knowledge-graph
Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries.
Best use case
knowledge-graph is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries.
Teams using knowledge-graph should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/knowledge-graph/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How knowledge-graph Compares
| Feature / Agent | knowledge-graph | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Knowledge Graph Skill
Maintain a lightweight, append-only entity graph that compounds durable facts across sessions.
## When to Use
- Extract durable facts from recent work or conversation history
- Rewrite entity summaries from active facts
- Answer "what do we know about X?" without reopening large transcripts
- Keep shared context for people, companies, and projects inside the workspace
## Data Model
Store the graph under:
```text
<workspace>/life/areas/
people/<slug>/
companies/<slug>/
projects/<slug>/
```
Each entity folder should contain:
- `summary.md` for the short, current snapshot
- `facts.jsonl` for atomic, append-only facts
Use one JSON object per line:
```json
{
"id": "<slug>-NNN",
"fact": "Plain-English fact",
"category": "relationship|milestone|status|preference|context|decision",
"ts": "YYYY-MM-DD",
"source": "conversation|manual|inference",
"status": "active|superseded",
"supersedes": "<older-id>"
}
```
## Fact Rules
- Keep facts atomic. One durable fact per entry.
- Append new facts instead of rewriting history.
- When something changes, add a new fact and mark the old one as superseded.
- Skip ephemera, greetings, speculation, and low-value chatter.
- Check existing facts before adding duplicates.
Durable facts usually include:
- role or relationship changes
- key decisions
- long-lived preferences
- major project milestones
- stable operating context
## Workflows
### Fact Extraction
1. Read the recent daily note and the recent conversation window.
2. Identify durable facts worth preserving.
3. Resolve entity type and slug.
4. Create the entity folder if it does not exist.
5. Append new facts to `facts.jsonl`.
6. Note extraction activity in the daily note if the workspace uses one.
### Weekly Synthesis
1. List entities changed during the week.
2. Load active facts only.
3. Rewrite `summary.md` in 3 to 8 concise lines.
4. Ensure contradicted facts are marked superseded.
5. Record a short synthesis note in the daily log if applicable.
### Entity Lookup
1. Read `summary.md` first.
2. Open `facts.jsonl` only if the summary is stale or the user asked for detail.
3. Fall back to broader memory search only when the entity is missing from the graph.
## Low-Token Recall
Recall should be triggered, not automatic.
- Recall when the user names a tracked person, company, or project.
- Recall when the user explicitly asks to remember, recall, or summarize prior context.
- Inject only the short summary by default.
- Avoid loading raw facts unless the user asked for specifics or contradictions need resolution.
## Setup
Create the core directories once:
```bash
mkdir -p life/areas/people life/areas/companies life/areas/projects
```
If multiple agents share one workspace, point them at the same `life/` directory so they operate on the same entity store.
## Safety Boundaries
- Do not store sensitive secrets, credentials, or highly personal data unless the user explicitly asked for it.
- Do not create entities or facts for casual chat that has no durable value.
- Do not inject the graph into every conversation by default.
- Do not delete historical facts; supersede them with a newer fact instead.Related Skills
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