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Get Started Free →Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
.claude/skills/aiskillstore-web-search/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 386% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 449% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -54% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 22% | 0% |
Search the web and extract content via inference.sh CLI.
> Requires inference.sh CLI (infsh). Install instructions
bashinfsh login # Search the web infsh app run tavily/search-assistant --input '{"query": "latest AI developments 2024"}'
| App | App ID | Description | |-----|--------|-------------| | Search Assistant | tavily/search-assistant | AI-powered search with answers | | Extract | tavily/extract | Extract content from URLs |
| App | App ID | Description | |-----|--------|-------------| | Search | exa/search | Smart web search with AI | | Answer | exa/answer | Direct factual answers | | Extract | exa/extract | Extract and analyze web content |
bashinfsh app run tavily/search-assistant --input '{ "query": "What are the best practices for building AI agents?" }'
Returns AI-generated answers with sources and images.
bashinfsh app run tavily/extract --input '{ "urls": ["https://example.com/article1", "https://example.com/article2"] }'
Extracts clean text and images from multiple URLs.
bashinfsh app run exa/search --input '{ "query": "machine learning frameworks comparison" }'
Returns highly relevant links with context.
bashinfsh app run exa/answer --input '{ "question": "What is the population of Tokyo?" }'
Returns direct factual answers.
bashinfsh app run exa/extract --input '{ "url": "https://example.com/research-paper" }'
Extracts and analyzes web page content.
bash# 1. Search for information infsh app run tavily/search-assistant --input '{ "query": "latest developments in quantum computing" }' > search_results.json # 2. Analyze with Claude infsh app run openrouter/claude-sonnet-45 --input '{ "prompt": "Based on this research, summarize the key trends: <search-results>" }'
bash# 1. Extract content from URL infsh app run tavily/extract --input '{ "urls": ["https://example.com/long-article"] }' > content.json # 2. Summarize with LLM infsh app run openrouter/claude-haiku-45 --input '{ "prompt": "Summarize this article in 3 bullet points: <content>" }'
bash# Full platform skill (all 250+ apps) npx skills add inference-sh/skills@infsh-cli # LLM models (combine with search for RAG) npx skills add inference-sh/skills@llm-models # Image generation npx skills add inference-sh/skills@ai-image-generation
Browse all apps: infsh app list
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 4,456 | 12,785 | +187% | 1 | 1 | 0% | 262 | 1,273 | +386% | 0 | 0 | — |
case-02 | fail→pass | 3,014 | 8,951 | +197% | 1 | 1 | 0% | 494 | 2,713 | +449% | 0 | 0 | — |
case-03 | fail→fail | 5,070 | 6,980 | +38% | 1 | 1 | 0% | 237 | 1,914 | +708% | 0 | 0 | — |
case-04 | fail→pass | 12,187 | 2,655 | -78% | 1 | 1 | 0% | 2,131 | 1,586 | -26% | 0 | 0 | — |
case-05 | fail→pass | 18,348 | 2,724 | -85% | 1 | 1 | 0% | 3,406 | 1,563 | -54% | 0 | 0 | — |
case-06 | fail→pass | 16,884 | 2,390 | -86% | 1 | 1 | 0% | 1,164 | 1,415 | +22% | 0 | 0 | — |
case-07 | fail→pass | 21,236 | 2,432 | -89% | 1 | 1 | 0% | 1,519 | 1,467 | -3% | 0 | 0 | — |
case-08 | fail→pass | 15,716 | 2,273 | -86% | 1 | 1 | 0% | 3,218 | 1,451 | -55% | 0 | 0 | — |
case-09 | fail→pass | 30,902 | 7,004 | -77% | 1 | 1 | 0% | 2,529 | 2,491 | -2% | 0 | 0 | — |
case-10 | fail→pass | 11,183 | 7,263 | -35% | 1 | 1 | 0% | 2,611 | 2,633 | +1% | 0 | 0 | — |
case-11 | fail→fail | 15,898 | 1,710 | -89% | 1 | 1 | 0% | 2,135 | 1,254 | -41% | 0 | 0 | — |
case-12 | fail→pass | 10,181 | 1,410 | -86% | 1 | 1 | 0% | 1,861 | 1,172 | -37% | 0 | 0 | — |
case-13 | fail→pass | 5,185 | 1,811 | -65% | 1 | 1 | 0% | 869 | 1,380 | +59% | 0 | 0 | — |
case-14 | fail→pass | 16,934 | 2,305 | -86% | 1 | 1 | 0% | 3,109 | 1,367 | -56% | 0 | 0 | — |
case-15 | fail→pass | 8,559 | 2,021 | -76% | 1 | 1 | 0% | 1,491 | 1,344 | -10% | 0 | 0 | — |
case-16 | pass→pass | 8,764 | 1,755 | -80% | 1 | 1 | 0% | 1,502 | 1,303 | -13% | 0 | 0 | — |
case-17 | pass→pass | 12,380 | 8,795 | -29% | 1 | 1 | 0% | 2,017 | 2,431 | +21% | 0 | 0 | — |
case-18 | pass→pass | 13,537 | 3,651 | -73% | 1 | 1 | 0% | 2,249 | 1,623 | -28% | 0 | 0 | — |
case-19 | fail→pass | 17,076 | 2,056 | -88% | 1 | 1 | 0% | 3,393 | 1,348 | -60% | 0 | 0 | — |
case-20 | pass→pass | 9,986 | 3,301 | -67% | 1 | 1 | 0% | 1,562 | 1,499 | -4% | 0 | 0 | — |
case-21 | fail→pass | 12,611 | 7,134 | -43% | 1 | 1 | 0% | 2,214 | 2,405 | +9% | 0 | 0 | — |
case-22 | pass→pass | 8,368 | 8,083 | -3% | 1 | 1 | 0% | 1,744 | 2,728 | +56% | 0 | 0 | — |
case-23 | pass→pass | 10,768 | 8,408 | -22% | 1 | 1 | 0% | 2,149 | 2,601 | +21% | 0 | 0 | — |
case-24 | pass→pass | 12,181 | 9,420 | -23% | 1 | 1 | 0% | 2,366 | 2,821 | +19% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted, and 22 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +63 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/22/2026 | +50% |
Other measured skills in the registry, with their headline benchmark lift.