Knowledge graph memory
Skill broomva/control-metalayer/.skills/knowledge-graph-memory
skills.sh skill for control-loop metalayer setup in agentic repos
npx -y skills add broomva/control-metalayer --skill knowledge-graph-memoryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Bridge Claude Code conversation logs to an Obsidian knowledge graph for persistent agent memory. Transforms .entire/ event logs and ~/.claude/ transcripts into searchable, wikilinked markdown docs with full conversation threads, tool call details, and session metadata. Use when setting up conversation history capture, configuring knowledge graph traversal for agents, building episodic memory from session logs, or connecting agent work to an Obsidian vault. Triggers on "conversation history", "session logs to obsidian", "knowledge graph memory", "agent episodic memory", "conversation bridge", "session documentation", or "knowledge traversal".
SKILL.md
7.7 KB, as published. Nobody here has run it
Knowledge Graph Memory
Broomva Stack Layer 2 (Memory & Consciousness) — part of the 24-skill Broomva Stack.
Bridge agent conversation logs into an Obsidian knowledge graph, giving every new session access to the full reasoning history of all prior sessions.
Default vault: ~/broomva-vault/ (symlinks into ~/broomva/ workspace per project).
Quick Start
Install the bridge script
Copy scripts/conversation_history.py into your project's scripts/ directory:
cp scripts/conversation_history.py <your-repo>/scripts/conversation-history.py
chmod +x <your-repo>/scripts/conversation-history.py
Generate conversation docs
python3 scripts/conversation-history.py --force # Full regeneration
python3 scripts/conversation-history.py # Incremental (skip existing)
python3 scripts/conversation-history.py --dry-run # Preview without writing
python3 scripts/conversation-history.py --limit 10 # Last 10 sessions only
Wire into git hooks
Add to .githooks/pre-push (or equivalent):
if [ -f scripts/conversation-history.py ] && command -v python3 >/dev/null 2>&1; then
echo "[pre-push] Updating conversation history..."
python3 scripts/conversation-history.py 2>/dev/null && \
git add docs/conversations/ 2>/dev/null || true
fi
Add smoke check
Add to your smoke gate script:
if [ -f docs/conversations/Conversations.md ]; then
echo "[ok] conversation history MOC present"
else
echo "[warn] docs/conversations/Conversations.md missing"
fi
What It Generates
Conversations.md (MOC)
An index of all sessions grouped by date:
## 2026-03-16
| Session | Branch | Turns | Duration | Topic |
|---------|--------|-------|----------|-------|
| [[session-2026-03-16-21f4eb55]] | `feature/sti-799` | 44 | 4h 15m | Implement AI Core... |
Per-Session Docs
Each session doc contains:
- YAML frontmatter — session_id, branch, tags, wikilinks
- Metadata table — date, duration, turns, tools, attribution stats
- Conversation thread — chronological timeline:
> [!quote]User prompts> [!info]Assistant reasoning (all text blocks, not truncated)> [!example]Tool calls (expandable, with full input details)
- Files touched — all files read/written/modified
- Commits — checkpoint IDs and timestamps
Data Sources
The bridge script reads two sources:
1. .entire/logs/entire.log — Event stream
Session lifecycle events (start/end/turn), checkpoints, attribution stats, phase transitions. Requires Entire to be installed. If not present, script exits gracefully with code 0.
2. ~/.claude/projects/{KEY}/*.jsonl — Transcripts
Full conversation transcripts from Claude Code. The project key is auto-derived from the repo path (slashes replaced with dashes). Each .jsonl file contains every user message, assistant response, tool invocation, and tool result.
Noise Filtering
The script filters out internal noise from raw transcripts:
<task-notification>blocks — internal task system messagestoolUseResultentries — tool→assistant feedback (not real user prompts)<system-reminder>— system injections- Messages < 5 chars — trivial acknowledgements
- Markdown headers inside callouts — converted to bold
- XML/HTML tags inside callouts — stripped
Obsidian Rendering
All content uses Obsidian callout syntax:
- User messages:
> [!quote] **User** (HH:MM) - Assistant reasoning:
> [!info] **Assistant** - Tool calls:
> [!example] Tool Callswith nested>> [!note] **ToolName** — descriptionper tool - Tool details are expanded by default (remove
-for collapsed)
CLAUDE.md Integration
Add to the "Context Acquisition" section:
### Conversation History as Context
Prior sessions are indexed in `docs/conversations/`. Use them to:
- Recall prior decisions before re-solving a problem
- Understand why code looks the way it does
- Resume interrupted work on a branch
- Avoid repeating mistakes from prior sessions
Search: `grep -rl "keyword" docs/conversations/`
Add to "On Session Start" protocol:
7. Scan `docs/conversations/Conversations.md` for prior sessions on current branch
AGENTS.md Integration
Add to working rules:
7. **Check conversation history for prior context** — before starting work on a branch,
scan `docs/conversations/` for prior sessions. Use `grep -rl "keyword" docs/conversations/`
or read `docs/conversations/Conversations.md` for a chronological index.
Graceful Degradation
| Scenario | Behavior |
|---|---|
No .entire/ installed | Script exits with code 0, skip message |
| No transcripts directory | Script exits with code 0, skip message |
| Different developer machine | Transcripts dir auto-derived from repo path |
| CI (no local sessions) | Smoke warns but doesn't block |
| Pre-push without Entire | Clean skip, exit 0, ` |
Lago Context Engine Integration
The knowledge graph memory now has a server-side persistence backend via Lago (core/life/lago/):
lago-knowledgecrate provides server-side frontmatter parsing, wikilink extraction, scored search, and BFS graph traversallago-authcrate provides JWT auth middleware with shared-secret validation (AUTH_SECRET)- Per-user vaults: Each authenticated user gets a Lago session (
vault:{user_id}) for persistent.mdstorage - CLI:
lago memory {status,ls,search,read,store,ingest,delete}— ingest local vault files into Lago for remote access - broomva.tech dual-vault: Chat agent tools search both server vault (
VAULT_PATH) and user vault (LAGO_URL) with merged, ranked results
Setup
# Start lagod with auth enabled
LAGO_JWT_SECRET=$AUTH_SECRET cargo run -p lagod -- --http-port 8080
# Ingest vault files
lago memory ingest ~/broomva-vault/ --token $JWT
# Search from CLI
lago memory search "consciousness" --token $JWT
Environment Variables
| Variable | Where | Purpose |
|---|---|---|
LAGO_JWT_SECRET | lagod | Shared secret for JWT validation |
LAGO_URL | broomva.tech | Lago daemon URL (e.g. http://localhost:8080) |
AUTH_SECRET | broomva.tech | Signs JWTs for Lago auth (existing) |
BROOMVA_API_TOKEN | CLI | JWT token for lago memory commands |
Stack Integration
This skill is the persistence backbone for higher layers:
- Strategy (L7):
braindumpfiles notes into the vault through this bridge - Strategy (L7):
decision-logwrites structured decisions tovault/decisions/ - Strategy (L7):
weekly-reviewscans vault changes generated by this bridge - Strategy (L7):
morning-briefingreads action items from vault notes - Foundation (L1):
control-metalayer-loopgovernance policies inform what gets persisted - Persistence (L0): Lago context engine provides server-side search, graph traversal, and per-user vault storage