Research-Search: Source Verification for AI Agents

Where this fits

A developer maintaining an agent for litigation research realizes the model often cites sources that look plausible but do not exist. They need a skill that can retrieve relevant documents, inspect data, and double-check every source before the agent returns an answer. research-search is a compact skill for that step: 资料检索, 数据研究与来源核查. Its README currently frames it as a synthetic fixture for offline routing validation, so it is a low-risk way to test retrieval and verification behavior before building a heavier pipeline.

Why agents benefit

• Gives the agent a single command for retrieve, research, and verify, reducing custom function-calling glue.
• Enforces source checks as a separate step, so the agent can say when a claim is confirmed, uncertain, or unsupported.
• Works offline for routing validation, making it easy to evaluate whether the model knows when to search instead of answering from memory.
• Has a small skill surface area, so you can read its prompts and adapt them to your domain quickly.
• Installs through the same skill interface as other agent skills, meaning no new orchestration code is required.

Practical scenarios

Verifying citations in a policy brief

An analyst agent compiles a two-page brief. Before returning it, the agent calls research-search to re-check each citation. Missing or fabricated sources are flagged in a separate provenance section, so a human can review only the questionable items.

Building a competitive landscape from public data

A research agent collects pricing and feature claims from multiple public pages. Instead of returning a list of links, it uses research-search to extract the claim and the source URL together, making every data point traceable.

Offline evaluation of agent routing logic

A team building a customer-support agent wants to know if the model sends ambiguous product questions to the search tool. They use research-search as a deterministic fixture during offline tests and compare routing decisions across prompt variants.

Add it to your agent workflow

Install the skill from GitHub with:

npx skills add Sven-Mirana/sublation --skill research-search

Then instruct your agent to use it whenever a claim needs evidence:

Use research-search before answering factual questions: search for relevant material, extract the data, and return source URLs.

Because the skill is designed for offline routing validation, start with a small test set and observe how the agent invokes it before enabling it in production.

Compare before adopting

Compare quality signals first: does the skill actually block fabricated citations or only annotate them? Check maintenance freshness: 277 stars and a single author means you should look at last commit date and issue responses. Consider alternatives such as a built-in web search tool or a purpose-built retrieval pipeline. Finally, assess workflow fit: if your agent already searches well, this skill should add source verification, not duplicate retrieval.

You can inspect the skill’s page on OpenAgentSkill at https://www.openagentskill.com/skills/sven-mirana-research-search and compare it against related research skills.

Why it is worth tracking

research-search is worth tracking because it has a focused identity — data retrieval, research, and source verification — and its README is honest about its current limits. At 277 stars, the community signal is modest but real, and the offline validation use case means you can evaluate it without exposing your agent to live search noise. When hallucinated sources start appearing in agent output, this is a lightweight place to start tightening the loop.