Summary This project automates the generation of professional, multi-lingual deep research reports, addressing AI report shortcomings like shallowness, high cost, and unreliable data. It serves independent developers, researchers, and small teams, offering a cost-effective, efficient alternative to expensive databases and generic AI summaries, delivering comprehensive analysis in minutes.

How It Works The skill employs a four-stage pipeline: outline analysis, robust data collection, parallel chapter writing, and final assembly/validation. Data acquisition uses a novel dual-engine parallel search (SearXNG + Exa) with quality-triggered fallback for comprehensive online data. A key differentiator is its offline mode, enabling analysis of local documents (PDF, DOCX, TXT, MD) without internet access. The approach prioritizes depth and accuracy, embedding sourced facts and performing rigorous validation.

Quick Start & Requirements Installation is streamlined via AI prompts: OpenCode users can paste a command into chat for automatic setup; others (Claude Code, Cursor, Codex CLI) adapt the skill via prompts. Prerequisites include an LLM runtime (e.g., OpenCode), Scrapling (online mode), and integrated SearXNG/Exa search engines. Reports generate rapidly, typically within 8-20 minutes depending on the depth mode (quick, standard, deep).

Highlighted Details

• Multi-lingual Output: Generates reports in 19 languages, auto-detecting topic language for native-like professional writing.
• Verifiable Data: Every numerical data point is cited with a clickable reference; numbers lacking sources are excluded.
• Balanced Perspectives: Incorporates opposing viewpoints or controversies per chapter for nuanced analysis.
• Confidence Grading: A concluding table categorizes information by confidence level (high, medium, low).
• Data Integrity: Automated detection and prevention of common data errors (unit mix-ups, misattribution).

Maintenance & Community Created by "hoolulu." Community discussion is linked via "LINUX DO." No specific details on active maintenance, contributor growth, sponsorships, or formal community channels are provided.

Licensing & Compatibility Released under the permissive MIT License, facilitating broad adoption and modification. Highly compatible with commercial use and integration into closed-source projects.

Limitations & Caveats Primary integration is through specific AI coding environments. The project creator ("hoolulu") is the sole contributor mentioned, indicating a potential bus factor. No explicit mention of alpha status or known bugs is present.