[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KNCF41BS0SWD55KP0CEP8F":3,"skill-same:01M6KNCF41BS0SWD55KP0CEP8F":90},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M6KNCF41BS0SWD55KP0CEP8F","speckit-checklist",null,"Generate a custom checklist for the current feature based on user requirements.","AI-native-Systems-Research\u002Fai-native-storage-certus","AI-native-Systems-Research","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F270755458?v=4","01M6KKQWSJX0D4X33E0KKSR4WM","AI-native Systems Research","01M6KKQWXTKR6MYVQMPKVJ5WD5",3,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FAI-native-Systems-Research\u002Fai-native-storage-certus\u002Fblob\u002Funstable\u002Fcomponents\u002Feviction-policy-session-lists\u002F.claude\u002Fskills\u002Fspeckit-checklist\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"speckit checklist","checklist","speckit","requirements","custom","for","true","kit","with","spec-kit","false","feature","github","project","focus","domain","structure","specify","generate","project structure","2026-11-10 10:12:30",false,"---\r\nname: openclaw-search\r\ndescription: \"Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.\"\r\nhomepage: https:\u002F\u002Fopenclaw.ai\r\nmetadata: {\"openclaw\":{\"emoji\":\"?\",\"requires\":{\"bins\":[\"curl\",\"python3\"],\"env\":[\"AISA_API_KEY\"]},\"primaryEnv\":\"AISA_API_KEY\"}}\r\n---\r\n\r\n# OpenClaw Search ?\r\n\r\n**Intelligent search for autonomous agents. Powered by AIsa.**\r\n\r\nOne API key. Multi-source retrieval. Confidence-scored answers.\r\n\r\n> Inspired by [AIsa Verity](https:\u002F\u002Fgithub.com\u002FAIsa-team\u002Fverity) - A next-generation search agent with trust-scored answers.\r\n\r\n## ? What Can You Do?\r\n\r\n### Research Assistant\r\n```\r\n\"Search for the latest papers on transformer architectures from 2024-2025\"\r\n```\r\n\r\n### Market Research\r\n```\r\n\"Find all web articles about AI startup funding in Q4 2025\"\r\n```\r\n\r\n### Competitive Analysis\r\n```\r\n\"Search for reviews and comparisons of RAG frameworks\"\r\n```\r\n\r\n### News Aggregation\r\n```\r\n\"Get the latest news about quantum computing breakthroughs\"\r\n```\r\n\r\n### Deep Dive Research\r\n```\r\n\"Smart search combining web and academic sources on 'autonomous agents'\"\r\n```\r\n\r\n## Quick Start\r\n\r\n```bash\r\nexport AISA_API_KEY=\"your-key\"\r\n```\r\n\r\n---\r\n\r\n## ?️ Architecture: Multi-Stage Orchestration\r\n\r\nOpenClaw Search employs a **Two-Phase Retrieval Strategy** for comprehensive results:\r\n\r\n### Phase 1: Discovery (Parallel Retrieval)\r\n\r\nQuery 4 distinct search streams simultaneously:\r\n- **Scholar**: Deep academic retrieval\r\n- **Web**: Structured web search\r\n- **Smart**: Intelligent mixed-mode search\r\n- **Tavily**: External validation signal\r\n\r\n### Phase 2: Reasoning (Meta-Analysis)\r\n\r\nUse **AIsa Explain** to perform meta-analysis on search results, generating:\r\n- Confidence scores (0-100)\r\n- Source agreement analysis\r\n- Synthesized answers\r\n\r\n```\r\n┌─────────────────────────────────────────────────────────────┐\r\n│                      User Query                              │\r\n└─────────────────────────────────────────────────────────────┘\r\n                              │\r\n              ┌───────────────┼───────────────┐\r\n              ▼               ▼               ▼\r\n        ┌─────────┐     ┌─────────┐     ┌─────────┐\r\n        │ Scholar │     │   Web   │     │  Smart  │\r\n        └─────────┘     └─────────┘     └─────────┘\r\n              │               │               │\r\n              └───────────────┼───────────────┘\r\n                              ▼\r\n                    ┌─────────────────┐\r\n                    │  AIsa Explain   │\r\n                    │ (Meta-Analysis) │\r\n                    └─────────────────┘\r\n                              │\r\n                              ▼\r\n                    ┌─────────────────┐\r\n                    │ Confidence Score│\r\n                    │  + Synthesis    │\r\n                    └─────────────────┘\r\n```\r\n\r\n---\r\n\r\n## Core Capabilities\r\n\r\n### Web Search\r\n\r\n```bash\r\n# Basic web search\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fweb?query=AI+frameworks&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n\r\n# Full text search (with page content)\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fsearch\u002Ffull?query=latest+AI+news&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n```\r\n\r\n### Academic\u002FScholar Search\r\n\r\n```bash\r\n# Search academic papers\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fscholar?query=transformer+models&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n\r\n# With year filter\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fscholar?query=LLM&max_num_results=10&as_ylo=2024&as_yhi=2025\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n```\r\n\r\n### Smart Search (Web + Academic Combined)\r\n\r\n```bash\r\n# Intelligent hybrid search\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fsmart?query=machine+learning+optimization&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n```\r\n\r\n### Tavily Integration (Advanced)\r\n\r\n```bash\r\n# Tavily search\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fsearch\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"query\":\"latest AI developments\"}'\r\n\r\n# Extract content from URLs\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fextract\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"urls\":[\"https:\u002F\u002Fexample.com\u002Farticle\"]}'\r\n\r\n# Crawl web pages\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fcrawl\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"url\":\"https:\u002F\u002Fexample.com\",\"max_depth\":2}'\r\n\r\n# Site map\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fmap\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"url\":\"https:\u002F\u002Fexample.com\"}'\r\n```\r\n\r\n### Explain Search Results (Meta-Analysis)\r\n\r\n```bash\r\n# Generate explanations with confidence scoring\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fexplain\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"results\":[...],\"language\":\"en\",\"format\":\"summary\"}'\r\n```\r\n\r\n---\r\n\r\n## ? Confidence Scoring Engine\r\n\r\nUnlike standard RAG systems, OpenClaw Search evaluates credibility and consensus:\r\n\r\n### Scoring Rubric\r\n\r\n| Factor | Weight | Description |\r\n|--------|--------|-------------|\r\n| **Source Quality** | 40% | Academic > Smart\u002FWeb > External |\r\n| **Agreement Analysis** | 35% | Cross-source consensus checking |\r\n| **Recency** | 15% | Newer sources weighted higher |\r\n| **Relevance** | 10% | Query-result semantic match |\r\n\r\n### Score Interpretation\r\n\r\n| Score | Confidence Level | Meaning |\r\n|-------|-----------------|---------|\r\n| 90-100 | Very High | Strong consensus across academic and web sources |\r\n| 70-89 | High | Good agreement, reliable sources |\r\n| 50-69 | Medium | Mixed signals, verify independently |\r\n| 30-49 | Low | Conflicting sources, use caution |\r\n| 0-29 | Very Low | Insufficient or contradictory data |\r\n\r\n---\r\n\r\n## Python Client\r\n\r\n```bash\r\n# Web search\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py web --query \"latest AI news\" --count 10\r\n\r\n# Academic search\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py scholar --query \"transformer architecture\" --count 10\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py scholar --query \"LLM\" --year-from 2024 --year-to 2025\r\n\r\n# Smart search (web + academic)\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py smart --query \"autonomous agents\" --count 10\r\n\r\n# Full text search\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py full --query \"AI startup funding\"\r\n\r\n# Tavily operations\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py tavily-search --query \"AI developments\"\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py tavily-extract --urls \"https:\u002F\u002Fexample.com\u002Farticle\"\r\n\r\n# Multi-source search with confidence scoring\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py verity --query \"Is quantum computing ready for enterprise?\"\r\n```\r\n\r\n---\r\n\r\n## API Endpoints Reference\r\n\r\n| Endpoint | Method | Description |\r\n|----------|--------|-------------|\r\n| `\u002Fscholar\u002Fsearch\u002Fweb` | POST | Web search with structured results |\r\n| `\u002Fscholar\u002Fsearch\u002Fscholar` | POST | Academic paper search |\r\n| `\u002Fscholar\u002Fsearch\u002Fsmart` | POST | Intelligent hybrid search |\r\n| `\u002Fscholar\u002Fexplain` | POST | Generate result explanations |\r\n| `\u002Fsearch\u002Ffull` | POST | Full text search with content |\r\n| `\u002Fsearch\u002Fsmart` | POST | Smart web search |\r\n| `\u002Ftavily\u002Fsearch` | POST | Tavily search integration |\r\n| `\u002Ftavily\u002Fextract` | POST | Extract content from URLs |\r\n| `\u002Ftavily\u002Fcrawl` | POST | Crawl web pages |\r\n| `\u002Ftavily\u002Fmap` | POST | Generate site maps |\r\n\r\n---\r\n\r\n## Search Parameters\r\n\r\n| Parameter | Type | Description |\r\n|-----------|------|-------------|\r\n| query | string | Search query (required) |\r\n| max_num_results | integer | Max results (1-100, default 10) |\r\n| as_ylo | integer | Year lower bound (scholar only) |\r\n| as_yhi | integer | Year upper bound (scholar only) |\r\n\r\n---\r\n\r\n## ? Building a Verity-Style Agent\r\n\r\nWant to build your own confidence-scored search agent? Here's the pattern:\r\n\r\n### 1. Parallel Discovery\r\n\r\n```python\r\nimport asyncio\r\n\r\nasync def discover(query):\r\n    \"\"\"Phase 1: Parallel retrieval from multiple sources.\"\"\"\r\n    tasks = [\r\n        search_scholar(query),\r\n        search_web(query),\r\n        search_smart(query),\r\n        search_tavily(query)\r\n    ]\r\n    results = await asyncio.gather(*tasks)\r\n    return {\r\n        \"scholar\": results[0],\r\n        \"web\": results[1],\r\n        \"smart\": results[2],\r\n        \"tavily\": results[3]\r\n    }\r\n```\r\n\r\n### 2. Confidence Scoring\r\n\r\n```python\r\ndef score_confidence(results):\r\n    \"\"\"Calculate deterministic confidence score.\"\"\"\r\n    score = 0\r\n    \r\n    # Source quality (40%)\r\n    if results[\"scholar\"]:\r\n        score += 40 * len(results[\"scholar\"]) \u002F 10\r\n    \r\n    # Agreement analysis (35%)\r\n    claims = extract_claims(results)\r\n    agreement = analyze_agreement(claims)\r\n    score += 35 * agreement\r\n    \r\n    # Recency (15%)\r\n    recency = calculate_recency(results)\r\n    score += 15 * recency\r\n    \r\n    # Relevance (10%)\r\n    relevance = calculate_relevance(results, query)\r\n    score += 10 * relevance\r\n    \r\n    return min(100, score)\r\n```\r\n\r\n### 3. Synthesis\r\n\r\n```python\r\nasync def synthesize(query, results, score):\r\n    \"\"\"Generate final answer with citations.\"\"\"\r\n    explanation = await explain_results(results)\r\n    return {\r\n        \"answer\": explanation[\"summary\"],\r\n        \"confidence\": score,\r\n        \"sources\": explanation[\"citations\"],\r\n        \"claims\": explanation[\"claims\"]\r\n    }\r\n```\r\n\r\nFor a complete implementation, see [AIsa Verity](https:\u002F\u002Fgithub.com\u002FAIsa-team\u002Fverity).