searxng-search▌
ypares/agent-skills · updated Apr 8, 2026
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Local metasearch engine aggregating results from multiple sources with JSON output and package repository support.
- ›Supports 20+ search categories including general web, Cargo/crates.io, npm, GitHub repositories, IT resources, academic papers, news, images, and videos
- ›Returns structured JSON with result metadata (title, URL, content, engines, score, publish date) plus answer boxes, suggestions, and infoboxes
- ›Auto-detects podman or docker, starts SearXNG on localhost:8888 with minimal
SearXNG Search
SearXNG is a privacy-respecting metasearch engine that you can run locally. It aggregates results from multiple search engines and package repositories, returning clean JSON output.
Quick Start
Start SearXNG:
start-searxng --detach
This will:
- Auto-detect podman or docker
- Create a minimal config with JSON output enabled
- Start SearXNG on
http://localhost:8888 - Wait until ready
Stop SearXNG:
podman stop searxng # or: docker stop searxng
Custom port:
start-searxng --port 9999 --detach
Quick Reference
| Task | Command | Category |
|---|---|---|
| General web search | curl "http://localhost:8888/search?q=<query>&format=json" |
general |
| Search Cargo/crates.io | curl "http://localhost:8888/search?q=<crate>&format=json&categories=cargo" |
cargo |
| Search npm packages | curl "http://localhost:8888/search?q=<pkg>&format=json&categories=packages" |
packages |
| Search code repositories | curl "http://localhost:8888/search?q=<query>&format=json&categories=repos" |
repos |
| Search IT resources | curl "http://localhost:8888/search?q=<query>&format=json&categories=it" |
it |
| Limit results | Add &limit=N to URL |
- |
| Multiple categories | &categories=cat1,cat2 |
- |
Available Categories
Run to see all categories:
curl -s "http://localhost:8888/config" | jq '.categories'
Notable categories:
- general: General web search (default)
- cargo: Rust crates from crates.io
- packages: Multi-repo (npm, rubygems, haskell/hoogle, hex, packagist, metacpan, pub.dev, pkg.go.dev, docker hub, alpine, etc.)
- it: IT/tech resources (includes GitHub, Docker Hub, crates.io)
- repos: Code repositories
- code: Code search
- scientific publications: Academic papers
- news, videos, images, books, etc.
See package-engine-status.md for comprehensive package search testing results.
JSON Response Structure
{
"query": "search term",
"number_of_results": 0,
"results": [
{
"url": "https://example.com",
"title": "Result Title",
"content": "Snippet of content...",
"publishedDate": "2025-01-01T00:00:00",
"engine": "duckduckgo",
"engines": ["duckduckgo", "startpage"],
"score": 3.0,
"category": "general"
}
],
"answers": [], // Direct answers/infoboxes
"suggestions": [], // Search suggestions
"corrections": [], // Query corrections
"infoboxes": [], // Knowledge panels
"unresponsive_engines": []
}
Common Usage Patterns
1. Package Repository Searches
Cargo/Rust crates:
curl -s "http://localhost:8888/search?q=tokio&format=json&categories=cargo" | \
jq '.results[] | {title, url, content}'
npm packages:
curl -s "http://localhost:8888/search?q=express&format=json&categories=packages" | \
jq '.results[] | select(.engines[] == "npm") | {title, url, content}'
PyPI packages (workaround - see below):
# PyPI engine is enabled but not returning results in current SearXNG config
# Use direct API or qypi CLI instead (see PyPI Workaround section)
2. Web Search with Filtering
IT/Tech search:
curl -s "http://localhost:8888/search?q=rust+async&format=json&categories=it" | \
jq '.results[0:5] | .[] | {title, url, engines}'
GitHub repositories:
curl -s "http://localhost:8888/search?q=machine+learning&format=json&categories=repos" | \
jq '.results[] | select(.engines[] == "github") | {title, url}'
3. Extracting Specific Information
Get top 3 results:
curl -s "http://localhost:8888/search?q=rust+ownership&format=json" | \
jq '.results[0:3] | .[] | {title, url, content}'
Check which engines returned results:
curl -s "http://localhost:8888/search?q=python&format=json" | \
jq '.results[0].engines'
Get answer boxes/infoboxes:
curl -s "http://localhost:8888/search?q=rust+language&format=json" | \
jq '.infoboxes, .answers'
PyPI Workaround
Since PyPI is not returning results in SearXNG (despite being enabled), use these alternatives:
Option 1: Direct PyPI JSON API
# Search (limited to simple package name matching)
curl -s "https://pypi.org/pypi/<package>/json" | jq '.info | {name, summary, version, home_page}'
# Example:
curl -s "https://pypi.org/pypi/requests/json" | jq '.info.summary'
Option 2: qypi CLI tool
# Install
uvx qypi search pandas --json
# Get package info
uvx qypi info requests --json
# List releases
uvx qypi releases flask --json
See references/pypi-direct-search.md for more details.
