web-search

inference-sh/skills · updated Apr 8, 2026

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$npx skills add https://github.com/inference-sh/skills --skill web-search
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summary

Search the web and extract content via inference.sh CLI.

skill.md

Web Search & Extraction

Search the web and extract content via inference.sh CLI.

Web Search & Extraction

Quick Start

Requires inference.sh CLI (infsh). Install instructions

infsh login

# Search the web
infsh app run tavily/search-assistant --input '{"query": "latest AI developments 2024"}'

Available Apps

Tavily

App App ID Description
Search Assistant tavily/search-assistant AI-powered search with answers
Extract tavily/extract Extract content from URLs

Exa

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

Examples

Tavily Search

infsh app run tavily/search-assistant --input '{
  "query": "What are the best practices for building AI agents?"
}'

Returns AI-generated answers with sources and images.

Tavily Extract

infsh app run tavily/extract --input '{
  "urls": ["https://example.com/article1", "https://example.com/article2"]
}'

Extracts clean text and images from multiple URLs.

Exa Search

infsh app run exa/search --input '{
  "query": "machine learning frameworks comparison"
}'

Returns highly relevant links with context.

Exa Answer

infsh app run exa/answer --input '{
  "question": "What is the population of Tokyo?"
}'

Returns direct factual answers.

Exa Extract

infsh app run exa/extract --input '{
  "url": "https://example.com/research-paper"
}'

Extracts and analyzes web page content.

Workflow: Research + LLM

# 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>"
}'

Workflow: Extract + Summarize

# 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>"
}'

Use Cases

  • Research: Gather information on any topic
  • RAG: Retrieval-augmented generation
  • Fact-checking: Verify claims with sources
  • Content aggregation: Collect data from multiple sources
  • Agents: Build research-capable AI agents

Related Skills

# Full platform skill (all 150+ 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

Documentation

how to use web-search

How to use web-search on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add web-search
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/inference-sh/skills --skill web-search

The skills CLI fetches web-search from GitHub repository inference-sh/skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/web-search

Reload or restart Cursor to activate web-search. Access the skill through slash commands (e.g., /web-search) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.669 reviews
  • Kaira Gonzalez· Dec 28, 2024

    I recommend web-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kaira Park· Dec 12, 2024

    Registry listing for web-search matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Zara Wang· Dec 8, 2024

    Useful defaults in web-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ganesh Mohane· Dec 4, 2024

    web-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Aisha Rahman· Dec 4, 2024

    We added web-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yusuf Diallo· Dec 4, 2024

    Keeps context tight: web-search is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kaira Farah· Nov 27, 2024

    web-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Rahul Santra· Nov 23, 2024

    Useful defaults in web-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Noah Choi· Nov 23, 2024

    web-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aisha Okafor· Nov 23, 2024

    web-search has been reliable in day-to-day use. Documentation quality is above average for community skills.

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