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Get Started Free →Deep web research — fetches full page content for analysis. Snippets alone are PROHIBITED for conclusions.
.claude/skills/yogsoth-ai-web-research/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 30% | 0% |
| case-12 | ✓→✗ | ▼ Worse | -77% | 0% |
| case-18 | ✓→✓ | = Same ✓ | 24% | 0% |
Deep web research. Fetch full page content for selected URLs and analyze thoroughly. Suitable for specific topic investigation, competitive analysis, technical documentation reading, and any task requiring substantive understanding of web content.
Use this when you need to:
This skill REQUIRES full-page content fetching. Snippet-only analysis is prohibited.
For URL discovery (Step 1), use whichever web-search MCP is configured — check availability yourself. If more than one is active, try them in the order listed below. This order is just the writing order, not a hard priority — any active provider is equally valid.
For full-page content fetching (Step 3), always use apify/rag-web-browser. This is not configurable — apify is the only supported content fetcher.
<HARD-GATE> Search snippets are NOT sufficient for research.
For EVERY page selected for analysis, you MUST fetch full content via apify/rag-web-browser.
PROHIBITED:
REQUIRED:
apify/rag-web-browser for every page you analyze</HARD-GATE>
Find candidate URLs via the configured web-search provider (~10 results per call). See Provider Details (Discovery) for exact tool name and parameters.
For time-sensitive topics, use news search if available.
Use snippets ONLY to assess relevance for URL selection — not for analysis.
Choose 3-10 most relevant URLs based on:
For each selected URL, fetch full content:
apify/rag-web-browser(query="<URL>", maxResults=1, outputFormats=["markdown"])Parameters:
query (required): the URL to fetch (when fetching a specific page) or search termsmaxResults (default 3): set to 1 when fetching a known URL, 3-5 when searchingoutputFormats (default "markdown"]): always use ["markdown"] for LLM consumptionWhen fetching a known URL: pass the full URL as query, set maxResults=1 When doing broad discovery: pass search terms as query, set maxResults=3
Base ALL conclusions on full page content:
If fetched pages contain links to deeper resources:
Use for URL discovery only — not for content analysis.
| Tool | Role | Returns | |------|------|---------| | brave_web_search | General URL discovery | URL, title, snippet | | brave_news_search | Recent news URL discovery | URL, title, snippet, date |
Key parameters:
query (required): search terms, max 400 charscount: 1-20 results (default 10)freshness: pd (24h), pw (7d), pm (31d), py (365d)Examples:
brave_web_search(query="model context protocol MCP server development guide 2025", count=10)
brave_news_search(query="GPT-5 announcement details capabilities", freshness="pw", count=10)Use for URL discovery only — not for content analysis.
| Tool | Role | Returns | |------|------|---------| | tavily_search | General URL discovery | URL, title, content snippet, score |
Key parameters:
query (required): search termsmax_results: number of results (default 10)search_depth: basic or advanced (default basic)Examples:
tavily_search(query="model context protocol MCP server development guide 2025", max_results=10)query (required): Google Search keywords OR a specific URL to fetchmaxResults (default 3): number of pages to scrape1 when fetching a known URL3-5 when doing a broad search via query termsoutputFormats (default "markdown"]): always use ["markdown"]| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 60,893 | 13,342 | -78% | 1 | 1 | 0% | 7,964 | 1,975 | -75% | 0 | 0 | — |
case-18 | pass→pass | 11,895 | 8,448 | -29% | 1 | 1 | 0% | 2,547 | 3,162 | +24% | 0 | 0 | — |
case-02 | fail→fail | 45,798 | 7,374 | -84% | 1 | 1 | 0% | 7,107 | 1,958 | -72% | 0 | 0 | — |
case-03 | fail→fail | 41,585 | 15,886 | -62% | 1 | 1 | 0% | 7,020 | 2,155 | -69% | 0 | 0 | — |
case-04 | fail→fail | 11,100 | 8,009 | -28% | 1 | 1 | 0% | 315 | 1,892 | +501% | 0 | 0 | — |
case-05 | fail→fail | 18,506 | 10,601 | -43% | 1 | 1 | 0% | 2,555 | 1,755 | -31% | 0 | 0 | — |
case-06 | fail→fail | 18,789 | 10,576 | -44% | 1 | 1 | 0% | 2,285 | 2,490 | +9% | 0 | 0 | — |
case-07 | fail→fail | 15,823 | 23,308 | +47% | 1 | 1 | 0% | 1,683 | 2,106 | +25% | 0 | 0 | — |
case-08 | fail→pass | 13,119 | 8,307 | -37% | 1 | 1 | 0% | 1,574 | 2,017 | +28% | 0 | 0 | — |
case-09 | fail→fail | 16,522 | 17,477 | +6% | 1 | 1 | 0% | 322 | 1,806 | +461% | 0 | 0 | — |
case-10 | fail→fail | 94,329 | 18,022 | -81% | 1 | 1 | 0% | 4,273 | 1,775 | -58% | 0 | 0 | — |
case-11 | fail→pass | 20,651 | 2,951 | -86% | 1 | 1 | 0% | 2,516 | 1,996 | -21% | 0 | 0 | — |
case-12 | pass→fail | 46,948 | 11,576 | -75% | 1 | 1 | 0% | 8,233 | 1,882 | -77% | 0 | 0 | — |
case-13 | fail→fail | 18,950 | 11,215 | -41% | 1 | 1 | 0% | 2,364 | 2,613 | +11% | 0 | 0 | — |
case-14 | fail→fail | 14,094 | 16,191 | +15% | 1 | 1 | 0% | 2,372 | 3,204 | +35% | 0 | 0 | — |
case-15 | fail→fail | 9,023 | 9,809 | +9% | 1 | 1 | 0% | 200 | 1,629 | +715% | 0 | 0 | — |
case-16 | fail→fail | 9,900 | 15,448 | +56% | 1 | 1 | 0% | 219 | 1,690 | +672% | 0 | 0 | — |
case-17 | fail→fail | 10,167 | 17,293 | +70% | 1 | 1 | 0% | 246 | 1,888 | +667% | 0 | 0 | — |
case-19 | fail→fail | 12,270 | 12,525 | +2% | 1 | 1 | 0% | 1,111 | 2,595 | +134% | 0 | 0 | — |
case-20 | pass→pass | 10,108 | 8,829 | -13% | 1 | 1 | 0% | 913 | 2,150 | +135% | 0 | 0 | — |
case-21 | pass→pass | 18,858 | 12,136 | -36% | 1 | 1 | 0% | 2,243 | 2,683 | +20% | 0 | 0 | — |
case-22 | fail→pass | 13,905 | 9,822 | -29% | 1 | 1 | 0% | 2,217 | 2,873 | +30% | 0 | 0 | — |
case-23 | fail→fail | 9,023 | 7,197 | -20% | 1 | 1 | 0% | 1,500 | 1,853 | +24% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 10 counted toward the lift figure. The other 13 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 percentage points is the difference between those two pass rates over the 10 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.