Orchestrate multi-agent research workflows and write findings to report.md.
npx skills add https://github.com/dppdppd/rpm --skill deep-research-dppdppd
Please help me install this Agent Skill. Skill: deep-research Source: https://github.com/dppdppd/rpm/tree/main/skills/deep-research Command: npx skills add https://github.com/dppdppd/rpm --skill deep-research-dppdppd
Exhaustive multi-agent research orchestration to deliver thorough, verifiable findings with traceable sources, reducing manual scoping and toil.
Initiate a full deep-research run on a topic to generate a structured findings report.
High-intent search queries and answers about installing and using this skill.
Automate multi-agent research by orchestrating parallel exploration, cross-checks, and citation audits through a phase-driven workflow. This protocol enforces URL fetching policies and validation gates to produce a verifiable final report with traceable sources saved on disk.
Coordinate deep research workflows using a structured protocol that moves through setup, scope, discovery, validation, and synthesis phases. This multi-agent coordination ensures thorough exploration and cross-checks, yielding source-grounded findings written as on-disk artifacts.
A phase-driven research workflow ensures source verification by enforcing strict phase gates between discovery, validation, and synthesis. It applies URL fetching policies and citation audits during cross-checks, guaranteeing that the final findings report contains only verified, traceable sources.
Yes, you can use multi-agent research for complex topic investigations and external lookups. The orchestration protocol automates exhaustive scoping and discovery across topics, applying cross-checks and citation audits to deliver thorough, source-grounded findings for any research request.
No external dependencies are required to run automated deep research orchestration. The workflow operates independently to orchestrate multi-agent phases from scope to synthesis, writing progress and final findings directly to on-disk artifacts for review.
Final findings and progress artifacts are stored directly on disk during a deep research run. The workflow writes progress tracking under docs/rpm/research and outputs a final structured findings report as report.md for review.
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