Web Research

Web Research Skill

Version: 1.1 Created: 2026-02-02 Author: Cipher (self-taught) Purpose: Effective web research using Brave Search API and web_fetch for content extraction

Overview

This skill encodes best practices for web research — finding, evaluating, and synthesizing information from online sources. It provides patterns for search query formulation, source evaluation, information synthesis, and attribution. Web research is not about collecting links — it's about building understanding from reliable sources.

Output templates for all research modes are in references/research-output-templates.md.

Core Tools

Available Tools:

• web_search(query, count, country, search_lang, ui_lang, freshness) — Brave Search API for finding sources
• web_fetch(url, extractMode, maxChars) — Extract readable content from URLs (markdown/text)

Parameters:

• query: Search query string (required)
• count: Number of results (1-10, default: 5)
• country: 2-letter country code for regional results (default: 'US')
• search_lang: ISO language code for search results
• ui_lang: ISO language code for UI elements
• freshness: Time filter for results (pd=past 24h, pw=past week, pm=past month, py=past year)
• url: HTTP or HTTPS URL to fetch
• extractMode: 'markdown' or 'text' (default: 'markdown')
• maxChars: Maximum characters to return (truncates when exceeded)

When to Use This Skill

• Investigating a topic that requires current information
• Finding sources for research or specifications
• Verifying claims or facts
• Gathering competitive intelligence or landscape scans
• Understanding a technology, framework, or practice
• Finding documentation or examples

Research Workflow

Step 1: Define Your Research Question

Before searching, clarify:

1. What specific question am I trying to answer?
2. What level of detail do I need? (overview vs. deep dive)
3. Is this time-sensitive? (recent events, API versions, industry news)
4. What context do I already have? (avoid re-searching known info)

Examples:

• Bad: "AROMA"
• Good: "AROMA agent collaboration architecture v0.0.1 specification"
• Good: "AI agent memory management best practices 2026"

Step 2: Formulate Search Queries

Pattern: [topic] [context/aspect] [specific keywords] [optional: date filter]

Examples:

Freshness filters:

• Breaking news: freshness="pd" (past 24 hours)
• Recent developments: freshness="pw" (past week)
• Tech releases: freshness="pm" (past month)
• Historical context: freshness="py" (past year) or no filter

Step 3: Search and Select Sources

Execute search:

web_search(
  query="your query here",
  count=5,
  country="US",
  search_lang="en"
)

Evaluate each source:

Source selection:

• Prioritize: Official docs, established news, expert publications
• Supplement: Community discussions, GitHub issues, forums
• Verify: Cross-reference claims across 2-3 sources

Step 4: Fetch and Extract Content

For relevant sources:

web_fetch(
  url="https://example.com/page",
  extractMode="markdown",
  maxChars=20000
)

Extraction strategy:

1. Read headers first — Understand structure, main sections
2. Extract key insights — 1-3 sentences per section
3. Note supporting details — Numbers, names, dates, versions
4. Skip filler — Introduction fluff, generic advice
5. Capture sources cited — Link back to original content

Step 5: Synthesize Findings

Goal: Answer the research question, not regurgitate content.

Synthesis template — see references/research-output-templates.md for the full Research Synthesis format.

Synthesis principles:

• Be specific — Avoid "some say," "likely," "possibly"
• Attribute claims — "According to [Source], X is true" not "X is true"
• Note contradictions — "Source A claims X, but Source B says Y"
• Signal uncertainty — "Could not verify" or "Limited evidence available"

Research Modes

Mode 1: Verification Research

Use when: Verifying a specific claim, fact, or data point

Process:

1. Formulate specific query: "[claim] verify"
2. Search 3-5 sources
3. Cross-reference across sources
4. Note consensus or conflict

Output: Verification document with Verdict (Confirmed / False / Partially Confirmed / Could Not Verify), Evidence section listing each source's position — see references/research-output-templates.md

