| name | websearch-standard |
| description | Standard multi-source verification search strategy for moderate complexity research. 2-iteration workflow with source ranking, consensus identification, and citation transparency. Use for feature comparisons, moderate complexity topics, fact-checking. Keywords: compare, differences, features, fact-check, verify, what are. |
Standard Web Research Strategy
What This Skill Does
Provides balanced research methodology for moderate complexity questions requiring multi-source verification but not full decomposition. Implements 2-iteration workflow with source evaluation, consensus identification, and citation transparency.
When to Use This Skill
Use this skill when the research question requires:
- Feature comparisons: "What are the differences between GraphQL and REST?"
- Moderate complexity topics: Well-documented subjects with established consensus
- Fact-checking: Verifying claims or information across sources
- Recent news/updates: "What's new in React 19?"
- Technology overviews: Understanding a framework, tool, or concept
Triggers: Keywords like "compare", "differences", "features", "what are", "fact-check", "verify", "overview"
Instructions
Iteration 1: Multi-Source Research
Objective: Formulate query variations, identify authoritative sources, evaluate quality.
Step 1: Query Formulation (2-3 Variations)
Generate 2-3 query variations with different angles:
- Variation 1 - Direct: "GraphQL vs REST API differences"
- Variation 2 - Technical: "GraphQL REST comparison performance"
- Variation 3 - Best Practices: "when to use GraphQL vs REST"
Search Operators:
site:domain.com - Specific domainsfiletype:pdf - PDF documentsintitle:"keyword" - Page titlesafter:2024 - Recent content"exact phrase" - Exact matching
Step 2: Source Identification (5-8 Sources)
Execute queries and identify 5-8 sources with diverse perspectives:
- 2-3 Official documentation sources
- 2-3 Established tech publications
- 1-2 Community sources (Stack Overflow, forums)
Step 3: Source Evaluation
Rank each source on three dimensions (0-10 scale):
Credibility (0-10):
- 10: Official docs, peer-reviewed
- 7-9: Established publications (MDN, Smashing Magazine)
- 4-6: Technical blogs, Stack Overflow
- 1-3: Unverified sources
Freshness (0-10):
- 10: Last 3 months
- 7-9: Last 6-12 months
- 4-6: Last 1-2 years
- 1-3: Older than 2 years
Relevance (0-10):
- 10: Directly addresses question with examples
- 7-9: Addresses with partial detail
- 4-6: Tangentially related
- 1-3: Minimal value
Overall Quality = (Credibility × 0.5) + (Freshness × 0.2) + (Relevance × 0.3)
Step 4: Extract Key Findings
For each source, extract 2-3 key findings with inline citations:
GraphQL uses a single endpoint [1] while REST uses multiple resource-specific endpoints [2].
GraphQL allows clients to specify exact data needs [1][3], reducing over-fetching compared to REST [2].
Iteration 2: Verification & Synthesis
Objective: Cross-reference findings, identify consensus, generate recommendations.
Step 5: Cross-Reference (3+ Sources)
For each key finding:
- Consensus: 3+ sources agree → strong signal
- Partial agreement: 2 sources agree → moderate confidence
- Outlier: Single source → flag as unverified or context-specific
Example:
**Consensus View** (5 sources): GraphQL reduces network overhead for complex data needs [1][2][3][4][5]
**Partial Agreement** (2 sources): GraphQL has steeper learning curve [2][6]
**Outlier** (1 source): REST always faster for simple queries [7] - context: depends on caching strategy
Step 6: Identify Contradictions
When sources conflict:
- Check dates (newer may reflect updates)
- Assess authority (official > community)
- Look for context (scenario-dependent)
- Present both views with citations
Example:
**Contradiction Noted**: Source A [1] recommends GraphQL for microservices, while Source B [2] suggests REST
is simpler for microservice communication. Context: GraphQL better for client-facing APIs [1],
REST better for service-to-service internal communication [2].