\r\n\r\n---\r\n\r\n## Pricing\r\n\r\n| API | Cost |\r\n|-----|------|\r\n| Web search | ~$0.001 |\r\n| Scholar search | ~$0.002 |\r\n| Smart search | ~$0.002 |\r\n| Tavily search | ~$0.002 |\r\n| Explain | ~$0.003 |\r\n\r\nEvery response includes `usage.cost` and `usage.credits_remaining`.\r\n\r\n---\r\n\r\n## Get Started\r\n\r\n1. Sign up at [aisa.one](https:\u002F\u002Faisa.one)\r\n2. Get your API key\r\n3. Add credits (pay-as-you-go)\r\n4. Set environment variable: `export AISA_API_KEY=\"your-key\"`\r\n\r\n## Full API Reference\r\n\r\nSee [API Reference](https:\u002F\u002Faisa.mintlify.app\u002Fapi-reference\u002Fintroduction) for complete endpoint documentation.\r\n\r\n## Resources\r\n\r\n- [AIsa Verity](https:\u002F\u002Fgithub.com\u002FAIsa-team\u002Fverity) - Reference implementation of confidence-scored search agent\r\n- [AIsa Documentation](https:\u002F\u002Faisa.mintlify.app) - Complete API documentation\r\n","# OpenClaw Search ?\r\n\r\n**Intelligent search for autonomous agents. Powered by AIsa.**\r\n\r\nOne API key. Multi-source retrieval. Confidence-scored answers.\r\n\r\n> Inspired by [AIsa Verity](https:\u002F\u002Fgithub.com\u002FAIsa-team\u002Fverity) - A next-generation search agent with trust-scored answers.\r\n\r\n## ? What Can You Do?\r\n\r\n### Research Assistant\r\n```\r\n\"Search for the latest papers on transformer architectures from 2024-2025\"\r\n```\r\n\r\n### Market Research\r\n```\r\n\"Find all web articles about AI startup funding in Q4 2025\"\r\n```\r\n\r\n### Competitive Analysis\r\n```\r\n\"Search for reviews and comparisons of RAG frameworks\"\r\n```\r\n\r\n### News Aggregation\r\n```\r\n\"Get the latest news about quantum computing breakthroughs\"\r\n```\r\n\r\n### Deep Dive Research\r\n```\r\n\"Smart search combining web and academic sources on 'autonomous agents'\"\r\n```\r\n\r\n## Quick Start\r\n\r\n```bash\r\nexport AISA_API_KEY=\"your-key\"\r\n```\r\n\r\n---\r\n\r\n## ?️ Architecture: Multi-Stage Orchestration\r\n\r\nOpenClaw Search employs a **Two-Phase Retrieval Strategy** for comprehensive results:\r\n\r\n### Phase 1: Discovery (Parallel Retrieval)\r\n\r\nQuery 4 distinct search streams simultaneously:\r\n- **Scholar**: Deep academic retrieval\r\n- **Web**: Structured web search\r\n- **Smart**: Intelligent mixed-mode search\r\n- **Tavily**: External validation signal\r\n\r\n### Phase 2: Reasoning (Meta-Analysis)\r\n\r\nUse **AIsa Explain** to perform meta-analysis on search results, generating:\r\n- Confidence scores (0-100)\r\n- Source agreement analysis\r\n- Synthesized answers\r\n\r\n```\r\n┌─────────────────────────────────────────────────────────────┐\r\n│                      User Query                              │\r\n└─────────────────────────────────────────────────────────────┘\r\n                              │\r\n              ┌───────────────┼───────────────┐\r\n              ▼               ▼               ▼\r\n        ┌─────────┐     ┌─────────┐     ┌─────────┐\r\n        │ Scholar │     │   Web   │     │  Smart  │\r\n        └─────────┘     └─────────┘     └─────────┘\r\n              │               │               │\r\n              └───────────────┼───────────────┘\r\n                              ▼\r\n                    ┌─────────────────┐\r\n                    │  AIsa Explain   │\r\n                    │ (Meta-Analysis) │\r\n                    └─────────────────┘\r\n                              │\r\n                              ▼\r\n                    ┌─────────────────┐\r\n                    │ Confidence Score│\r\n                    │  + Synthesis    │\r\n                    └─────────────────┘\r\n```\r\n\r\n---\r\n\r\n## Core Capabilities\r\n\r\n### Web Search\r\n\r\n```bash\r\n# Basic web search\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fweb?query=AI+frameworks&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n\r\n# Full text search (with page