LLM Wiki Karpathy Knowledge Base Runtime
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About this skill
Problem
Personal knowledge bases often suffer from scattered source material in raw/, weak note structure, unanswered questions that are never persisted, and drift in source IDs, manifests, and indexes. llm-wiki-karpathy targets Vaults managed by the LLM Wiki Karpathy runtime and turns reading, compiling, maintaining, searching, and persisting notes into a controlled workflow.
Core Capabilities and Workflow
The skill operates the runtime-managed Vault through kb_* tools. Its main paths include:
- Inspection and diagnosis: kb_status and kb_lint report counts, placeholder content, missing representations, and structural issues.
- Source note compilation: text assets use kb_prepare_source, kb_read_raw, and kb_upsert_source_note; PDFs and images use source bundles and representation artifacts before compiling grounded source notes.
- Search and persistence: kb_search and kb_read_notes retrieve evidence; answers can be archived in wiki/outputs/ or promoted into concept, entity, or synthesis pages.
- Continuous maintenance: kb_repair_source_ids runs as a dry run before apply, and kb_rebuild_indexes keeps collection indexes consistent.
Applicability and Caveats
It fits incremental maintenance, batch source-note backfill, AI-topic cleanup, and book-review batches with clear scope. Do not modify raw/ directly, and do not write into wiki/ or .llm-kb/representations/ with generic file tools. When scope is unclear, the skill favors small, high-confidence batches instead of expanding an underspecified request into a long-running rewrite.
Use Cases
- Maintain an AI-topic Vault by linting placeholder titles and broken links, then repairing a small batch of AI source notes.
- After collecting book reviews, compile the first 10 missing source notes in the book-review raw folder and rebuild indexes.
- Answer a scoped query from the wiki, read supporting notes, then persist reusable conclusions as concept or synthesis pages.
- When source IDs or manifest entries drift, dry-run repairs first, apply only if the plan looks correct, and rebuild indexes.
Best For
- Researchers maintaining a personal AI knowledge base who want to organize scattered notes into concept, entity, and synthesis pages.
- Editors curating book-review material who need to compile source notes in batches and keep indexes current.
- Engineers using LLM Wiki Karpathy Vaults who need to lint issues and repair manifest or source-ID drift.
- Product managers maintaining multi-source knowledge bases who want to write grounded answers back into output or derived pages.
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