Mode 2: Deep Dive Research

Use when: Need comprehensive understanding of a complex topic

Process:

1. Start with overview query: "[topic] overview"
2. Identify subtopics from results
3. Query each subtopic: "[topic] [subtopic] details"
4. Fetch and read 2-3 sources per subtopic
5. Synthesize into structured overview

Output: Deep Dive document organized by subtopics with cross-subtopic synthesis — see references/research-output-templates.md

Mode 3: Landscape Scan

Use when: Broad pattern recognition across many topics

Process:

1. Formulate 5-10 related queries
2. Execute searches (1-2 sources each)
3. Extract patterns and themes
4. Create opportunity or comparison matrix

Output: Landscape Scan document with themes, key players table, and gaps list — see references/research-output-templates.md

Quality Checklist

Before concluding web research, verify:

Source Quality

• At least 2-3 sources consulted (unless verification)
• Sources are credible (official docs, established news, experts)
• Sources are recent (within 1-3 years, unless historical context)
• Conflicting information is noted

Content Quality

• Specific claims are supported by evidence
• Attribution is clear (which source said what)
• Uncertainty is signaled (couldn't verify, limited data)
• Filler is minimized (no generic advice)

Synthesis Quality

• Research question is answered directly
• Key findings are specific and actionable
• Sources are cited with URLs
• Open questions or gaps are noted

Common Pitfalls to Avoid

• Single-source confirmation — Finding one source that confirms belief — cross-reference across 2-3 sources
• Over-fetching — Reading 50 pages for one query — focus on 2-5 relevant sources
• Generic queries — "AI tools" returns 10M results — use specific queries with context
• No attribution — "Studies show X" — attribute: "According to [Source], X"
• Outdated data — Using 2019 info for a 2026 decision — use freshness filter
• Content dumping — Copying entire articles — extract key insights (1-3 sentences per section)

Integration with Other Skills

Use with:

• research-modes — For structuring deep vs. wide research phases
• specification-writer — When research feeds into spec writing
• seed-extraction — When research reveals reusable patterns
• workspace-navigation — When organizing research findings in shared workspaces

Pattern:

web_search() -> web_fetch() -> Extract insights ->
  research-modes(structure) OR specification-writer(draft) OR seed-extraction(capture)

Output

• A Research Summary markdown document answering the research question
• Saved to the project's docs/research/ or scouts/ directory
• Named: [date]_[topic]_web_research.md
• Includes: summary, key findings, supporting details, cited sources with URLs, open questions

Examples

Scenario 1: "Find current information about WebSocket performance benchmarks for Go" → Verification/Deep Dive document with 3-5 authoritative sources, specific numbers, attribution per claim, and open questions flagged

Scenario 2: "Search for what the field says about AI agent orchestration patterns" → Landscape Scan document with theme clusters, key tool/framework table, and identified gaps across 8-10 sources

Edge Cases

• Paywalled content: Note the source in the synthesis as "paywalled, snippet only" and extract what is available from the search snippet; do not fabricate full content
• Contradictory authoritative sources: Surface the contradiction explicitly; do not pick one silently — present both and note the disagreement
• Topic with no recent sources (>2 years old): Label findings as potentially outdated; recommend confirming with a domain expert or official changelog
• Query returns zero relevant results: Reformulate with 2-3 alternative phrasings before concluding the topic is unresearchable

Anti-Patterns

• Fetching the full text of 10+ pages when 2-3 targeted extractions answer the question — token cost with no quality gain
• Summarizing sources in the order they were found rather than organizing findings by theme — produces a list, not a synthesis
• Accepting a source as authoritative because its domain sounds credible without checking the actual author or date
• Using web research for questions that can be answered from the existing codebase or memory — always check local context first

Related Skills:

• research-modes — Deep vs. wide research structuring
• specification-writer — Research to spec conversion
• seed-extraction — Pattern extraction from research findings

Last Updated: 2026-04-08 Maintained By: Cipher Status: Active