Parallel Research

    2

    Automate deep research by decomposing complex topics into parallel sub-tasks and generating a structured report.

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    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    parallel-research

    Example session with this skill installed

    Perform a deep dive on the state of AI agent frameworks for 2024-2025 and save the report to my research folder.

    • Read your context and instructions
    • Compiled the parallel-research

    Research complete.
    Topic: AI Agent Frameworks (2024-2025)
    Angles researched: 4
    Sources consulted: 12
    Report saved: ./research/ai-agent-frameworks-2025-05-10.md

    Executive Summary: CrewAI and LangGraph have emerged as leaders in multi-agent orchestration...

    Key findings
    • LangGraph leads for stateful...

    Connects securely to your tools. The creator never sees your data.

    About this skill

    What it does

    Parallel Research is a powerful orchestration skill designed to turn slow, manual deep-dives into a structured, automated workflow. Instead of researching in a linear loop, this skill decomposes a complex topic into 3-5 distinct sub-questions (angles), executes targeted searches for each simultaneously, fetches full-page content for deeper analysis, and synthesizes everything into a professional Markdown report saved directly to your workspace.

    Why use this skill

    Standard LLM web searches often suffer from "rabbit hole" syndrome—getting bogged down in one source or missing the bigger picture. This skill enforces a "parallel execution" pattern used by professional analysts to ensure comprehensive coverage. It handles the heavy lifting of:

    • Angle Decomposition: Automatically identifying market, technical, and strategic dimensions of your query.
    • Fact Verification: Cross-referencing sources to flag contradictions and information gaps.
    • Structured Output: Generating a citation-rich report with executive summaries and recommended next steps.
    • Persistence: Saving the final output to disk so your research lives beyond the chat window.

    Supported Tools

    This skill leverages a full suite of agentic capabilities including WebSearch for discovery, WebFetch for deep content extraction, and Filesystem access (Read/Write/Bash) for report generation and directory management.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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