llm-wiki

name	llm-wiki
description	Karpathy's LLM Wiki — build and maintain a persistent, interlinked markdown knowledge base. Ingest sources, query compiled knowledge, and lint for consistency.
version	3.0.0
author	Hermes Agent
license	MIT
metadata	{"hermes":{"tags":["wiki","knowledge-base","research","notes","markdown","rag-alternative","batch-ingest"],"category":"research","related_skills":["obsidian","arxiv","agentic-research-ideas"],"config":[{"key":"wiki.path","description":"Path to the LLM Wiki knowledge base directory","default":"~/wiki","prompt":"Wiki directory path"}]}}

Karpathy's LLM Wiki

Build and maintain a persistent, compounding knowledge base as interlinked markdown files. Based on Andrej Karpathy's LLM Wiki pattern.

Unlike traditional RAG (which rediscovers knowledge from scratch per query), the wiki compiles knowledge once and keeps it current. Cross-references are already there. Contradictions have already been flagged. Synthesis reflects everything ingested.

Division of labor: The human curates sources and directs analysis. The agent summarizes, cross-references, files, and maintains consistency.

When This Skill Activates

Use this skill when the user:

• Asks to create, build, or start a wiki or knowledge base
• Asks to ingest, add, or process a source into their wiki
• Asks a question and an existing wiki is present at the configured path
• Asks to lint, audit, or health-check their wiki
• References their wiki, knowledge base, or "notes" in a research context
• Wants to batch-ingest hundreds/thousands of documents into wiki format

Wiki Location

Configured via skills.config.wiki.path in ~/.hermes/config.yaml (prompted during hermes config migrate or hermes setup):

skills:
  config:
    wiki:
      path: ~/wiki

Falls back to ~/wiki default. The resolved path is injected when this skill loads — check the [Skill config: ...] block above for the active value.

Before setting wiki.path, verify the target actually exists. A stale default like ~/wiki is easy to leave behind even when the real wiki lives elsewhere.

For the workspace-hub multi-wiki layout, the preferred root is usually:

skills:
  config:
    wiki:
      path: /mnt/local-analysis/workspace-hub/knowledge/wikis

This points at the domain-wiki root (engineering/, marine-engineering/, maritime-law/, naval-architecture/, etc.) rather than a single flat ~/wiki folder.

The wiki is just a directory of markdown files — open it in Obsidian, VS Code, or any editor. No database, no special tooling required.

CLI Tool (workspace-hub)

The llm-wiki CLI at scripts/knowledge/llm_wiki.py provides 6 commands for operating wikis programmatically. All commands use the pattern:

uv run scripts/knowledge/llm_wiki.py <command> --wiki <domain>

Command	Purpose
init <domain>	Scaffold a new domain wiki under knowledge/wikis/<domain>/
status --wiki <d>	Report page counts, source counts, link density
ingest <file> --wiki <d>	Copy source file + generate LLM processing instructions
query "..." --wiki <d>	Keyword search across wiki pages with relevance ranking
lint --wiki <d>	Health checks (orphans, empty pages, index consistency, link density)
batch-ingest <file> --wiki <d> --batch-size N	Bulk-create source pages from metadata JSONL/JSON/YAML

batch-ingest is designed for scale:

• Checkpoint-based resume (.checkpoint.jsonl in wiki root)
• --dry-run for preview
• Progress reporting every batch
• Skips already-processed records
• Used to ingest 22K conference papers → 12K source pages in one run
• Proven: 100 records/batch, ~400 records per 10 seconds

Location: knowledge/wikis/<domain>/ (not ~/wiki). This is a multi-wiki ecosystem — multiple domain wikis coexist under knowledge/wikis/. Force-add to git despite .gitignore since wiki content is the compounding artifact.

Architecture

Multi-Wiki Pattern

In workspace-hub, wikis are organized as a multi-domain ecosystem under knowledge/wikis/<domain>/, not a single ~/wiki. Each domain (marine-engineering, maritime-law, naval-architecture) has its own complete three-layer structure. Cross-wiki linking connects related topics across domains.

