Automate topic research into a synthesized markdown report.
npx skills add https://github.com/ksm0709/dotfiles --skill deep-research-ksm0709
Please help me install this Agent Skill. Skill: deep-research Source: https://github.com/ksm0709/dotfiles/tree/main/opencode/config/skills/deep-research Command: npx skills add https://github.com/ksm0709/dotfiles --skill deep-research-ksm0709
Deep Research automates end-to-end topic investigation by planning, collecting sources, and synthesizing findings into a markdown report, reducing manual effort and ensuring reproducibility.
Run the Deep Research Skill on a topic to trigger the end-to-end research including plan creation, data collection, and final report generation.
High-intent search queries and answers about installing and using this skill.
You can automate deep research with LLMs by triggering an end-to-end workflow that generates a research plan, performs parallel web scraping across multiple sources, and synthesizes findings into a markdown report.
Automated web scraping for academic literature surveys works best through a structured approach: generating a research plan, collecting sources in parallel, and consolidating the data into a synthesized LLM-based report.
Yes, automated research workflows require either a GEMINI_API_KEY or OPENAI_API_KEY in the environment for LLM-based reporting, though the skill can run in a mock mode when these keys are absent.
Yes, cross-domain investigations can run without manual data collection by automating the entire pipeline from plan generation to parallel web scraping and final markdown report synthesis.
In mock mode, the research workflow runs without GEMINI_API_KEY or OPENAI_API_KEY, meaning the LLM-based reporting and data synthesis phases will not execute with actual language model capabilities.
Parallel scraping improves technology trend analysis by fetching multiple web sources simultaneously during the data collection phase, significantly increasing speed and scalability before final report generation.
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