Tencent SkillHub · AI

Agent Wal

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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High Signal

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

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, scripts/wal.py, skill.toml

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

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

ClawHub primary doc Primary doc: SKILL.md 9 sections Open source page

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 Primary doc
scripts/wal.py Scripts
skill.toml Files