content)\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fsearch\u002Ffull?query=latest+AI+news&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n```\r\n\r\n### Academic\u002FScholar Search\r\n\r\n```bash\r\n# Search academic papers\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fscholar?query=transformer+models&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n\r\n# With year filter\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fscholar?query=LLM&max_num_results=10&as_ylo=2024&as_yhi=2025\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n```\r\n\r\n### Smart Search (Web + Academic Combined)\r\n\r\n```bash\r\n# Intelligent hybrid search\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fsearch\u002Fsmart?query=machine+learning+optimization&max_num_results=10\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\"\r\n```\r\n\r\n### Tavily Integration (Advanced)\r\n\r\n```bash\r\n# Tavily search\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fsearch\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"query\":\"latest AI developments\"}'\r\n\r\n# Extract content from URLs\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fextract\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"urls\":[\"https:\u002F\u002Fexample.com\u002Farticle\"]}'\r\n\r\n# Crawl web pages\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fcrawl\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"url\":\"https:\u002F\u002Fexample.com\",\"max_depth\":2}'\r\n\r\n# Site map\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Ftavily\u002Fmap\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"url\":\"https:\u002F\u002Fexample.com\"}'\r\n```\r\n\r\n### Explain Search Results (Meta-Analysis)\r\n\r\n```bash\r\n# Generate explanations with confidence scoring\r\ncurl -X POST \"https:\u002F\u002Fapi.aisa.one\u002Fapis\u002Fv1\u002Fscholar\u002Fexplain\" \\\r\n  -H \"Authorization: Bearer $AISA_API_KEY\" \\\r\n  -H \"Content-Type: application\u002Fjson\" \\\r\n  -d '{\"results\":[...],\"language\":\"en\",\"format\":\"summary\"}'\r\n```\r\n\r\n---\r\n\r\n## ? Confidence Scoring Engine\r\n\r\nUnlike standard RAG systems, OpenClaw Search evaluates credibility and consensus:\r\n\r\n### Scoring Rubric\r\n\r\n| Factor | Weight | Description |\r\n|--------|--------|-------------|\r\n| **Source Quality** | 40% | Academic > Smart\u002FWeb > External |\r\n| **Agreement Analysis** | 35% | Cross-source consensus checking |\r\n| **Recency** | 15% | Newer sources weighted higher |\r\n| **Relevance** | 10% | Query-result semantic match |\r\n\r\n### Score Interpretation\r\n\r\n| Score | Confidence Level | Meaning |\r\n|-------|-----------------|---------|\r\n| 90-100 | Very High | Strong consensus across academic and web sources |\r\n| 70-89 | High | Good agreement, reliable sources |\r\n| 50-69 | Medium | Mixed signals, verify independently |\r\n| 30-49 | Low | Conflicting sources, use caution |\r\n| 0-29 | Very Low | Insufficient or contradictory data |\r\n\r\n---\r\n\r\n## Python Client\r\n\r\n```bash\r\n# Web search\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py web --query \"latest AI news\" --count 10\r\n\r\n# Academic search\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py scholar --query \"transformer architecture\" --count 10\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py scholar --query \"LLM\" --year-from 2024 --year-to 2025\r\n\r\n# Smart search (web + academic)\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py smart --query \"autonomous agents\" --count 10\r\n\r\n# Full text search\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py full --query \"AI startup funding\"\r\n\r\n# Tavily operations\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py tavily-search --query \"AI developments\"\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py tavily-extract --urls \"https:\u002F\u002Fexample.com\u002Farticle\"\r\n\r\n# Multi-source search with confidence scoring\r\npython3 {baseDir}\u002Fscripts\u002Fsearch_client.py verity --query \"Is quantum computing ready for enterprise?