Fast web search for current information, research, and fact-finding across the internet.
Works with
Executes single objective-based queries or multiple keyword searches in parallel, returning up to 10 results with excerpts and metadata
Supports time-sensitive filtering via --after-date and domain-specific searches with --include-domains
Outputs structured JSON with titles, URLs, publish dates, and excerpts for easy parsing and follow-up queries
Requires inline citations for every claim using
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionparallel-web-searchExecute the skills CLI command in your project's root directory to begin installation:
Fetches parallel-web-search from parallel-web/parallel-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate parallel-web-search. Access via /parallel-web-search in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Search the web for: $ARGUMENTS
Choose a short, descriptive filename based on the query (e.g., ai-chip-news, react-vs-vue). Use lowercase with hyphens, no spaces.
parallel-cli search "$ARGUMENTS" -q "<keyword1>" -q "<keyword2>" --json --max-results 10 --excerpt-max-chars-total 27000 -o "/tmp/$FILENAME.json"
The first argument is the objective — a natural language description of what you're looking for. It replaces multiple keyword searches with a single call for broad or complex queries. Add -q flags for specific keyword queries to supplement the objective. The -o flag saves the full results to a JSON file for follow-up questions.
Options if needed:
--after-date YYYY-MM-DD for time-sensitive queries--include-domains domain1.com,domain2.com to limit to specific sourcesDo not set max_output_tokens on the command execution — the output is already bounded by --max-results and --excerpt-max-chars-total. Capping output tokens will truncate the JSON and break parsing.
Parse the JSON from stdout. For each result, extract:
CRITICAL: Every claim must have an inline citation. Use markdown links like Title pulling only from the JSON output. Never invent or guess URLs.
Synthesize a response that:
End with a Sources section listing every URL referenced:
Sources:
- [Source Title](https://example.com/article) (Feb 2026)
- [Another Source](https://example.com/other) (Jan 2026)
This Sources section is mandatory. Do not omit it.
After the Sources section, mention the output file path (/tmp/$FILENAME.json) so the user knows it's available for follow-up questions.
If parallel-cli is not found, install and authenticate:
curl -fsSL https://parallel.ai/install.sh | bash
If unable to install that way, install via pipx instead:
pipx install "parallel-web-tools[cli]"
pipx ensurepath
Then authenticate:
parallel-cli login
Or set an API key: export PARALLEL_API_KEY="your-key"
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
supercent-io/skills-template
kostja94/marketing-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
Useful defaults in parallel-web-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend parallel-web-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added parallel-web-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: parallel-web-search is focused, and the summary matches what you get after install.
Registry listing for parallel-web-search matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: parallel-web-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
parallel-web-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: parallel-web-search is focused, and the summary matches what you get after install.
parallel-web-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
parallel-web-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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