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Contributing to the AI Hub

Thank you for your interest in improving the Home-Office Automation & AI Hub! We welcome contributions from both humans and AI agents.

LLM Agent Quick Start

Before changing files, read these in order:

  1. AGENTS.md — repository operating contract, checklists, and quality bar
  2. skills.md — reusable task patterns for intake, docs updates, workflow edits, and branch hygiene
  3. Standards — taxonomy and canonical-page rules

Use this sequence for most tasks:

  1. Find canonical page or confirm it does not exist.
  2. Make scoped edits for one intent only.
  3. Run relevant validation scripts.
  4. Open/merge PR only after required checks pass.

How You Can Help

  • Add New Tools: Found a tool that fits the stack? Document it using our standard template.
  • Refine Playbooks: Improve our existing automation guides with more technical detail or new variants.
  • Update Services: Ensure the documentation for self-hosted services remains accurate as versions change.
  • Improve Prompts: Optimize our LLM prompt templates for better extraction and classification results.

Automated Contributions: The Ralph-loop

This repository implements the Ralph-loop, a systematic directive for AI agents (primarily Google Jules) to close issues by performing one of three actions:

a) Do the work: Implement the requested feature, fix the bug, or perform the documentation update. b) Add links: Find the appropriate canonical location for provided external links. c) Decompose: Divide complex tasks into smaller, trackable issues with extracted context.

Daily Ingestion & Maintenance Lanes

Automation is split into specialized lanes to maintain the "High Confidence" standard:

  1. Daily Digest: Scans and summarizes external sources (GitHub, Reddit, News).
  2. Intake Bridge: Stages qualifying items in docs/new-sources/.
  3. Maintenance Run: Automates routine audits, broken link fixes, and catalog syncs.
  4. Knowledge Deepening: Targets "Shallow" or "Stale" documents for technical expansion.
  5. Quality Gates: Mandatory execution of audit_docs_quality.py and check_docs_contract.py.

Assigning a Task to Jules

You can request Jules to perform a task by: 1. Opening an Issue: Describe the task clearly (e.g., "Add documentation for Tool X" or "Fix broken link in README"). 2. Adding the Label: Apply the label jules (case-insensitive) to the issue. 3. Review the Plan: Jules will analyze the task and post a plan as a comment. Once you approve, Jules will get to work. 4. Review the PR: Jules will open a Pull Request with its changes. Depending on the automation lane and repository policy, it may auto-merge after required checks pass.

Contribution Standards

  • Precise & Technical: Avoid marketing language; focus on implementation details.
  • Cross-Link: Always link to related tools, playbooks, or architectural docs.
  • One canonical page per tool/framework/provider: All mentions elsewhere must link to the canonical page.
  • Use templates: Tool template for tools/frameworks/providers. Article template for papers and articles.
  • No stub pages: Only create a page if you have enough information to fill the template meaningfully.
  • JSON Metadata: If adding a tool, ensure you also update data/all_tools.json.

Multi-Agent KnowledgeOps Contract (Mandatory)

For AI-authored documentation updates, this contract is required:

  1. Deduplicate first: Search for existing tool/topic pages and aliases before creating new files.
  2. Keep canonical ownership: Update the existing canonical page whenever possible.
  3. Use the right template and taxonomy: Follow tool template, article template, and standards.
  4. Add auditable metadata on every AI-authored knowledge-page update:
  5. Last reviewed in YYYY-MM-DD
  6. Confidence as high, medium, or low
  7. Sources / References with at least one URL
  8. Keep PR intent narrow: Intake, curation, or audit work should be separate PRs whenever possible.

See Multi-Agent KnowledgeOps Governance for the full operating model.

AI PR Checklist

Before requesting review, AI-authored PRs must satisfy:

  • [ ] Canonical page search completed (name + aliases)
  • [ ] No duplicate canonical pages introduced
  • [ ] Correct template and taxonomy used
  • [ ] Required metadata added (Last reviewed, Confidence, Sources / References)
  • [ ] At least one high-signal source URL included
  • [ ] data/all_tools.json and mkdocs.yml updated when applicable
  • [ ] audit_docs_quality.py and check_docs_contract.py pass with 100% compliance

Every contribution helps make this hub a better operating manual for everyone.

Sources / References

Contribution Metadata

  • Last reviewed: 2026-05-28
  • Confidence: high