Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Agent Wal
Write-Ahead Log protocol for agent state persistence. Prevents losing corrections, decisions, and context during conversation compaction. Use when: (1) recei...
skill openclawclawhub Free
Write-Ahead Log protocol for agent state persistence. Prevents losing corrections, decisions, and context during conversation compaction. Use when: (1) recei...
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Install for OpenClaw
Known item issue.
This item's current download entry is known to bounce back to a listing or homepage instead of returning a package file.
Quick setup
- Open the source page and confirm the package flow manually.
- Review SKILL.md if you can obtain the files.
- Treat this source as manual setup until the download is verified.
Package facts
- Download mode
- Manual review
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/wal.py, skill.toml
Validation
- Open the source listing and confirm there is a real package or setup artifact available.
- Review SKILL.md before asking your agent to continue.
- Treat this source as manual setup until the upstream download flow is fixed.
Install with your agent
Agent handoff
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New install
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- bowen31337
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Agent WAL (Write-Ahead Log)
Write important state to disk before responding. Prevents the #1 agent failure mode: losing corrections and context during compaction.
Core Rule
Write before you respond. If something is worth remembering, WAL it first.
When to WAL
TriggerAction TypeExampleUser corrects youcorrection"No, use Podman not Docker"You make a key decisiondecision"Using CogVideoX-2B for text-to-video"Important analysis/conclusionanalysis"WAL/VFM patterns should be core infra not skills"State changestate_change"GPU server SSH key auth configured"User says "remember this"correctionWhatever they said
Commands
All commands via scripts/wal.py (relative to this skill directory): # Write before responding python3 scripts/wal.py append agent1 correction "Use Podman not Docker for all EvoClaw tooling" python3 scripts/wal.py append agent1 decision "CogVideoX-5B with multi-GPU via accelerate" python3 scripts/wal.py append agent1 analysis "Signed constraints prevent genome tampering" # Working buffer (batch writes during conversation, flush before compaction) python3 scripts/wal.py buffer-add agent1 decision "Some decision" python3 scripts/wal.py flush-buffer agent1 # Session start: replay lost context python3 scripts/wal.py replay agent1 # After applying a replayed entry python3 scripts/wal.py mark-applied agent1 <entry_id> # Maintenance python3 scripts/wal.py status agent1 python3 scripts/wal.py prune agent1 --keep 50
On Session Start
Run replay to get unapplied entries Read the summary into your context Mark entries as applied after incorporating them
On User Correction
Run append with action_type correction BEFORE responding Then respond with the corrected behavior
On Pre-Compaction Flush
Run flush-buffer to persist any buffered entries Then write to daily memory files as usual
During Conversation
For less critical items, use buffer-add to batch writes. Buffer is flushed to WAL on flush-buffer (called during pre-compaction) or manually.
Storage
WAL files: ~/clawd/memory/wal/<agent_id>.wal.jsonl Buffer files: ~/clawd/memory/wal/<agent_id>.buffer.jsonl Entries are append-only JSONL. Each entry: {"id": "abc123", "timestamp": "ISO8601", "agent_id": "agent1", "action_type": "correction", "payload": "Use Podman not Docker", "applied": false}
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts1 Files
- SKILL.md
- scripts/wal.py
- skill.toml