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Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill openclaw/skills --skill clawvault- _meta.json2.9 KB
- SKILL.md14.4 KB
Overview
This skill implements a local, structured memory system for AI agents with a memory graph, context profiles, checkpoint/recover tools, and Obsidian integration. It keeps all data on-disk (no cloud sync) and provides semantic search, observational compression, task tracking, canvas dashboards, and Tailscale-based cross-vault networking. Use it to prevent context loss, repair broken sessions, and surface graph-aware context for agent prompts.
How this skill works
clawvault stores memories as markdown files in a vault directory and maintains a ledger and graph index to build relationships from wiki-links, tags, and frontmatter. It instruments sessions with checkpointing, session observation (LLM compression), and repair tools that update transcripts safely with backups. The tool offers CLI commands for remembering items, semantic/keyword search, task management, canvas generation, and serving the vault over Tailscale for cross-vault queries.
When to use it
- When an agent needs durable, structured memory stored locally with graph relationships
- To avoid context death by checkpointing and recovering OpenClaw sessions
- When you want graph-aware context retrieval using profiles (planning, incident, handoff)
- To compress and summarize session transcripts or generate weekly reflections
- To manage tasks, generate canvas dashboards, or integrate with Obsidian views
- When you need cross-vault search and sharing over a Tailscale network
Best practices
- Initialize a dedicated vault path and set CLAWVAULT_PATH to avoid accidental writes
- Use checkpoint frequently during long sessions and enable the OpenClaw hook for auto-checkpointing
- Run graph refresh after bulk edits to keep context retrieval accurate
- Provide GEMINI_API_KEY if you want observe/reflect LLM compression to run
- Keep backups before running repair-session; use --dry-run first to inspect changes
Example use cases
- An agent captures decision notes and later retrieves related project context via graph-aware context profiles
- Recovering a broken OpenClaw session where tool_result blocks were orphaned or calls aborted
- Generating an Obsidian canvas dashboard for sprint planning filtered by owner and include-done flag
- Compressing long session transcripts into scored observations and weekly reflections
- Serving vault contents over Tailscale to allow secure cross-vault semantic search with peers
FAQ
No. All data is stored locally in your vault; there is no cloud sync.
What powers observe/reflect LLM calls?
Observe and reflect can use an LLM key (GEMINI_API_KEY) to compress transcripts and generate reflections; it’s optional and local outputs are written to the ledger.