Comprehensive web research via Parallel Search API. Use when user requests parallel search for deep multi-source research, technical analysis, learning new topics, current events, or comparative studies. Returns LLM-ready ranked URLs with extended excerpts (up to 30K chars). Single API call handles multiple query angles with automatic deduplication.
Resources
7Install
npx skillscat add otrebu/agents/parallel-search Install via the SkillsCat registry.
Parallel Search
Web research using Parallel's Search API with extended excerpts (up to 30K chars per result).
When to Use
Use for comprehensive research on:
- Technical topics requiring multiple perspectives
- New frameworks, libraries, technologies
- Comparative analysis
- Current events
- Documentation synthesis
Prerequisites
Required:
PARALLEL_API_KEYenvironment variable- Get key: https://platform.parallel.ai/
Dependencies: Auto-installed via pnpm
Workflow
When user requests research:
- Analyze question to identify main objective
- Generate 3-5 targeted query angles for multi-perspective coverage
- Execute single bash command with
--objectiveand--queriesparameters - API returns deduplicated results from parallel execution
- Analyze extended excerpts and synthesize findings
- Save report to
docs/research/parallel/TIMESTAMP-topic.md
Usage
Comprehensive Research (Recommended)
cd plugins/knowledge-work/skills/parallel-search
pnpm tsx scripts/search.ts \
--objective "Production RAG system architecture" \
--queries \
"RAG chunking strategies" \
"RAG evaluation metrics" \
"RAG deployment challenges" \
"RAG vector database selection"The API executes all queries in parallel and returns deduplicated results automatically.
Quick Single Query
pnpm tsx scripts/search.ts --objective "When was the UN founded?"Processor Levels
# Default: pro (balanced quality/speed)
pnpm tsx scripts/search.ts --objective "..."
# Ultra: maximum quality for critical research
pnpm tsx scripts/search.ts --objective "..." --processor ultraParameters
--objective(required): Main search objective (natural language, be specific)--queries: Additional query angles (max 5, 200 chars each)--processor: lite/base/pro/ultra (default: pro)--max-results: Results per search (default: 15)--max-chars: Excerpt length per result (default: 5000, max: 30000)
Output Format
Returns markdown with:
- Search metadata (objective, result count, execution time)
- Top domains distribution
- Ranked results:
- Title and URL
- Domain
- Extended excerpts (joined with double newlines)
- Rank
Query Generation Strategy
For broad topics: Generate queries covering different aspects
Example: "RAG systems"
- Objective: "Production RAG system architecture overview"
- Queries: "chunking strategies", "evaluation metrics", "deployment patterns", "vector databases"
For comparisons: Generate queries for each option plus general comparison
Example: "PostgreSQL vs MongoDB"
- Objective: "PostgreSQL vs MongoDB comparison"
- Queries: "PostgreSQL use cases", "MongoDB use cases", "relational vs document databases"
For current events: Use temporal and source diversity
Example: "Latest AI developments"
- Objective: "Recent AI model releases and benchmarks"
- Queries: "GPT-4 updates", "open source LLMs", "AI safety research", "industry adoption"
Research Persistence
After synthesis, save report:
- Get timestamp: Use
timestampskill for YYYYMMDDHHMMSS format - Sanitize topic: Use
sanitizeForFilenamefrom formatter.ts (kebab-case, 50 char limit) - Save to:
docs/research/parallel/TIMESTAMP-topic.md - Include: Findings, sources with URLs, analysis
Error Handling
Missing API key:
export PARALLEL_API_KEY="your-key-here"Rate limit exceeded: Wait for reset time (shown in error message)
Network errors: Retry with --processor lite for faster response
Validation errors: Check constraints (max 5 queries, 200 chars each)
Constraints
- Max 5 queries per request
- Max 200 chars per query
- Max 30K chars per excerpt (not guaranteed above 30K)
- Rate limits depend on API plan tier
- Requires internet connection
Best Practices
- Use specific objectives: "Production RAG architecture" > "RAG systems"
- Leverage all 5 query slots for comprehensive coverage
- Use
--max-charsup to 30000 for deep content analysis - Adapt processor level to urgency: pro for most, ultra for critical
- Save multi-query research for future reference
Implementation
Files:
types.ts- Interfaces and error typesparallel-client.ts- API client with validationformatter.ts- Markdown output formattinglog.ts- CLI loggingsearch.ts- CLI entry point
Testing:
pnpm test