websearch_service_skill
- Python
7
GitHub Stars
13
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill lin-a1/skills-agent --skill websearch_service- analyzer.py18.5 KB
- browser_pool.py3.5 KB
- clear_cache.py1.8 KB
- client.py2.1 KB
- content_analyzer.py13.8 KB
- Dockerfile1.2 KB
- models.py2.8 KB
- page_fetcher.py13.6 KB
- result_filter.py2.9 KB
- server.py5.7 KB
- SKILL.md1.8 KB
- storage_clients.py13.2 KB
- url_scorer.py5.7 KB
Overview
This skill provides a real-time, internet-connected search service built on SearXNG and a Visual Language Model (VLM). It fetches up-to-date web results, automatically extracts and structures page content, and uses a dual-layer cache (vector semantic cache + database cache) for fast, relevant responses.
How this skill works
The service queries SearXNG for web results, then passes pages through a VLM to extract main content, summaries, key facts, and credibility signals. Results are stored in a two-tier cache: a vector cache for semantic similarity lookups and a database cache for metadata and raw extracts. Calls support forced refresh to bypass caches when fresh data is required.
When to use it
- Retrieve the latest news or breaking events that require live web data.
- Gather fact-specific answers or source snippets with provenance and credibility hints.
- Support agent workflows that need structured web content for summarization or analysis.
- Reduce repeated fetch costs by leveraging semantic caching for similar queries.
- Force-refresh when immediate, uncached updates are essential.
Best practices
- Start with default searches that use caching; enable force_refresh sparingly for critical freshness.
- Limit max_results to a focused number (3–10) to balance depth and latency.
- Check result 'from_cache' and 'credibility' fields before relying on extracted facts.
- Use the provided health_check before bulk operations to confirm service availability.
- Combine vector cache hits with time-based validation for highly volatile topics.
Example use cases
- An agent compiling up-to-the-minute news briefs about market-moving events.
- A research assistant extracting key facts and authoritative passages for citation.
- A monitoring tool that scans for mentions of a brand and captures contextual excerpts.
- A content summarizer that pulls and condenses multiple live web pages into an executive summary.
- Rapid fact-checking workflows where source URLs and relevance scores are required.
FAQ
Pass force_refresh=True to the search call to ignore both vector and database caches and re-fetch pages.
What does the credibility field indicate?
Credibility is an automated signal from the VLM and heuristics that indicates the perceived trustworthiness of the source or extracted content (e.g., authoritative, mixed, low).
Can I retrieve the original page text and structured extracts?
Yes. Each result includes data.main_content for the primary extracted text and data.key_information for structured facts or bullet points.
