| name | wiki_graph_index |
| description | Extract Obsidian-style relationships (wikilinks, tags, aliases) into a structured AI-friendly SQLite graph database. |
| commands | {"graph":"Extract a SQLite knowledge graph from the wiki."} |
LLM Wiki — Graph Index Skill (wiki_graph_index)
Resolving script paths (read first): Commands below invoke scripts as <BIN>/X.py (and a few as <SKILLS>/...). Resolve these to absolute paths once before running anything:
<SKILL_DIR> = the directory this SKILL.md lives in.<SKILLS> = the skills/ folder containing this skill = <SKILL_DIR>/..<BIN> = the bin/ folder beside it = <SKILL_DIR>/../../bin
Do not hardcode a fixed prefix like .agents/bin or ../bin: shell relative paths resolve against the current working directory (usually the topic root), not this skill's location. Once resolved, <BIN> is typically .agents/bin when invoked from the hub root, or .claude/bin from inside a topic directory.
This skill extracts the Markdown-based knowledge graph (comprised of [[wikilinks]], tags, and aliases) into a structured SQLite database (output/graph.db) that an AI agent can easily query using standard SQL.
Usage
When the user asks to extract, index, or query the knowledge graph of their wiki:
1. Build the Graph Database
Run the deterministic python script to extract the graph from the markdown files:
python <BIN>/llm-wiki.py graph <TOPIC_DIR>
This will parse all markdown files under wiki/ (ignoring _index.md), extract frontmatter (tags, aliases) and body links, and rebuild the SQLite database located at output/graph.db.
2. Query the Graph Database
Once built, you (the AI) can query output/graph.db using Python's sqlite3 module to traverse the graph and answer the user's questions.
The database schema is as follows:
nodes(id, path, title, type, category, summary, created, updated)
id: The file path without extension (e.g., 'concepts/concept_name') or a tag (e.g., 'tag:machine-learning').
edges(source_id, target_id, type)
type can be 'wikilink' (between files) or 'has_tag' (from file to tag node).
tags(node_id, tag)aliases(node_id, alias)
Use python <BIN>/query-graph.py "<SQL>" --db <TOPIC_DIR>/output/graph.db to query the knowledge graph. Do not use direct sqlite3 command line execution.
Example:
python <BIN>/query-graph.py "SELECT source_id FROM edges WHERE target_id = 'Transformer';" --db <TOPIC_DIR>/output/graph.db
Or via a temporary Python script if you need complex graph traversal.
3. Report
Present the findings of your graph queries to the user.
Error Handling
- If any script exits with non-zero code, report the full stderr output to the user and stop.
- If a file cannot be read or parsed, log a warning and continue with remaining files.
- Do NOT silently skip errors or proceed with partial results without reporting.
