How do I install the Web Searcher skill?

Run npx skillmds add modbender/web-searcher in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Web Searcher safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Web Searcher?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Web Searcher free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Web Searcher?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Web Searcher by modbender — Agent Skill

Name: Web Searcher
Rating: 4.2 (5 reviews)
Author: modbender

Web Searcher

Autonomous web research agent that performs multi-step searches, follows links, extracts data, and synthesizes findings into structured reports. Use when asked to research a topic, find information across multiple sources, compare options, gather market data, compile lists, or answer questions requiring deep web investigation beyond a single search.

modbender 80a1858 2 files · 3.5 KB Updated Sep 9, 2026 12 repo stars

File contents

Web Searcher Agent

Workflow

Parse the query — Break the user's request into 2-5 specific search queries that cover different angles of the topic.
Search phase — Execute searches using web_search. Rate limit: max 3 searches, then assess before continuing.
Deep dive phase — For promising results, use web_fetch to extract full content. Prioritize:
- Primary sources over aggregators
- Recent content over old (check dates)
- Authoritative domains over random blogs
Cross-reference — Compare findings across sources. Flag contradictions. Note consensus.
Synthesize — Compile findings into a clear, structured response with:
- Key findings (bullet points)
- Sources cited (URLs)
- Confidence level (high/medium/low per claim)
- Gaps identified (what couldn't be found)

Search Strategies

Factual queries

Search → verify across 2+ sources → report with citations.

Comparison/market research

Search each option separately → fetch detail pages → build comparison table → recommend.

People/company research

Search name + context → fetch LinkedIn/company pages → cross-reference news → compile profile.

How-to/technical

Search with specific technical terms → fetch documentation/guides → distill steps.

Guidelines

Max 10 searches per task to avoid rate limits and token waste.
Max 5 page fetches — be selective about which URLs to deep-dive.
Always include source URLs so the user can verify.
If a search returns nothing useful, rephrase and retry once before moving on.
For time-sensitive info, use freshness parameter (pd/pw/pm/py).
Prefer web_fetch with maxChars: 5000 to keep context manageable.
If the task is massive, suggest breaking it into sub-tasks or spawning sub-agents.

Output Format

## [Topic]

### Key Findings
- Finding 1 (Source: url)
- Finding 2 (Source: url)

### Details
[Expanded analysis]

### Sources
1. [Title](url) — what was found here
2. [Title](url) — what was found here

### Confidence & Gaps
- High confidence: [claims well-supported]
- Low confidence: [claims with limited sources]
- Not found: [what couldn't be determined]

---name: web-searcherdescription: Autonomous web research agent that performs multi-step searches, follows links, extracts data, and synthesizes findings into structured reports. Use when asked to research a topic, find information across multiple sources, compare options, gather market data, compile lists, or answer questions requiring deep web investigation beyond a single search.--- # Web Searcher Agent ## Workflow 1. **Parse the query** — Break the user's request into 2-5 specific search queries that cover different angles of the topic. 2. **Search phase** — Execute searches using `web_search`. Rate limit: max 3 searches, then assess before continuing. 3. **Deep dive phase** — For promising results, use `web_fetch` to extract full content. Prioritize: - Primary sources over aggregators - Recent content over old (check dates) - Authoritative domains over random blogs 4. **Cross-reference** — Compare findings across sources. Flag contradictions. Note consensus. 5. **Synthesize** — Compile findings into a clear, structured response with: - Key findings (bullet points) - Sources cited (URLs) - Confidence level (high/medium/low per claim) - Gaps identified (what couldn't be found) ## Search Strategies ### Factual queries Search → verify across 2+ sources → report with citations. ### Comparison/market research Search each option separately → fetch detail pages → build comparison table → recommend. ### People/company research Search name + context → fetch LinkedIn/company pages → cross-reference news → compile profile. ### How-to/technical Search with specific technical terms → fetch documentation/guides → distill steps. ## Guidelines - **Max 10 searches per task** to avoid rate limits and token waste. - **Max 5 page fetches** — be selective about which URLs to deep-dive. - Always include source URLs so the user can verify. - If a search returns nothing useful, rephrase and retry once before moving on. - For time-sensitive info, use `freshness` parameter (pd/pw/pm/py). - Prefer `web_fetch` with `maxChars: 5000` to keep context manageable. - If the task is massive, suggest breaking it into sub-tasks or spawning sub-agents. ## Output Format ``` ## [Topic] ### Key Findings - Finding 1 (Source: url) - Finding 2 (Source: url) ### Details [Expanded analysis] ### Sources 1. [Title](url) — what was found here 2. [Title](url) — what was found here ### Confidence & Gaps - High confidence: [claims well-supported] - Low confidence: [claims with limited sources] - Not found: [what couldn't be determined] ```

Frequently asked questions