v0.1.0 - Local self-learning knowledge-base harness
v0.1.0 - Local self-learning knowledge-base harness
Self-Learning Library v0.1.0 introduces the first public shape of a local-first knowledge-base harness for AI-assisted work.
What is included
- A layered Obsidian-compatible Markdown vault.
- Codex-facing root instructions in
AGENTS.md. - A project harness workflow in
PROJECT_HARNESS_WORKFLOW.md. - Lightweight local retrieval with
tools/kb_rag.py. - Full graph-style iteration and rule promotion with
tools/paper_iteration.py. - PowerShell entrypoint
run_paper_iteration.ps1. - Example exported manuscript-assistance vault.
- Documentation for lightweight retrieval, full iteration, no-regression guards, and domain adaptation.
- Security and publishing guidance.
Who should try it
This project is useful if you want an AI assistant to:
- retrieve relevant rules before a task;
- avoid repeating past mistakes;
- separate concrete evidence from stable rules;
- detect unresolved conflicts;
- promote repeated lessons into reusable knowledge;
- keep a local, inspectable, file-backed memory structure.
Current limitations
- The included vault comes from a paper-writing workflow, although the mechanism is domain-independent.
- Convenience scripts are Windows/PowerShell-first.
- Windows users should enable Git long paths and clone into a short local path because the example vault contains descriptive long note filenames.
- The repository currently includes a real exported example vault, so users should review privacy and publishing guidance before adapting the pattern.
- Retrieval and rule promotion are intentionally simple and local-first.
Suggested repository description
Local-first self-learning knowledge-base harness for AI agents. Retrieve rules, avoid regressions, and promote repeated lessons into stable reusable knowledge.
Suggested topics
ai-agent, ai-memory, knowledge-base, self-learning, local-first, codex, rag, obsidian, markdown, workflow, research, writing, automation, python