Step 7: Generate Recommendations
Create actionable recommendations based on verified findings:
Critical (High Confidence - 3+ Sources):
Important (Moderate Confidence - 2 Sources):
Enhancements (Context-Specific):
Step 8: Citation Reference Table
Create structured citation table:
[1] **GraphQL Official Docs** - https://graphql.org/learn/
Author/Org: GraphQL Foundation
Date: 2025-01-15
Excerpt: "GraphQL is a query language for APIs and a runtime..."
[2] **REST API Best Practices** - https://restfulapi.net/
Author/Org: REST API Tutorial
Date: 2024-11-20
Excerpt: "REST uses HTTP methods to operate on resources..."
Step 9: Completeness Validation
Check completeness (target ≥ 85%):
If <85% complete: Note gaps in output, do NOT iterate further (max 2 iterations for standard mode)
Output Template
# Web Research Analysis (Standard Mode)
**Research Mode**: standard
**Objective**: {1-sentence: what was researched}
---
## Key Findings
{2-3 paragraph synthesis with inline citations [1][2][3]}
**Consensus Views**: {areas where 3+ sources agree}
**Contradictions**: {conflicting information with context}
---
## Methodology
**Queries Executed**: {count} query variations
- {query 1 with operators}
- {query 2 with operators}
**Sources Consulted**: {count} total ({count} authoritative, {count} recent)
**Iterations**: 2 (multi-source verification complete)
---
## Verified Sources
| # | Title | Author/Org | Date | Credibility | Freshness | Relevance | Overall |
|---|-------|------------|------|-------------|-----------|-----------|---------|
| [1] | {title} | {author} | {date} | {score} | {score} | {score} | {calc} |
| [2] | {title} | {author} | {date} | {score} | {score} | {score} | {calc} |
---
## Actionable Recommendations
### Critical (Do First) {count}
- [ ] {Specific recommendation with rationale} [1][2]
### Important (Do Next) {count}
- [ ] {Specific recommendation with rationale} [3][4]
### Enhancements (Nice to Have) {count}
- [ ] {Specific recommendation with rationale} [5]
---
## Citations
[1] **{Source Title}** - {URL} ({Author/Org}, {Date})
Excerpt: "{relevant quote}"
[2] **{Source Title}** - {URL} ({Author/Org}, {Date})
Excerpt: "{relevant quote}"
{...continue...}
Examples
Example 1: Feature Comparison
Scenario: "What are the main differences between GraphQL and REST APIs?"
Process:
Iteration 1 - Multi-Source Research:
Queries (3 variations):
- "GraphQL vs REST API differences"
- "GraphQL REST comparison 2025"
- "when to use GraphQL vs REST"
Sources Identified (8):
- GraphQL Official Docs (Cred: 10, Fresh: 10, Rel: 10) [1]
- MDN REST Guide (Cred: 10, Fresh: 9, Rel: 10) [2]
- Apollo GraphQL Blog (Cred: 9, Fresh: 10, Rel: 10) [3]
- Smashing Magazine Article (Cred: 8, Fresh: 8, Rel: 9) [4]
- Stack Overflow Discussion (Cred: 6, Fresh: 7, Rel: 8) [5]
- Dev.to Comparison (Cred: 7, Fresh: 10, Rel: 9) [6]
- API Design Patterns Book (Cred: 9, Fresh: 6, Rel: 9) [7]
- Hacker News Thread (Cred: 6, Fresh: 10, Rel: 7) [8]
Key Findings Extracted:
- GraphQL single endpoint vs REST multiple endpoints [1][2]
- GraphQL client-specified queries reduce over-fetching [1][3][4]
- REST simpler caching due to HTTP standards [2][7]
- GraphQL steeper learning curve [3][6][8]
Iteration 2 - Verification & Synthesis:
Cross-Reference:
- Consensus (7 sources): GraphQL reduces over/under-fetching [1][2][3][4][5][6][7]
- Consensus (5 sources): REST has better caching support [2][4][5][7][8]
- Partial Agreement (3 sources): GraphQL better for complex data needs [1][3][6]
Contradictions:
- Performance: GraphQL faster [3][6] vs REST faster [7][8]
Context: Depends on use case - GraphQL for complex client needs, REST for simple CRUD
Recommendations Generated:
- Critical: Use GraphQL for complex client data requirements with nested relationships [1][3]
- Important: Use REST for simple CRUD operations with well-defined resources [2][7]
- Enhancement: Consider REST for public APIs needing extensive caching [2][7]
Completeness: 92% (8 sources, all findings verified, contradictions explained)
Output: Standard Mode Context File with key findings, 8 source citations, consensus views, contradiction analysis, recommendations
Example 2: Technology Update Research
Scenario: "What's new in React 19?"
