self-improving-ai

Consolidate AI learnings by logging model selection, prompt engineering, and RAG pipeline configurations into a `.learnings` directory to generate a standalone skill unit with YAML frontmatter for reuse.

Document LLM prompt engineering patterns by categorizing them into reusable playbooks and promotion workflows, enabling cross-project consistency by extracting recurring tuning patterns into a standalone skill.

Yes, you can log RAG pipeline tuning adjustments by creating an entry in the `.learnings` directory, which captures inference tuning and guardrail adjustments to promote into a reusable skill.

Structure metadata for AI model selection runbooks by ensuring the SKILL.md file includes YAML frontmatter with at least a name and description, plus optional assets, references, and scripts for discovery and integration.

Yes, the scope explicitly covers multimodal processing documentation, allowing you to log and categorize guardrail adjustments alongside code-generation and evaluation benchmarks within the skill.

Limitations include the requirement to maintain precise metadata in YAML frontmatter for discovery and the need to explicitly define categorization and promotion criteria to ensure the consolidated AI learnings remain reusable across projects.