| name | parallel-web |
| description | Search the web, extract URL content, and run deep research using the Parallel Chat API and Extract API. Use for ALL web searches, research queries, and general information gathering. Provides synthesized summaries with citations. |
| allowed-tools | ["Read","Write","Edit","Bash"] |
Parallel Web Systems API
Overview
This skill provides access to Parallel Web Systems APIs for web search, deep research, and content extraction. It is the primary tool for all web-related operations in the scientific writer workflow.
Primary interface: Parallel Chat API (OpenAI-compatible) for search and research.
Secondary interface: Extract API for URL verification and special cases only.
API Documentation: https://docs.parallel.ai
API Key: https://platform.parallel.ai
Environment Variable: PARALLEL_API_KEY
When to Use This Skill
Use this skill for ALL of the following:
- Web Search: Any query that requires searching the internet for information
- Deep Research: Comprehensive research reports on any topic
- Market Research: Industry analysis, competitive intelligence, market data
- Current Events: News, recent developments, announcements
- Technical Information: Documentation, specifications, product details
- Statistical Data: Market sizes, growth rates, industry figures
- General Information: Company profiles, facts, comparisons
Use Extract API only for:
- Citation verification (confirming a specific URL's content)
- Special cases where you need raw content from a known URL
Do NOT use this skill for:
- Academic-specific paper searches (use
research-lookup which routes to Perplexity for purely academic queries)
- Google Scholar / PubMed database searches (use
citation-management skill)
Two Capabilities
1. Web Search (search command)
Search the web via the Parallel Chat API (base model) and get a synthesized summary with cited sources.
Best for: General web searches, current events, fact-finding, technical lookups, news, market data.
python scripts/parallel_web.py search "latest advances in quantum computing 2025"
python scripts/parallel_web.py search "compare EV battery chemistries NMC vs LFP" --model core
python scripts/parallel_web.py search "renewable energy policy updates" -o results.txt
python scripts/parallel_web.py search "AI regulation landscape" --json -o results.json
Key Parameters:
objective: Natural language description of what you want to find
--model: Chat model to use (base default, or core for deeper research)
-o: Output file path
--json: Output as JSON
Response includes: Synthesized summary organized by themes, with inline citations and a sources list.
2. Deep Research (research command)
Run comprehensive multi-source research via the Parallel Chat API (core model) that produces detailed intelligence reports with citations.
Best for: Market research, comprehensive analysis, competitive intelligence, technology surveys, industry reports, any research question requiring synthesis of multiple sources.
python scripts/parallel_web.py research "comprehensive analysis of the global EV battery market"
python scripts/parallel_web.py research "AI adoption in healthcare 2025" -o report.md
python scripts/parallel_web.py research "latest funding rounds in AI startups" --model base
python scripts/parallel_web.py research "renewable energy storage market in Europe" --json -o data.json
Key Parameters:
query: Research question or topic
--model: Chat model to use (core default for deep research, or base for faster results)
-o: Output file path
--json: Output as JSON
3. URL Extraction (extract command) — Verification Only
Extract content from specific URLs. Use only for citation verification and special cases.
For general research, use search or research instead.
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings"
python scripts/parallel_web.py extract "https://docs.example.com/api" --full-content
python scripts/parallel_web.py extract "https://paper-url.com" --objective "methodology" -o extracted.md
Model Selection Guide
The Chat API supports two research models. Use base for most searches and core for deep research.
| Model | Latency | Strengths | Use When |
|---|
base | 15s-100s | Standard research, factual queries | Web searches, quick lookups |
core | 60s-5min | Complex research, multi-source synthesis | Deep research, comprehensive reports |
Recommendations:
search command defaults to base — fast, good for most queries
research command defaults to core — thorough, good for comprehensive reports
- Override with
--model when you need different depth/speed tradeoffs
Python API Usage
Search
from parallel_web import ParallelSearch
searcher = ParallelSearch()
result = searcher.search(
objective="Find latest information about transformer architectures in NLP",
model="base",
)
if result["success"]:
print(result["response"])
for src in result["sources"]:
print(f" {src['title']}: {src['url']}")
Deep Research
from parallel_web import ParallelDeepResearch
researcher = ParallelDeepResearch()
result = researcher.research(
query="Comprehensive analysis of AI regulation in the EU and US",
model="core",
)
if result["success"]:
print(result["response"])
print(f"Citations: {result['citation_count']}")
Extract (Verification Only)
from parallel_web import ParallelExtract
extractor = ParallelExtract()
result = extractor.extract(
urls=["https://docs.example.com/api-reference"],
objective="API authentication methods and rate limits",
)
if result["success"]:
for r in result["results"]:
print(r["excerpts"])
MANDATORY: Save All Results to Sources Folder
Every web search and deep research result MUST be saved to the project's sources/ folder.
