Nubenetes Intelligent Curation: Meta-Instructions & Learning Roadmap

This file contains the accumulated instructions and long-term vision for the autonomous maintenance of Nubenetes.com. AI agents must consult this document in every iteration to ensure learning continuity.

🧠 Core Mandates

Information Preservation: NEVER delete summaries, comments, or stars (🌟) accompanying links. The bot should only update the URL or reorganize the item's position, never delete the descriptive context.
Persistent Learning: Use src/memory/health_learning.json to store knowledge about domains (anti-bot blocks, successful strategies) and navigation patterns.
Minimum Viable Quality (MVQ): For GitHub/GitLab repositories, the bot MUST check the last commit date. If the repository has had NO activity (commits) in more than 4 years, it must receive a significantly lower impact_score and be deprioritized, even if the content remains technically relevant. This ensures Nubenetes stays fresh and focuses on maintained projects.
Style Guide (Descriptive Summaries): All injected summaries MUST follow a Descriptive style. Avoid generic "clickbait" or action-oriented phrases (e.g., "Check this out"). Instead, provide a clear, neutral description of what the resource contains, its scope, and why it is technically significant for the Kubernetes ecosystem.
Semantic Interlinking: The bot should identify related categories for each resource. While the full entry is injected into the primary category, a short reference ("See also: Title in [Category]") should be added to up to two related categories to improve site navigation.
Visual Health Dashboard: Every curation run MUST generate a local report.html (outside the repo) for visual validation of metrics, quality (MVQ), and AI decisions.
Total Resilience: The workflow must be able to continue even if there are individual errors in link or file validations. Prioritize generating a result (PR) even if it is partial.
Repository Consolidation: In case of a failure in a deep GitHub/GitLab link, always try to validate the repository root before considering it dead. We prefer stable links to repository roots over volatile deep-links.
URL Expansion: All shortened links (t.co, bit.ly, buff.ly, etc.) MUST be expanded to their original long version before being evaluated or injected. This ensures inventory homogeneity and improves global deduplication precision.
Official Language (English Only): All injected content (titles, descriptions, headers), execution logs, and automated communications (PRs) MUST be exclusively in ENGLISH. Nubenetes is a global resource and linguistic consistency is critical.
Workflow-Config Synchronization: The GitHub Actions curation workflow form (agentic_cron.yml) MUST remain perfectly synchronized with the curation sources configuration file (data/curation_sources.yaml). Any addition, removal, or renaming of topics/categories in the configuration file requires a corresponding update to the workflow's input fields (checkboxes) to ensure users can toggle those sources manually. This maintains consistency between data-driven sources and the UI trigger.
V2 Elite Maintenance: The Nubenetes V2 (Agentic Elite) edition is a derived view of the V1 archive. It is managed via the src/v2_optimizer.py script and stored in the v2-docs/ directory. AI agents MUST NOT modify v2-docs/ directly via standard curation workflows; they must only use the agentic_v2_builder.yml workflow to perform the periodic "Elite Selection" process. Standard curation and cleaning workflows must always target the docs/ directory as the primary source of truth.
Detailed Logging for V2: When running the V2 Optimizer, agents MUST use unbuffered logging and detailed output messages. If the optimizer returns '0 links kept', the agent MUST investigate the logs to determine if it was due to AI selection or a parsing/API error.
Persistent V2 Caching: The V2 Optimizer MUST use a persistent cache file (data/v2_cache.json) to store AI evaluations (year, quality, category). This is mandatory to minimize API costs and ensure execution speed across 15k+ links.
GitHub Metadata Enrichment: For all github.com resources, the bot MUST attempt to fetch real-time metadata (stars, last commit) using the GitHub API. This data must be included in the V2 rendering to provide current context.
Resilient Link Health: Every V2 generation cycle MUST perform asynchronous health checks. The bot MUST use identity rotation (User-Agents) and multiple attempts (3x) with backoff to minimize false negatives. Only definitive 404 Not Found errors lead to removal; other failures (timeouts, 403s) result in the link being preserved but flagged as [OFFLINE?] to ensure maximum technical preservation. GitHub and 'Foundational' resources are exempt from removal based on health checks.

