| name | knowledge-graph |
| description | Knowledge graph and smart memory management using graphify + Obsidian-inspired patterns. Use when: setting up a knowledge graph, managing memory health, cross-linking notes, compiling wiki pages from scattered notes, adding structured frontmatter, or running memory health checks. Triggers on: 'knowledge graph', 'graphify', 'wiki', 'cross-link', 'memory health', 'frontmatter', 'compile notes', 'wikilinks'. |
Knowledge Graph Skill
Turn any workspace into a queryable knowledge graph with smart memory management.
Combines graphify (code/doc graph) with three Obsidian-inspired patterns.
Installation
In pa-skills repo, the skill is at the root: pa-skills/knowledge-graph/
(not under skills/). Copy it to your workspace:
cp -r knowledge-graph/ /path/to/your/workspace/skills/knowledge-graph/
Prerequisites
pip install graphifyy
graphify claw install
Important: graphify claw install appends a ## graphify section to your AGENTS.md.
This is intentional โ it tells your agent to check the graph before reading raw files.
Review the changes after running it.
.graphifyignore
Create .graphifyignore in your workspace root to exclude noise:
node_modules/
.git/
media/
.tmp/
tmp/
graphify-out/
pa-skills/
skills-external/
*.pdf
*.ogg
*.jpg
*.png
Critical: If you have both pa-skills/knowledge-graph/ and skills/knowledge-graph/
on disk, add pa-skills/ to .graphifyignore to avoid duplicate nodes in the graph.
If your repo clone lives under .tmp/pa-skills/, exclude .tmp/ too or the duplicate noise will still leak in.
Components
1. Knowledge Graph (graphify)
Build and query a knowledge graph from code + docs.
graphify update .
graphify query "how does X connect to Y"
graphify path "ModuleA" "ModuleB"
graphify explain "concept"
graphify update .
Full semantic extraction (with LLM) produces richer cross-doc connections.
See graphify claw install output for AGENTS.md integration rules.
2. Auto Cross-Linker
Scans notes and adds [[wikilinks]] for known concepts from the graph.
python3 scripts/wiki_crosslinker.py --build-index
python3 scripts/wiki_crosslinker.py --daily
python3 scripts/wiki_crosslinker.py --all-daily
Concepts come from: graphify nodes, skill names, project names, contacts.
Only links document-type nodes with 4+ character names. Skips code internals.
3. Wiki Compiler (Karpathy Pattern)
"Compile once, query forever" โ instead of RAG every time, compile scattered
mentions into structured wiki pages per topic.
python3 scripts/wiki_compiler.py --scan
python3 scripts/wiki_compiler.py --compile
python3 scripts/wiki_compiler.py --compile "onboarding"
python3 scripts/wiki_compiler.py --status
Output: wiki/<topic>.md with frontmatter, timeline of mentions, and graph connections.
Each page is a self-contained summary โ query it directly instead of scanning raw notes.
4. Structured Frontmatter
Adds YAML frontmatter with auto-detected tags to notes.
python3 scripts/note_frontmatter.py --all-daily
python3 scripts/note_frontmatter.py --projects
python3 scripts/note_frontmatter.py --query tag=onboarding
python3 scripts/note_frontmatter.py --query type=project
Auto-tags: graphify, crons, ops, whatsapp, calendar, content, monday,
onboarding, pa-network, infra, memory, eval, self-improve, skills, owner.
5. Memory Health Checker
Runs on the knowledge graph to detect memory problems.
python3 scripts/memory_health.py
python3 scripts/memory_health.py --quick
Checks: orphan nodes, daily note gaps, stale MEMORY.md entries,
weak communities, unreferenced skills, recent vs old activity.
Recommended Crons
# Daily: memory health check (04:00 UTC)
daily-memory-health: python3 scripts/memory_health.py --quick
# Weekly: wiki compilation + cross-linking (Sun 03:00 UTC)
weekly-wiki-compile:
1. python3 scripts/wiki_crosslinker.py --build-index
2. python3 scripts/wiki_compiler.py --compile
3. python3 scripts/note_frontmatter.py --all-daily
4. python3 scripts/note_frontmatter.py --projects
5. graphify update .
Token Impact
| Operation | Without | With | Reduction |
|---|
| Topic recall | ~15K tokens (scan daily notes) | ~200 tokens (wiki page) | 75x |
| Architecture query | ~411K tokens (read all files) | ~155 tokens (graph query) | 2,655x |
| "What happened with X" | grep all notes | frontmatter query + wiki page | ~50x |