Process:
Iteration 1:
Queries:
- "React 19 new features"
- site:react.dev "React 19" "what's new"
- "React 19 changes" after:2024
Sources (6):
- React Official Blog [1]
- React GitHub Changelog [2]
- Vercel Blog [3]
- React Newsletter [4]
- Dev Community Posts [5][6]
Findings:
- New React Compiler (automatic optimization) [1][2]
- Improved Server Components [1][3]
- Actions API for form handling [1][4]
- use() Hook for async data [1][2]
Iteration 2:
Cross-Reference:
- Consensus (4 sources): React Compiler auto-optimizes components [1][2][3][4]
- Consensus (3 sources): Actions simplify form state management [1][4][6]
Completeness: 88%
Output: Standard Mode summary with React 19 features, 6 citations, migration considerations
Best Practices
- Balance Breadth and Depth: Aim for 5-8 sources, not 20+ (diminishing returns)
- Prioritize Quality Over Quantity: 3 authoritative sources beat 10 low-quality ones
- Verify Before Recommending: Every recommendation needs 2-3 source citations minimum
- Note Recency: For technology topics, flag sources >1 year old
- Consensus Matters: Findings supported by 3+ sources are high-confidence
- Context Contradictions: Don't hide conflicts - explain why sources disagree
- Stay Focused: Stick to 2 iterations max - if incomplete, note gaps
Common Patterns
Pattern 1: Technology Comparison (A vs B)
Iteration 1:
- Query: "{A} vs {B}", "{A} {B} comparison", "when to use {A} vs {B}"
- Sources: Official docs for both, tech publications, community discussions
- Extract: Key differences, use cases, trade-offs
Iteration 2:
- Cross-reference: Consensus on strengths/weaknesses
- Synthesize: Decision matrix (when to use A vs B)
Pattern 2: Fact-Checking
Iteration 1:
- Query: Claim keywords, source verification queries
- Sources: Primary sources, authoritative refs, fact-checking sites
- Extract: Evidence for/against claim
Iteration 2:
- Verify: Check dates, authority, context
- Synthesize: True/False/Nuanced with citations
Pattern 3: Technology Overview
Iteration 1:
- Query: "{Technology} overview", "{Technology} use cases", "{Technology} best practices"
- Sources: Official docs, getting started guides, tutorials
- Extract: What it is, when to use, how it works
Iteration 2:
- Verify: Consensus on core concepts
- Synthesize: Quick reference guide with citations
Troubleshooting
Issue 1: Conflicting Information Across Sources
- Check publication dates (recent vs outdated)
- Assess source authority (official vs opinion)
- Look for context (scenario-specific recommendations)
- Present both views with citations and context
Issue 2: Insufficient Authoritative Sources
- Broaden search to related domains
- Include slightly older but highly credible sources
- Note in output: "Limited recent authoritative sources; recommendations based on 2023-2024 data"
Issue 3: Completeness Below 85%
- Note specific gaps in output
- Do NOT iterate beyond 2 iterations (standard mode limit)
- Recommend user consider deep mode for comprehensive analysis
Integration Points
- WebSearch Tool: Execute all queries through WebSearch
- Context7 MCP: Supplement with official framework docs when applicable
- Source Table: Track sources in structured format for quality tracking
- Context Files: Persist findings to
.agent/Session-{name}/context/research-web-analyst.md
Key Terminology
- Query Variation: Different phrasing/angle of same search
- Source Quality Score: Composite metric (credibility + freshness + relevance)
- Consensus View: Finding supported by 3+ sources
- Contradiction: Conflicting claims requiring contextual explanation
- Completeness: Percentage of research objectives met (target ≥85%)
- Iteration: Research cycle (max 2 for standard mode)
Additional Resources