\"\r\n```\r\n\r\n---\r\n\r\n## API Endpoints Reference\r\n\r\n| Endpoint | Method | Description |\r\n|----------|--------|-------------|\r\n| `\u002Fscholar\u002Fsearch\u002Fweb` | POST | Web search with structured results |\r\n| `\u002Fscholar\u002Fsearch\u002Fscholar` | POST | Academic paper search |\r\n| `\u002Fscholar\u002Fsearch\u002Fsmart` | POST | Intelligent hybrid search |\r\n| `\u002Fscholar\u002Fexplain` | POST | Generate result explanations |\r\n| `\u002Fsearch\u002Ffull` | POST | Full text search with content |\r\n| `\u002Fsearch\u002Fsmart` | POST | Smart web search |\r\n| `\u002Ftavily\u002Fsearch` | POST | Tavily search integration |\r\n| `\u002Ftavily\u002Fextract` | POST | Extract content from URLs |\r\n| `\u002Ftavily\u002Fcrawl` | POST | Crawl web pages |\r\n| `\u002Ftavily\u002Fmap` | POST | Generate site maps |\r\n\r\n---\r\n\r\n## Search Parameters\r\n\r\n| Parameter | Type | Description |\r\n|-----------|------|-------------|\r\n| query | string | Search query (required) |\r\n| max_num_results | integer | Max results (1-100, default 10) |\r\n| as_ylo | integer | Year lower bound (scholar only) |\r\n| as_yhi | integer | Year upper bound (scholar only) |\r\n\r\n---\r\n\r\n## ? Building a Verity-Style Agent\r\n\r\nWant to build your own confidence-scored search agent? Here's the pattern:\r\n\r\n### 1. Parallel Discovery\r\n\r\n```python\r\nimport asyncio\r\n\r\nasync def discover(query):\r\n    \"\"\"Phase 1: Parallel retrieval from multiple sources.\"\"\"\r\n    tasks = [\r\n        search_scholar(query),\r\n        search_web(query),\r\n        search_smart(query),\r\n        search_tavily(query)\r\n    ]\r\n    results = await asyncio.gather(*tasks)\r\n    return {\r\n        \"scholar\": results[0],\r\n        \"web\": results[1],\r\n        \"smart\": results[2],\r\n        \"tavily\": results[3]\r\n    }\r\n```\r\n\r\n### 2. Confidence Scoring\r\n\r\n```python\r\ndef score_confidence(results):\r\n    \"\"\"Calculate deterministic confidence score.\"\"\"\r\n    score = 0\r\n    \r\n    # Source quality (40%)\r\n    if results[\"scholar\"]:\r\n        score += 40 * len(results[\"scholar\"]) \u002F 10\r\n    \r\n    # Agreement analysis (35%)\r\n    claims = extract_claims(results)\r\n    agreement = analyze_agreement(claims)\r\n    score += 35 * agreement\r\n    \r\n    # Recency (15%)\r\n    recency = calculate_recency(results)\r\n    score += 15 * recency\r\n    \r\n    # Relevance (10%)\r\n    relevance = calculate_relevance(results, query)\r\n    score += 10 * relevance\r\n    \r\n    return min(100, score)\r\n```\r\n\r\n### 3. Synthesis\r\n\r\n```python\r\nasync def synthesize(query, results, score):\r\n    \"\"\"Generate final answer with citations.\"\"\"\r\n    explanation = await explain_results(results)\r\n    return {\r\n        \"answer\": explanation[\"summary\"],\r\n        \"confidence\": score,\r\n        \"sources\": explanation[\"citations\"],\r\n        \"claims\": explanation[\"claims\"]\r\n    }\r\n```\r\n\r\nFor a complete implementation, see [AIsa Verity](https:\u002F\u002Fgithub.com\u002FAIsa-team\u002Fverity).\r\n\r\n---\r\n\r\n## Pricing\r\n\r\n| API | Cost |\r\n|-----|------|\r\n| Web search | ~$0.001 |\r\n| Scholar search | ~$0.002 |\r\n| Smart search | ~$0.002 |\r\n| Tavily search | ~$0.002 |\r\n| Explain | ~$0.003 |\r\n\r\nEvery response includes `usage.cost` and `usage.credits_remaining`.\r\n\r\n---\r\n\r\n## Get Started\r\n\r\n1. Sign up at [aisa.one](https:\u002F\u002Faisa.one)\r\n2. Get your API key\r\n3. Add credits (pay-as-you-go)\r\n4. 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