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No Link Limits: There are NO hard limits on the number of links per page or per section (##/###). Nubenetes is built to host thousands of references.
TOC Consistency: Every .md page (including the main index docs/index.md) MUST maintain an internal Table of Contents (TOC) at the beginning. This TOC must include all sections (##) and subsections (###) nested correctly using a numbered list format with working anchors.
Relative References & Anchors:
- Internal: Use simplified lowercase slugs for anchors (remove special characters, replace spaces with hyphens).
- External/Cross-page: Ensure references between different .md files are correct and up-to-date.
Main Index Maintenance (docs/index.md):
- docs/index.md is the landing page for nubenetes.com and the primary entry point. It MUST be updated whenever a new page is added or a major category is renamed.
- Top Links Preservation: The "Motivation" section in docs/index.md contains highly relevant links. These MUST be preserved even if they are duplicated in other thematic pages. The AI should prioritize keeping this index curated and high-signal.
Intelligent Internal Reorganization:
- No File Splitting: Do NOT create new .md files unless strictly necessary for a major new theme. Prefer creating new sub-sections (## or ###) within existing files to maintain order.
- Semantic Polish: When a section becomes excessively flat, the AI should propose and implement a reorganization into logical sub-sections purely to improve readability and classification, without restricting the volume of content.
Navigation Integrity: Every structural change must be reflected in:
- mkdocs.yml (Navigation menu).
- v2-mkdocs.yml (V2 Navigation menu).
- docs/index.md (Main Table of Contents).
- The internal TOC of the modified page.
Orphan Curation: Periodically audit the docs/ folder to find unlinked files and integrate them into the navigation based on their topic.

📊 Mermaid Diagram Best Practices

To ensure robust rendering across GitHub, VSCode, and MkDocs, follow these standards when creating or modifying Mermaid diagrams:

Node Label Quoting: ALWAYS wrap node labels in double quotes (e.g., A["Label Text"]) if they contain spaces, special characters (parentheses, brackets, dots), or reserved words. This prevents parse errors in more restrictive environments.
Explicit Direction: Use graph TD (Top-Down) for deep hierarchies and graph LR (Left-to-Right) for flat process flows to optimize readability and prevent horizontal clipping.
Label Length: Keep labels concise (under 25 characters). If a longer description is needed, use a tooltip or sub-text.
Syntax Validation: Before committing, verify the syntax using a Mermaid previewer. Common pitfalls include:
- Unescaped brackets [ or ] inside labels.
- Missing semicolons or newlines between node definitions.
- Recursive loops without proper termination.
Integration with MkDocs: Ensure pymdownx.superfences is configured in mkdocs.yml to support Mermaid blocks within Markdown.

📝 README Synchronization & Maintenance Protocols

The README.md is the primary entry point for Nubenetes and must accurately reflect the state of both the V1 (Exhaustive) and V2 (Elite) editions. AI agents and contributors MUST follow these protocols:

1. Mandatory Updates on `develop` Branch

Before any Pull Request is merged from develop to master, the README.md must be audited and updated to reflect the latest changes. This is critical for maintaining the "Source of Truth" status.

2. Metric Recalculation

Whenever a significant curation cycle (automatic or manual) is completed:

Link Counts: Update the "Heart of Nubenetes" table with the current total link count and specialized page count.
Top Categories: Recalculate the density of the top 10 categories.
Historical Growth: Add/update the monthly surge rows in the "2026: The Agentic Monthly Surge" table.
Reference Estimates: Use the established ratio (~4.13 links/commit) to estimate new reference growth if exact numbers aren't extracted by the bot.

3. Visual & Diagram Sync

Mermaid Charts: If new top-level categories are created or existing ones grow significantly, update the "Major Ecosystem Pillars" and "Specialized Sub-ecosystems" pie charts.
Architecture Flow: If the Agentic Stack or the deployment lifecycle changes (e.g., new workflows, different dependencies), the corresponding Mermaid diagrams MUST be updated immediately.
Robustness: Follow the "Mermaid Diagram Best Practices" (node quoting, explicit direction) as defined in this document.

4. V1 vs V2 Alignment

Ensure any changes to the V2VisionEngine or the elite selection criteria are reflected in the "Dual-Edition Architecture" section.
Update the "Comparison Matrix" if the technical differences between V1 and V2 evolve.

5. Automation vs Manual Intervention

Automated Updates: The Agentic Bot should ideally include a step to refresh these metrics in its curation PRs.
Manual Fallback: If a manual update is performed (emergency fixes, structural changes), the human/AI agent is responsible for manually running the metric extraction scripts and updating the README.md accordingly.

🚀 Block Evasion Strategies

The bot must rotate between profiles to avoid detection:

Desktop/Google: Standard desktop request.
Mobile/Twitter: Mobile request with Twitter Referer (high success rate).
Playwright/LinkedIn: Real navigation with JS enabled.
Firefox/Reddit: Alternative desktop profile.

📈 Learning Diary (Improvement History)

May 2026: Initial implementation of the autonomous engine with Playwright and Wayback Machine.
May 2026: Added Multidimensional Evasion system (5 attempts, profile rotation).
May 2026: Creation of AgenticCurator for navigation audit and repository consolidation.
May 2026: Generation of PRs with visual analytics (Mermaid) and Health Matrix.
May 2026: Implementation of Backup-based Curation (JSON/MD) to avoid X.com blocks.
May 2026: Implementation of multi-source curation and category-based filtering in GitHub Workflow.
May 2026: Introduction of Nubenetes V2 (Agentic Elite) architecture. Implemented persistent v2-docs/ storage, the v2_optimizer.py engine for 2026 standard filtering, and a dual-deployment pipeline to host both V1 (Exhaustive) and V2 (Elite) versions in parallel.

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