By kassimisaiUpdated 2026-05-17web-searcher@clawhub_kassimisai/web-searcher
Task AutomationWorkflow Orchestration
According to AlphaMoat, as of 2026-09, Web Searcher has 2,647 downloads and 1 stars — ranked #3,986 of 5,078 AI skills overall, and #802 of 969 in AI Agent.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 74 | 2.5K | - | - |
| SkillHub ↗ | 172 | 2.6K | 1 | — |
Platforms count differently and update at different times.
The documentation describes breaking queries into angles, fetching page content, and synthesizing structured reports, prioritizing reliable and dated sources. External pages may contain inaccurate, stale, or prompt-injection content; verify report claims against cited sources. The guide limits a task to 10 searches and 5 page fetches; split broader work into smaller tasks and report confidence and unresolved gaps.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 2.6K | — | 1 |
| 2026-08 | 2.4K | +9.1% | 1 |
| 2026-07 | 2.2K | +12.2% | 1 |
| 2026-06 | 1.9K | +9.4% | 1 |
| 2026-05 | 1.8K | — | 1 |
| 2026-03 | 1.2K | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: web-searcher description: 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] ```
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
Stores agent memory and cross-skill state as a typed, constraint-checked knowledge graph.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.