Three Layers (per domain wiki)

knowledge/wikis/<domain>/
├── CLAUDE.md             # Schema: conventions, structure rules, domain config
├── raw/                  # Layer 1: Immutable source material
│   ├── papers/           # PDFs, standards, papers
│   ├── standards/        # Standards documents
│   ├── articles/         # Web articles, clippings
│   └── assets/           # Images, diagrams
└── wiki/                 # Layer 2: The LLM-maintained wiki
    ├── index.md          # Content catalog with sectioned entries
    ├── log.md            # Chronological action log (append-only)
    ├── overview.md       # Domain synthesis summary
    ├── entities/         # Entity pages (things: equipment, orgs, vessels)
    ├── concepts/         # Concept pages (ideas: methods, principles)
    ├── sources/          # Source summary pages (one per ingested document)
    ├── comparisons/      # Filed query outputs
    └── visualizations/   # matplotlib plots, Marp slide decks

Layer 1 — Raw Sources: Immutable. The agent reads but never modifies these. Layer 2 — The Wiki: Agent-owned markdown files. Created, updated, and cross-referenced by the agent. Layer 3 — The Schema: CLAUDE.md defines structure, conventions, and tag taxonomy.

Scaling Pattern (learned from 12K+ source ingestion)

• Metadata-first approach: Don't extract PDF content (hits 5-min timeouts on large files). Instead, read structured metadata (titles, topics, sizes) and create wiki source pages.
• Proven at scale: 22K conference metadata records → 12K unique source pages, skipping 10K+ duplicates via checkpoint file.
• Batch size: 100 records per batch, progress reported every batch.
• Checkpoint resume: .checkpoint.jsonl tracks processed records by unique ID.
• Index management: Updates index.md after each batch, not after every record.
• Git considerations: Wiki pages must be force-added (git add -f) even if .gitignore excludes the wikis directory. Wiki content is the compounding artifact.
• Workspace-hub hook gotcha: knowledge/wikis/<domain>/CLAUDE.md files are wiki schema/config files generated by llm-wiki init, not harness adapter files. If the repo hook .claude/hooks/check-claude-md-limits.sh applies the 20-line harness limit to all CLAUDE.md paths, commits touching wiki CLAUDE.md can fail with a false positive. The minimal safe fix is to exclude ^knowledge/wikis/ from that hook's staged-file filter so harness limits still apply to real adapter files while wiki schema files remain editable.

Resuming an Existing Wiki (CRITICAL — do this every session)

When the user has an existing wiki, always orient yourself before doing anything:

① Read CLAUDE.md (or SCHEMA.md) — understand the domain, conventions, and tag taxonomy. ② Read index.md — learn what pages exist and their summaries. ③ Scan recent log.md — read the last 20-30 entries to understand recent activity.

WIKI="${wiki_path:-$HOME/wiki}"
# Orientation reads at session start
read_file "$WIKI/SCHEMA.md"
read_file "$WIKI/index.md"
read_file "$WIKI/log.md" offset=<last 30 lines>

Only after orientation should you ingest, query, or lint. This prevents:

• Creating duplicate pages for entities that already exist
• Missing cross-references to existing content
• Contradicting the schema's conventions
• Repeating work already logged

For large wikis (100+ pages), also run a quick search_files for the topic at hand before creating anything new.

Initializing a New Wiki (via CLI)

uv run scripts/knowledge/llm_wiki.py init <domain>

This scaffolds the full three-layer structure, creates CLAUDE.md with domain-specific schema, initializes index.md and log.md, and creates the raw/ and wiki/ subdirectories.

After scaffolding:

1. Add some sources: ingest <file> --wiki <domain>
2. For bulk: batch-ingest metadata.jsonl --wiki <domain> --batch-size 100
3. Check health: lint --wiki <domain>

Core Operations

1. Ingest

When the user provides a source (URL, file, paste), integrate it into the wiki:

① Capture the raw source:

• URL → use web_extract to get markdown, save to raw/articles/
• PDF → use web_extract (handles PDFs), save to raw/papers/
• Pasted text → save to appropriate raw/ subdirectory
• Name the file descriptively: raw/articles/karpathy-llm-wiki-2026.md

② Discuss takeaways with the user — what's interesting, what matters for the domain. (Skip this in automated/cron contexts — proceed directly.)

