mit einem Klick
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.
// Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries.
| name | knowledge-graph |
| description | Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries. |
| metadata | {"version":"1.1.0","openclaw":{"emoji":"🧠"}} |
| permissions | [{"exec":"Uses local filesystem commands when creating entity folders or scheduled knowledge-graph jobs."},{"file_write":"Appends facts, summaries, and daily-note synthesis inside the workspace knowledge graph."}] |
Maintain a lightweight, append-only entity graph that compounds durable facts across sessions.
Store the graph under:
<workspace>/life/areas/
people/<slug>/
companies/<slug>/
projects/<slug>/
Each entity folder should contain:
summary.md for the short, current snapshotfacts.jsonl for atomic, append-only factsUse one JSON object per line:
{
"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"status": "active|superseded",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>"
}
Durable facts usually include:
facts.jsonl.summary.md in 3 to 8 concise lines.summary.md first.facts.jsonl only if the summary is stale or the user asked for detail.Recall should be triggered, not automatic.
Create the core directories once:
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.
Generate beautifully designed PDF reports with a Nordic/Scandinavian aesthetic. Use when creating polished executive briefings, analysis reports, or presentation-style PDF outputs from markdown and HTML via Nutrient DWS.
Daily wisdom review applying Charlie Munger's mental models to your work and thinking. Use when asked to review decisions, analyze thinking patterns, detect biases, apply mental models, do a "Munger review", or run the Munger Observer. Triggers on scheduled daily reviews or manual requests like "run munger observer", "review my thinking", "check for blind spots", or "apply mental models".
Automatically recover working context after session compaction or when continuation is implied but context is missing. Works across Discord, Slack, Telegram, Signal, and other supported channels.
Query, audit, and optimize Google Ads campaigns. Supports two modes: (1) API mode for bulk operations with the google-ads Python SDK, (2) attached-browser mode for users without API access. Use when asked to check ad performance, pause campaigns or keywords, find wasted spend, audit conversion tracking, or optimize Google Ads accounts.
Bulk download images from login-protected gallery websites using an attached browser session. Use when asked to scrape, download, or save images from authenticated gallery pages, extract full-size images from thumbnails, or batch download from multi-page galleries.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.