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docs: document Zero-Config Dynamic AI Discovery Engine in README and GEMINI.md

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GEMINI.md
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@@ -45,6 +45,10 @@ This file contains the accumulated instructions and long-term vision for the aut
- **Tracking Stripping**: Systematically remove UTM parameters, social media trackers (X.com, LinkedIn), and URL fragments (`#`).
- **Protocol Uniformity**: Standardize on `https://` whenever possible.
- **Merge Logic**: Metadata from multiple sources for the same canonical URL MUST be merged, prioritizing the highest star rating and most recent date.
24. **Dynamic AI Model Discovery**: To remain at the cutting edge and ensure system stability, all agents MUST use the dynamic model discovery engine.
- **Live Discovery**: Query the `models.list` API at runtime to identify actually available models for each key.
- **Scoring & Ranking**: Prioritize models using the established 2026 hierarchy (Generation 3.x > 2.x > 1.x; Pro > Flash).
- **Resilient Fallback**: Automatically transition between models and API keys upon encountering 404 (Unsupported) or 429 (Rate Limit) errors.
## 🛠️ Structural Evolution & Navigation
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README.md
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@@ -220,22 +220,25 @@ Nubenetes employs a strategic "Double-Format" protocol to ensure system reliabil
- **JSON for AI Communication**: When agents talk to Google Gemini, they utilize **JSON** as the messaging protocol. This ensures rigid data structures and prevents AI formatting errors (like indentation slips) from breaking the processing scripts.
- **YAML for Repository Storage**: Once the data is validated, it is serialized into **YAML** for the local database. This provides a clean, human-readable format that is easy to audit via Git diffs and respects the repository's aesthetic standards.
### Dynamic AI Discovery & Intelligence
To minimize API latencies and optimize quality, the system features a **Dynamic Model Discovery Engine**:
1. **Capability-First Discovery**: At the start of each run, the bot queries the Google Model Service to list all available models for the current API keys.
2. **Autonomous Scoring**: Models are ranked using a weighted algorithm:
* **Generation Score**: 3.x > 2.x > 1.x series.
* **Capability Tier**: `Pro` models (Intelligence) are prioritized over `Flash` models (Speed).
* **Stability Factor**: Stable releases are prioritized over experimental/preview versions.
3. **Intelligent Rotation**: If a model returns a 404 (Unsupported) or 429 (Rate Limit), the system automatically falls back to the next best model in the ranked list or rotates the API key.
### Dynamic AI Discovery & Optimization
To eliminate configuration overhead and ensure Nubenetes always utilizes the frontier of AI technology, the system features a **Zero-Config Dynamic Model Discovery Engine**:
1. **Live Capability Discovery**: At the start of each workflow run, the bot programmatically queries the Google Model Service API to list all models actually available to the provided API keys.
2. **Autonomous Scoring & Ranking**: Models are automatically ranked using a sophisticated 2026-standard weighted algorithm:
* **Generation Tier**: 3.1 > 3.0 > 2.5 > 1.5.
* **Capability Level**: `Pro` models (High Reasoning) are prioritized over `Flash` (High Speed).
* **Production Readiness**: Stable releases are prioritized over experimental or preview versions.
3. **Resilient Failover Protocol**: If a model returns a `404` (Unsupported) or `429` (Rate Limit), the engine immediately rotates to the next best model or switches API keys without interrupting the curation process.
```mermaid
graph LR
Start[Workflow Start] --> List[Models.list API]
List --> Score{Scoring Algorithm}
Score -->|Ranked List| Run[Process Tasks]
Run -->|429/404| Fallback[Next Model / Key]
Fallback --> Run
A[Workflow Initiation] --> B[API Model Discovery]
B --> C{Scoring Engine}
C -->|Ranked Queue| D[Task Processing]
D -->|404 / 429| E[Auto-Failover]
E -->|Next Best Model| D
E -->|Key Rotation| D
D -->|Success| F[Inventory Sync]
```
### Agentic Data Flow