③ Check what already exists — search index.md and use search_files to find existing pages for mentioned entities/concepts. This is the difference between a growing wiki and a pile of duplicates.

④ Write or update wiki pages:

• New entities/concepts: Create pages only if they meet the Page Thresholds in SCHEMA.md (2+ source mentions, or central to one source)
• Existing pages: Add new information, update facts, bump updated date. When new info contradicts existing content, follow the Update Policy.
• Cross-reference: Every new or updated page must link to at least 2 other pages via [[wikilinks]]. Check that existing pages link back.
• Tags: Only use tags from the taxonomy in SCHEMA.md

⑤ Update navigation:

• Add new pages to index.md under the correct section, alphabetically
• Update the "Total pages" count and "Last updated" date in index header
• Append to log.md: ## [YYYY-MM-DD] ingest | Source Title
• List every file created or updated in the log entry

⑥ Report what changed — list every file created or updated to the user.

A single source can trigger updates across 5-15 wiki pages. This is normal and desired — it's the compounding effect.

2. Query

When the user asks a question about the wiki's domain:

① Read index.md to identify relevant pages. ② For wikis with 100+ pages, also search_files across all .md files for key terms — the index alone may miss relevant content. ③ Read the relevant pages using read_file. ④ Synthesize an answer from the compiled knowledge. Cite the wiki pages you drew from: "Based on [[page-a]] and [[page-b]]..." ⑤ File valuable answers back — if the answer is a substantial comparison, deep dive, or novel synthesis, create a page in queries/ or comparisons/. Don't file trivial lookups — only answers that would be painful to re-derive. ⑥ Update log.md with the query and whether it was filed.

3. Lint

When the user asks to lint, health-check, or audit the wiki:

① Orphan pages: Find pages with no inbound [[wikilinks]] from other pages.

# Use execute_code for this — programmatic scan across all wiki pages
# Scan all .md files in entities/, concepts/, comparisons/, queries/
# Extract all [[wikilinks]] — build inbound link map
# Pages with zero inbound links are orphans

② Broken wikilinks: Find [[links]] that point to pages that don't exist.

③ Index completeness: Every wiki page should appear in index.md. Compare the filesystem against index entries.

④ Frontmatter validation: Every wiki page must have all required fields (title, created, updated, type, tags, sources). Tags must be in the taxonomy.

⑤ Stale content: Pages whose updated date is >90 days older than the most recent source that mentions the same entities.

⑥ Contradictions: Pages on the same topic with conflicting claims. Look for pages that share tags/entities but state different facts.

⑦ Page size: Flag pages over 200 lines — candidates for splitting.

⑧ Tag audit: List all tags in use, flag any not in the SCHEMA.md taxonomy.

⑨ Log rotation: If log.md exceeds 500 entries, rotate it.

⑩ Report findings with specific file paths and suggested actions, grouped by severity (broken links > orphans > stale content > style issues).

⑪ Append to log.md: ## [YYYY-MM-DD] lint | N issues found

4. Bulk Ingest (CLI-based)

For large-scale ingestion (100+ sources), use the llm-wiki batch-ingest CLI:

# Dry-run first to preview
uv run scripts/knowledge/llm_wiki.py batch-ingest metadata.jsonl --wiki <domain> --batch-size 100 --dry-run

# Then run for real (resume-safe via .checkpoint.jsonl)
uv run scripts/knowledge/llm_wiki.py batch-ingest metadata.jsonl --wiki <domain> --batch-size 100

The CLI handles:

• ✅ Checkpoint-based resume (safe to interrupt/restart)
• ✅ Progress reporting every batch — 100 records per batch at ~400 records per 10s
• ✅ Skip already-processed records (10K+ duplicates in a 22K run)
• ✅ Batch index.md/log.md updates (efficient)
• ✅ --dry-run mode to preview filenames and counts

5. Seed Migration (YAML → Wiki)

For structured YAML knowledge seeds in knowledge/seeds/:

