Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
agent-self-governance
Self-governance protocol for autonomous agents: WAL (Write-Ahead Log), VBR (Verify Before Reporting), ADL (Anti-Divergence Limit), and VFM (Value-For-Money)....
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Self-governance protocol for autonomous agents: WAL (Write-Ahead Log), VBR (Verify Before Reporting), ADL (Anti-Divergence Limit), and VFM (Value-For-Money)....
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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.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/adl.py, scripts/vbr.py, scripts/vfm.py, 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
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- Download the package from Yavira.
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- 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.
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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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.0
Provenance
- Publisher
- bowen31337
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Agent Self-Governance
Five protocols that prevent agent failure modes: losing context, false completion claims, persona drift, wasteful spending, and infrastructure amnesia.
1. WAL (Write-Ahead Log)
Rule: Write before you respond. If something is worth remembering, WAL it first. TriggerAction TypeExampleUser corrects youcorrection"No, use Podman not Docker"Key decisiondecision"Using CogVideoX-2B for text-to-video"Important analysisanalysis"WAL patterns should be core infra not skills"State changestate_change"GPU server SSH key auth configured" # Write before responding python3 scripts/wal.py append <agent_id> correction "Use Podman not Docker" # Working buffer (batch, flush before compaction) python3 scripts/wal.py buffer-add <agent_id> decision "Some decision" python3 scripts/wal.py flush-buffer <agent_id> # Session start: replay lost context python3 scripts/wal.py replay <agent_id> # After incorporating a replayed entry python3 scripts/wal.py mark-applied <agent_id> <entry_id> # Maintenance python3 scripts/wal.py status <agent_id> python3 scripts/wal.py prune <agent_id> --keep 50
Integration Points
Session start β replay to recover lost context User correction β append BEFORE responding Pre-compaction flush β flush-buffer then write daily memory During conversation β buffer-add for less critical items
2. VBR (Verify Before Reporting)
Rule: Don't say "done" until verified. Run a check before claiming completion. # Verify a file exists python3 scripts/vbr.py check task123 file_exists /path/to/output.py # Verify a file was recently modified python3 scripts/vbr.py check task123 file_changed /path/to/file.go # Verify a command succeeds python3 scripts/vbr.py check task123 command "cd /tmp/repo && go test ./..." # Verify git is pushed python3 scripts/vbr.py check task123 git_pushed /tmp/repo # Log verification result python3 scripts/vbr.py log <agent_id> task123 true "All tests pass" # View pass/fail stats python3 scripts/vbr.py stats <agent_id>
When to VBR
After code changes β check command "go test ./..." After file creation β check file_exists /path After git push β check git_pushed /repo After sub-agent task β verify the claimed output exists
3. ADL (Anti-Divergence Limit)
Rule: Stay true to your persona. Track behavioral drift from SOUL.md. # Analyze a response for anti-patterns python3 scripts/adl.py analyze "Great question! I'd be happy to help you with that!" # Log a behavioral observation python3 scripts/adl.py log <agent_id> anti_sycophancy "Used 'Great question!' in response" python3 scripts/adl.py log <agent_id> persona_direct "Shipped fix without asking permission" # Calculate divergence score (0=aligned, 1=fully drifted) python3 scripts/adl.py score <agent_id> # Check against threshold python3 scripts/adl.py check <agent_id> --threshold 0.7 # Reset after recalibration python3 scripts/adl.py reset <agent_id>
Anti-Patterns Tracked
Sycophancy β "Great question!", "I'd be happy to help!" Passivity β "Would you like me to", "Shall I", "Let me know if" Hedging β "I think maybe", "It might be possible" Verbosity β Response length exceeding expected bounds
Persona Signals (Positive)
Direct β "Done", "Fixed", "Ship", "Built" Opinionated β "I'd argue", "Better to", "The right call" Action-oriented β "Spawning", "On it", "Kicking off"
4. VFM (Value-For-Money)
Rule: Track cost vs value. Don't burn premium tokens on budget tasks. # Log a completed task with cost python3 scripts/vfm.py log <agent_id> monitoring glm-4.7 37000 0.03 0.8 # Calculate VFM scores python3 scripts/vfm.py score <agent_id> # Cost breakdown by model and task python3 scripts/vfm.py report <agent_id> # Get optimization suggestions python3 scripts/vfm.py suggest <agent_id>
Task β Tier Guidelines
Task TypeRecommended TierModelsMonitoring, formatting, summarizationBudgetGLM, DeepSeek, HaikuCode generation, debugging, creativeStandardSonnet, Gemini ProArchitecture, complex analysisPremiumOpus, Sonnet+thinking
When to Check VFM
After spawning sub-agents β log cost and outcome During heartbeat β run suggest for optimization tips Weekly review β run report for cost breakdown
5. IKL (Infrastructure Knowledge Logging)
Rule: Log infrastructure facts immediately. When you discover hardware specs, service configs, or network topology, write it down BEFORE continuing.
Triggers
Discovery TypeLog ToExampleHardware specsTOOLS.md"GPU server has 3 GPUs: RTX 3090 + 3080 + 2070 SUPER"Service configsTOOLS.md"ComfyUI runs on port 8188, uses /data/ai-stack"Network topologyTOOLS.md"Pi at 192.168.99.25, GPU server at 10.0.0.44"Credentials/authmemory/encrypted/"SSH key: ~/.ssh/id_ed25519_alexchen"API endpointsTOOLS.md or skill"Moltbook API: POST /api/v1/posts"
Commands to Run on Discovery
# Hardware discovery nvidia-smi --query-gpu=index,name,memory.total --format=csv lscpu | grep -E "Model name|CPU\(s\)|Thread" free -h df -h # Service discovery systemctl list-units --type=service --state=running docker ps # or podman ps ss -tlnp | grep LISTEN # Network discovery ip addr show cat /etc/hosts
The IKL Protocol
SSH to new server β Run hardware/service discovery commands Before responding β Update TOOLS.md with specs New service discovered β Log port, path, config location Credentials obtained β Encrypt and store in memory/encrypted/
Anti-Pattern: "I'll Remember"
β "The GPU server has 3 GPUs" (only in conversation) β "The GPU server has 3 GPUs" β Update TOOLS.md β then continue Memory is limited. Files are permanent. IKL before you forget.
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
4 Scripts1 Docs1 Files
- SKILL.md
- scripts/adl.py
- scripts/vbr.py
- scripts/vfm.py
- scripts/wal.py
- skill.toml