Integration with Nushell
Create a helper function:
def searx [
query: string,
--category (-c): string = "general",
--limit (-l): int = 10
] {
http get $"http://localhost:8888/search?q=($query | url encode)&format=json&categories=($category)"
| get results
| first $limit
| select title url content engines
}
Usage:
searx "tokio async" --category cargo --limit 5
searx "flask tutorial" --category general
Debugging
Check SearXNG config:
curl -s "http://localhost:8888/config" | jq '.engines[] | select(.name == "pypi")'
Check for engine errors:
curl -s "http://localhost:8888/search?q=test&format=json" | jq '.unresponsive_engines'
Test specific engine:
curl -s "http://localhost:8888/search?q=flask&format=json&engines=pypi" | jq .
Known Issues
- PyPI engine enabled but not working: Use direct API or qypi CLI as workaround
- Cargo category sometimes returns empty: Try
categories=packagesorcategories=itwhich also include crates.io - Rate limiting: SearXNG may rate-limit if too many requests in quick succession
Configuration
Using the Helper Script (Recommended)
The start-searxng script creates a minimal configuration automatically:
start-searxng --help
Default config includes:
use_default_settings: true(inherits all SearXNG defaults)- JSON format enabled
- Rate limiting disabled (for local use)
- Secret key (change in production!)
Using Your Own Config
start-searxng --config /path/to/your/config/dir
Your config directory should contain settings.yml.
Manual Container Start
# Create config
mkdir -p /tmp/searxng-config
cat > /tmp/searxng-config/settings.yml << 'EOF'
use_default_settings: true
search:
formats:
- html
- json
server:
secret_key: "change-me-in-production"
bind_address: "0.0.0.0"
port: 8080
EOF
# Start with podman
podman run --rm -d --name searxng \
-p 8888:8080 \
-v /tmp/searxng-config:/etc/searxng:Z \
docker.io/searxng/searxng:latest
# Or with docker
docker run --rm -d --name searxng \
-p 8888:8080 \
-v /tmp/searxng-config:/etc/searxng \
docker.io/searxng/searxng:latest
Check Logs
podman logs searxng # or: docker logs searxng
Advanced Config
See SearXNG Settings Documentation for all options.
Minimal config to add JSON output to defaults:
use_default_settings: true
search:
formats:
- html
- json
How to use searxng-search on Cursor
AI-first code editor with Composer
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 searxng-search
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches searxng-search from GitHub repository ypares/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate searxng-search. Access the skill through slash commands (e.g., /searxng-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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★69 reviews- ★★★★★Advait Sethi· Dec 28, 2024
Useful defaults in searxng-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Daniel White· Dec 28, 2024
searxng-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Nikhil Mensah· Dec 24, 2024
searxng-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Alexander Yang· Dec 20, 2024
Useful defaults in searxng-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Ganesh Mohane· Dec 12, 2024
searxng-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Shikha Mishra· Dec 8, 2024
Registry listing for searxng-search matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★James Malhotra· Nov 19, 2024
I recommend searxng-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Xiao Diallo· Nov 19, 2024
searxng-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Advait Menon· Nov 15, 2024
Solid pick for teams standardizing on skills: searxng-search is focused, and the summary matches what you get after install.
- ★★★★★Alexander Menon· Nov 11, 2024
I recommend searxng-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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