This ensures all research is preserved for reproducibility, auditability, and context window recovery.
Saving Rules
| Operation | -o Flag Target | Filename Pattern |
|---|
| Web Search | sources/search_<topic>.md | search_YYYYMMDD_HHMMSS_<brief_topic>.md |
| Deep Research | sources/research_<topic>.md | research_YYYYMMDD_HHMMSS_<brief_topic>.md |
| URL Extract | sources/extract_<source>.md | extract_YYYYMMDD_HHMMSS_<brief_source>.md |
How to Save (Always Use -o Flag)
CRITICAL: Every call to parallel_web.py MUST include the -o flag pointing to the sources/ folder.
python scripts/parallel_web.py search "latest advances in quantum computing 2025" \
-o sources/search_20250217_143000_quantum_computing.md
python scripts/parallel_web.py research "comprehensive analysis of the global EV battery market" \
-o sources/research_20250217_144000_ev_battery_market.md
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings" \
-o sources/extract_20250217_143500_example_article.md
Why Save Everything
- Reproducibility: Every claim in the final document can be traced back to its raw source material
- Context Window Recovery: If context is compacted mid-task, saved results can be re-read from
sources/
- Audit Trail: The
sources/ folder provides complete transparency into how information was gathered
- Reuse Across Sections: Saved research can be referenced by multiple sections without duplicate API calls
- Cost Efficiency: Avoid redundant API calls by checking
sources/ for existing results
- Peer Review Support: Reviewers can verify the research backing every claim
Logging
When saving research results, always log:
[HH:MM:SS] SAVED: Search results to sources/search_20250217_143000_quantum_computing.md
[HH:MM:SS] SAVED: Deep research report to sources/research_20250217_144000_ev_battery_market.md
Before Making a New Query, Check Sources First
Before calling parallel_web.py, check if a relevant result already exists in sources/:
ls sources/
Integration with Scientific Writer
Routing Table
| Task | Tool | Command |
|---|
| Web search (any) | parallel_web.py search | python scripts/parallel_web.py search "query" -o sources/search_<topic>.md |
| Deep research | parallel_web.py research | python scripts/parallel_web.py research "query" -o sources/research_<topic>.md |
| Citation verification | parallel_web.py extract | python scripts/parallel_web.py extract "url" -o sources/extract_<source>.md |
| Academic paper search | research_lookup.py | Routes to Perplexity sonar-pro-search |
| DOI/metadata lookup | parallel_web.py extract | Extract from DOI URLs (verification) |
When Writing Scientific Documents
- Before writing any section, use
search or research to gather background information — save results to sources/
- For academic citations, use
research-lookup (which routes academic queries to Perplexity) — save results to sources/
- For citation verification (confirming a specific URL), use
parallel_web.py extract — save results to sources/
- For current market/industry data, use
parallel_web.py research --model core — save results to sources/
- Before any new query, check
sources/ for existing results to avoid duplicate API calls
Environment Setup
export PARALLEL_API_KEY="your_api_key_here"
pip install openai
pip install parallel-web
Get your API key at https://platform.parallel.ai
Error Handling
The script handles errors gracefully and returns structured error responses:
{
"success": false,
"error": "Error description",
"timestamp": "2025-02-14 12:00:00"
}
Common issues:
PARALLEL_API_KEY not set: Set the environment variable
openai not installed: Run pip install openai
parallel-web not installed: Run pip install parallel-web (only needed for extract)
Rate limit exceeded: Wait and retry (default: 300 req/min for Chat API)
Complementary Skills
| Skill | Use For |
|---|
research-lookup | Academic paper searches (routes to Perplexity for scholarly queries) |
citation-management | Google Scholar, PubMed, CrossRef database searches |
literature-review | Systematic literature reviews across academic databases |
scientific-schematics | Generate diagrams from research findings |