1. Parse the YAML to extract entries
2. Group entries by domain/category
3. For each entry: create entity/concept/source page
4. Cross-link with existing wiki pages
5. Update index.md with all migrated entries
6. Append structured entries to log.md

Proven pattern: 18 mooring failure entries → 4 wiki pages (source + 2 concepts + 1 entity) 10 law cases + 6 conventions → 20 wiki pages with cross-references

Working with the Wiki

Searching

# Find pages by content
search_files "transformer" path="$WIKI" file_glob="*.md"

# Find pages by filename
search_files "*.md" target="files" path="$WIKI"

# Find pages by tag
search_files "tags:.*alignment" path="$WIKI" file_glob="*.md"

# Recent activity
read_file "$WIKI/log.md" offset=<last 20 lines>

Seed Migration Pattern

For structured YAML seeds (like knowledge/seeds/naval-architecture-resources.yaml):

1. Parse YAML categories (textbooks, hydrostatics, portals, ship plans, etc.)
2. Create concept pages for each major topic domain (5-6 pages)
3. Create source pages for each individual resource (17-36 pages per seed)
4. Update index.md with structured tables
5. Update log.md with migration entry

This approach is much faster than full PDF extraction and creates structured wiki pages that can be enhanced later with LLM content.

Cross-Wiki Linking

When managing multiple domain wikis, look for natural connections:

• Deepwater Horizon → marine-engineering (lng-carrier-mooring) + maritime-law (OPA 90)
• Mooring failures → marine-eng + naval-arch stability concepts
• Classification rules → naval-arch + maritime-law liability conventions

Obsidian Integration

The wiki directory works as an Obsidian vault out of the box:

• [[wikilinks]] render as clickable links
• Graph View visualizes the knowledge network
• YAML frontmatter powers Dataview queries
• The raw/assets/ folder holds images referenced via ![[image.png]]

For best results:

• Set Obsidian's attachment folder to raw/assets/
• Enable "Wikilinks" in Obsidian settings (usually on by default)
• Install Dataview plugin for queries like TABLE tags FROM "entities" WHERE contains(tags, "company")

Pitfalls

• Never modify files in raw/ — sources are immutable. Corrections go in wiki pages.
• Always orient first — read SCHEMA + index + recent log before any operation in a new session. Skipping this causes duplicates and missed cross-references.
• Always update index.md and log.md — skipping this makes the wiki degrade. These are the navigational backbone.
• Don't create pages for passing mentions — follow the Page Thresholds in SCHEMA.md. A name appearing once in a footnote doesn't warrant an entity page.
• Don't create pages without cross-references — isolated pages are invisible. Every page must link to at least 2 other pages.
• Frontmatter is required — it enables search, filtering, and staleness detection.
• Tags must come from the taxonomy — freeform tags decay into noise. Add new tags to SCHEMA.md first, then use them.
• Keep pages scannable — a wiki page should be readable in 30 seconds. Split pages over 200 lines. Move detailed analysis to dedicated deep-dive pages.
• Ask before mass-updating — if an ingest would touch 10+ existing pages, confirm the scope with the user first.
• Rotate the log — when log.md exceeds 500 entries, rename it log-YYYY.md and start fresh. The agent should check log size during lint.
• Handle contradictions explicitly — don't silently overwrite. Note both claims with dates, mark in frontmatter, flag for user review.
• PDF extraction timeout — Large PDFs hit 5-min sandbox timeouts. Use metadata-first approach for speed. Full extraction = enhancement when async workers exist.
• Git force-add required — Wiki dirs may be in .gitignore. Use git add -f to commit content. Wiki content is the compounding artifact and must be tracked.
• Batch size trade-off — 100 records/batch balances speed with index update frequency. Smaller = more frequent updates, slower. Larger = fewer index commits, risk more on crash.
• Low link density early — Newly created wikis naturally have low link density. This resolves as cross-references grow during normal ingest operations.
• YAML seed migration is fast — Converting structured YAML to wiki pages is 10x faster than PDF extraction. Use this pattern whenever seeds exist.