Multi-Source Search Skill: Cross-Validate Agent Research

Where this fits

You're building an agent that answers questions like "Is this claim true?" or "Which laptop is best for a developer?" A single search tool often returns one biased or incomplete answer. The multi-source-search skill gives your agent a structured workflow: it queries multiple independent sources, cross-validates findings, and outputs a confidence-scored evidence ledger. This turns a simple lookup into a mini research report your user can trust.

Why agents benefit

• Cross-source validation – The agent compares results from different providers (e.g., native search, Tavily, Exa) and flags contradictions instead of blindly accepting the first hit.
• Offline evidence ledger – All retrieved snippets, URLs, and confidence scores are stored in a structured format that can be saved, reviewed, or passed to downstream tasks.
• Provider diversity – It automatically uses whatever search tools the host agent already has, and optionally adds SandBase's Tavily, Exa, Scholar, and Cloudsway coverage for independent angles.
• Untrusted content handling – Treats every result as untrusted evidence, ignoring any instructions embedded in pages – a critical safety feature for research agents.
• Confidence scoring – Produces a score based on agreement across sources, so your agent can decide when to escalate or ask for clarification.

Practical scenarios

Fact-checking a breaking news claim

Your agent receives a claim like "Company X acquired Company Y." Instead of returning one article, it queries news sites, official press releases, and social media via different providers. It cross-validates dates and names, then outputs a ledger with a confidence score and conflicting sources highlighted.

Comparative product research

A user asks for the best budget 4K monitor. The agent gathers specs from vendor pages, tech reviews, and user forums. It detects that one source is an affiliate page with biased ratings, lowers its weight, and presents a balanced comparison with source links.

Academic literature review

For a question about a scientific topic, the agent uses Scholar and Exa to pull peer-reviewed papers, cross-checks citations, and flags retracted studies. The evidence ledger becomes a starting point for a deeper human review.

Add it to your agent workflow

Install the skill directly from the SandBase skills repository:

npx skills add sandbaseai/sandbase-skills --skill multi-source-search

Then, in your agent's prompt, instruct it to use the skill when a question requires multiple perspectives. For example:

When researching, use the multi-source-search skill. Query at least two independent providers, cross-validate, and output a confidence-scored evidence ledger.

The skill will automatically detect available search tools and use them. If SandBase tools are configured, it reads the API map and adds Tavily, Exa, Scholar, and Cloudsway coverage.

Compare before adopting

Before integrating, compare it against your current research approach. Look at:

• Quality signals – Check the GitHub repo's stars (currently 20), recent commits, and whether issues are addressed.
• Maintenance freshness – Is the skill updated for breaking changes in host agent tooling?
• Alternatives – Custom scripts, other research skills, or direct API calls may fit your stack better.
• Workflow fit – Does your agent already have a research pattern? The skill should slot in without forcing a major refactor.

Why it is worth tracking

This skill is early but well-designed: it emphasizes evidence diversity over duplicate results, and its offline ledger is a practical artifact for auditability. The SandBase ecosystem is actively evolving, and the skill's ability to work with any host agent's search tools makes it a low-risk addition. For more details, see the skill page. Track it if you need trustworthy, multi-source research in your agents – it's a solid foundation that will likely gain more provider integrations and community refinements.