Most knowledge bases mix personal thinking, external evidence, published work, and AI-generated text in the same pile. This template gives each a clear role—and gives AI agents rules for working without silently polluting originals or publication workflows.

Originals stay trustworthy. The wiki stays readable. The graph stays queryable. AI output stays reviewable.

The screenshot shows a mature vault using this architecture. A fresh clone starts with a small synthetic example and grows as you add reviewed notes, links, and tags.

git clone https://github.com/mrbear1024/ai-content-kb.git
cd ai-content-kb

No application is required to read the vault. Use plain Markdown, open the root as an Obsidian vault, or use the intended Codex workflow below.

1. Open the Codex desktop app.
2. Select + in Projects.
3. Choose Use an existing folder.
4. Select the cloned repository root.
5. Start a new task and say:

Inspect the project structure, read the knowledge-base rules,
make no changes, and list the available workflows.

Codex reads repository AGENTS.md guidance before starting work. The root instructions then route it to the rest of this project's rules. See the official Codex documentation.

The phrases below are natural-language workflows defined by this repository, not native slash commands. No plugin is required.

flowchart LR
    R["raw/\nOwner input"]
    S["sources/\nExternal evidence"]
    P["products/\nReviewed output"]
    T[".kb/staging/\nAI candidates"]
    W["wiki/\nHuman interface"]
    G[".kb/links/\nMachine graph"]

    R -->|ingest| T
    S -->|ingest| T
    P -->|backfill| T
    T -->|human review| W
    T -->|human review| G
    W -->|compose| P
    G -->|retrieve evidence| P

Read the architecture rationale and graph schema for details.

add to knowledge base: this attachment is an external source.
Preserve provenance, check existing aliases, and keep generated wiki and graph data in staging.

Review .kb/staging/wiki/ and .kb/staging/links/ before promotion.

Compose from the knowledge base:
Write an article for AI product managers about Context Engineering.
Separate my judgments from external claims, cite repository paths,
and create a source plan and outline before drafting.

Unreviewed prose goes to .kb/staging/drafts/, accepted work moves to raw/drafts/ for development, and only publication-ready work belongs in products/.

Read the content creation guide.

Migrate legacy knowledge base: inventory the provided directory in read-only mode.
Do not move, copy, delete, or modify files.
Propose a 20–50 file pilot and stop for review.

Migration follows backup → inventory → mapping review → pilot → validation → reviewed batches → cutover.

Read the migration guide.

Open the repository root with Open folder as vault, then select Graph view in the left ribbon. The included .obsidian/graph.json colors nodes by raw, sources, products, and wiki.

Obsidian visualizes Markdown links and tags. Typed .kb/links/*.yaml relationships are a separate machine layer and do not automatically become Obsidian lines.

The deciding factors are review status, intended use, human ownership, and publication readiness—not whether AI helped write the text.

This repository is a reference architecture and working template, not a hosted knowledge-management application. Automation is intentionally minimal: validate the content roles and review workflow with files first, then add manifests, search, embeddings, or a graph database only when real queries justify them.

Obsidian and Codex are optional interfaces. The durable system is the repository itself.

• Found a problem or have a proposal? Open an issue.
• Pull requests improving the information model, examples, schemas, lint rules, and documentation are welcome.
• Do not submit private notes, copyrighted captures, credentials, absolute home paths, or generated databases containing personal metadata.
• Before publishing a fork, use the public release checklist.

Maintained by mrbear1024 and contributors.

MIT. This project is not affiliated with or endorsed by Obsidian or OpenAI.