✅ Yes | ❌ SQL-like |\n| Learning Curve | Low | Low | Medium | High |\n\n**When to choose Guidance:**\n- Need regex\u002Fgrammar constraints\n- Want token healing\n- Building complex workflows with control flow\n- Using local models (Transformers, llama.cpp)\n- Prefer Pythonic syntax\n\n**When to choose alternatives:**\n- Instructor: Need Pydantic validation with automatic retrying\n- Outlines: Need JSON schema validation\n- LMQL: Prefer declarative query syntax\n\n## Performance Characteristics\n\n**Latency Reduction:**\n- 30-50% faster than traditional prompting for constrained outputs\n- Token healing reduces unnecessary regeneration\n- Grammar constraints prevent invalid token generation\n\n**Memory Usage:**\n- Minimal overhead vs unconstrained generation\n- Grammar compilation cached after first use\n- Efficient token filtering at inference time\n\n**Token Efficiency:**\n- Prevents wasted tokens on invalid outputs\n- No need for retry loops\n- Direct path to valid outputs\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fguidance.readthedocs.io\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance (18k+ stars)\n- **Notebooks**: https:\u002F\u002Fgithub.com\u002Fguidance-ai\u002Fguidance\u002Ftree\u002Fmain\u002Fnotebooks\n- **Discord**: Community support available\n\n## See Also\n\n- `references\u002Fconstraints.md` - Comprehensive regex and grammar patterns\n- `references\u002Fbackends.md` - Backend-specific configuration\n- `references\u002Fexamples.md` - Production-ready examples\n\n\n",{"name":5,"license":15,"metadata":56,"description":7},{"tags":57,"author":65,"version":66,"dependencies":67},[58,59,36,60,35,61,62,63,64],"Prompt Engineering","Guidance","Structured Output","Grammar","Microsoft Research","Format Enforcement","Multi-Step Workflows","Orchestra Research","1.0.0",[27,68],"transformers","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-guidance",3931,"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","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":80,"prompt":90,"zipAvailable":91},[81,84,87],{"tool":82,"command":83},"npx","npx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-guidance",{"tool":85,"command":86},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-guidance",{"tool":88,"command":89},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-guidance","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-guidance\u002FSKILL.md",true,[93,99,103],{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fbackends.md","REFERENCE","参考文档",12010,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-guidance\u002Freferences\u002Fbackends.md",{"path":100,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fconstraints.md",15886,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-guidance\u002Freferences\u002Fconstraints.md",{"path":104,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fexamples.md",20079,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-guidance\u002Freferences\u002Fexamples.md",[108,136,162,195,230,254],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":115,"publishedAt":51},"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",[114],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":143,"publishedAt":51},"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",[142],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[144,145,146,147,148,149,150,151,152,153,154,125,46,155,156,157,158,159,160,161],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":177,"publishedAt":51},"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",[168,171,172],{"categoryNo":169,"categoryName":170,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":173,"categoryName":174,"taxonomyType":175,"taxonomyTypeDesc":176},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[178,179,180,181,182,183,184,121,185,186,187,188,189,190,191,124,192,193,125,194],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":200,"sourceHtmlUrl":201,"categories":202,"keywords":206,"publishedAt":51},"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",[203],{"categoryNo":204,"categoryName":205,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,121,229],"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":231,"name":232,"nameZh":6,"description":233,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":234,"sourceHtmlUrl":235,"categories":236,"keywords":241,"publishedAt":51},"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",[237,240],{"categoryNo":238,"categoryName":239,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":204,"categoryName":205,"taxonomyType":21,"taxonomyTypeDesc":22},[242,243,244,245,246,208,247,217,218,248,249,250,251,121,252,127,253],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":255,"name":256,"nameZh":6,"description":257,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":199,"licenseFileUrl":258,"sourceHtmlUrl":259,"categories":260,"keywords":268,"publishedAt":51},"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",[261,264,265],{"categoryNo":262,"categoryName":263,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":266,"categoryName":267,"taxonomyType":175,"taxonomyTypeDesc":176},"01JBSEEDCAT000000000000036","内容创作者",[269,270,271,272,273,274,275,276,210,277,220,46,278,279,280,281,282,190,283,125,284,285,286,287,288,133,289],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]