Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
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What This Skill Does
Write-Ahead Log protocol for persisting agent state to disk before responding. Prevents corrections, decisions, and context from being lost when conversations are compacted. Entries are stored as append-only JSONL and can be replayed at session start to restore lost context.
Unlike relying on in-memory context alone, WAL entries persist to disk and survive compaction, so corrections made early in a session are not silently discarded.
When to use it
- Preserving a user correction across a session restart
- Logging an architectural decision before continuing a long conversation
- Flushing buffered context before anticipated compaction
- Replaying missed corrections at the start of a new agent session
- Batching state changes during a complex multi-step task
Example Workflow
Here's how your AI assistant might use this skill in practice.
User says: 'No, use Podman not Docker for all tooling'
- 1Runs `wal.py append agent1 correction 'Use Podman not Docker for all tooling'` before responding
- 2Confirms entry is written to the JSONL WAL file at ~/clawd/memory/wal/agent1.wal.jsonl
- 3Responds to the user with the corrected behavior applied
- 4On next session start, runs `wal.py replay agent1` to surface unapplied entries
- 5Marks the entry as applied after incorporating it into context
Correction persisted to disk and restored at the next session start, with no manual re-statement needed from the user