magi
MAGI Multi-Perspective Analysis for Gemini CLI
Open source Open in the app JSON README (API)
About
MAGI Multi-Perspective Analysis for Gemini CLI
Details
- Kind
- Plugins
- Topic
- AI, RAG & memory
- Publisher
- bolivartech
- Origin
- gemini
- Category
- ferramentas
- Version
- 0.1.12
- Last push
- 2026-05-10T05:52:00Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-30 14:13:39
- Updated
- 2026-08-30 14:13:39
- Origin id
bolivartech/magi-gem
README
# MAGI — Multi-Perspective Analysis Extension for Gemini CLI [](https://github.com/BolivarTech/MAGI-Gem/actions/workflows/verify.yml) [](https://www.python.org/downloads/) [](#tests) [](https://docs.astral.sh/ruff/) [](#license) A Gemini CLI extension that implements a **multi-perspective analysis system** inspired by the [MAGI supercomputers](https://evangelion.fandom.com/wiki/Magi) from *Neon Genesis Evangelion*. Three specialized AI agents independently analyze the same problem from complementary — and deliberately adversarial — perspectives, then synthesize their verdicts via weight-based majority vote. --- ## Why Three Adversarial Perspectives? ### The MAGI in Evangelion In *Neon Genesis Evangelion* (1995, Hideaki Anno / Gainax), the MAGI are three supercomputers that govern Tokyo-3's critical decisions. Each embodies a different facet of their creator, Dr. Naoko Akagi: **Melchior** (the scientist), **Balthasar** (the mother), and **Caspar** (the woman). Decisions require consensus — no single perspective dominates. ### The Theory in Practice The adversarial multi-perspective model addresses well-documented cognitive biases in software engineering: | Bias | How MAGI Mitigates It | |------|----------------------| | **Confirmation bias** | Three agents with different evaluation criteria are unlikely to share the same blind spots | | **Anchoring** | Agents analyze independently — no agent sees the others' output before forming its own verdict | | **Groupthink** | Caspar (Critic) is designed to be adversarial; its role is to find fault, not agree | | **Optimism bias** | The weight-based scoring penalizes reject (-1) more heavily than approve (+1), making negative signals harder to override | The key insight is that **disagreement between agents is a feature, not a failure**. When Melchior (Scientist) approves but Caspar (Critic) rejects, the dissent surfaces a genuine tension between technical correctness and risk tolerance. --- ## Agents | Agent | Codename | Lens | Personality | |-------|----------|------|-------------| | **Melchior** | Scientist | Technical rigor and correctness | Precise, evidence-based, favors proven solutions | | **Balthasar** | Pragmatist | Practicality and maintainability | Grounded, trade-off oriented, advocates for the team | | **Caspar** | Critic | Risk, edge cases, and failure modes | Adversarial by design, finds what others miss | --- ## Installation ### Official Method (Recommended) Install the extension directly from the official repository with auto-updates enabled: ```powershell gemini extensions install https://github.com/BolivarTech/MAGI-Gem.git --consent --auto-update ``` ### Development Method If you want to contribute or modify the extension: ```powershell git clone https://github.com/BolivarTech/MAGI-Gem.git cd MAGI-Gem gemini extensions install . --consent ``` --- ## Updating To update the extension to the latest version (e.g., v0.1.9) from the official repository: ```powershell gemini extensions update magi ``` If you are using the **Development Method**, just pull the latest changes and reinstall: ```powershell git pull origin main gemini extensions uninstall magi gemini extensions install . --consent ``` --- ## Usage Invoke with `gemini magi` or trigger the skill naturally: ```bash gemini magi "review this code" gemini "Give me three perspectives on this design" ``` ### Modes | Mode | When to Use | Example | |------|-------------|---------| | `code-review` | Reviewing code or diffs | "magi review this PR" | | `design` | Evaluating architecture decisions | "magi analyze this migration plan" | | `analysis` | General problem analysis, trade-offs | "magi should we use Redis or Postgres?" | --- ## How It Works 1. **Progressive Disclosure** — The `SKILL.md` identifies the need for deep analysis. 2. **Parallel Dispatch** — The orchestrator `run_magi.py` launches 3x `gemini -p` calls in parallel using your existing CLI authentication. 3. **Synthesis** — Agent outputs are validated, weighted, and synthesized into a final report. --- ## Project Structure ``` gemini-extension.json -- Extension manifest skills/magi/ SKILL.md -- Skill entry point and workflow agents/ -- Agent system prompts (.md) scripts/ -- Core Python logic (consensus, reporting, etc.) ``` --- ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. --- ## Credits The MAGI concept originates from *Neon Genesis Evangelion* (1995) by Hideaki Anno / Gainax. This extension is a creative adaptation of that decision-making philosophy for modern software engineering.