Execute Parallel CLI workflows for web search, extraction, and research.
npx skills add https://github.com/Signmanal/VIGIL --skill parallel-cli-signmanal
Please help me install this Agent Skill. Skill: parallel-cli Source: https://github.com/Signmanal/VIGIL/tree/main/optional-skills/research/parallel-cli Command: npx skills add https://github.com/Signmanal/VIGIL --skill parallel-cli-signmanal
This Skill eliminates the gap between VIGIL's native workflows and Parallel's advanced vendor-specific capabilities for web research, data enrichment, and entity discovery, removing the need to manually build integrations for async, agent-compatible research tasks.
Use the parallel-cli skill to launch an async deep research job on the latest security vulnerabilities for a newly identified malware strain, poll for the completed results, and summarize the findings with properly cited sources.
High-intent search queries and answers about installing and using this skill.
Async deep research and data enrichment is achieved by launching long-running jobs that process tabular datasets without blocking workflow progress. The system executes these tasks non-interactively and returns structured JSON output for easy parsing.
Agent-native web search performs structured web lookups and clean content extraction from URLs using non-interactive terminal execution. It formats results as machine-readable JSON output, enabling agents to easily parse and chain context for follow-up queries.
Yes, you can monitor recurring changes to web pages and discover web-scale sets of relevant entities. This functionality supports security operations and threat research by tracking sources over time and identifying new entities asynchronously.
Yes, parallel-cli supports non-interactive terminal execution specifically designed for security operations and threat research. It satisfies functional requirements for machine-readable output formatting and async long-running jobs without requiring manual intervention.
You can poll for completed results from an async deep research job by checking the job status periodically while completing other triage tasks. Once finished, the job returns findings with properly cited sources and structured JSON output.
Context chaining for follow-up queries relies on structured JSON output from previous non-interactive terminal executions. Limitations arise if the initial content extraction fails to return clean machine-readable data, impacting the accuracy of subsequent entity discovery operations.
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