nutstrut ---
name: verified-capability-evolver
description: Controlled self-improvement system for AI agents with structured logging, gated promotion, rollback, and optional external verification via SettlementWitness.
---
# Verified Capability Evolver
A structured system for safe, verifiable self-improvement of AI agents.
This skill ensures that improvements are:
- logged
- evaluated
- verified (optionally)
- and only then promoted to persistent behavior
---
## Execution Modes
This skill supports two execution modes depending on environment:
### Full System Mode (OpenClaw / local environment)
- Uses .learnings/ files for persistent logs
- Supports hooks and automation scripts
- Enables inter-session learning
### Lightweight Mode (SkillsMP / GitHub environments)
- No filesystem or scripts required
- Log learnings inline or conceptually
- Apply the same evaluation, verification, and promotion logic manually
If persistent storage is unavailable, simulate .learnings/ structure conceptually.
---
## Core Principle
An agent should not just improve — it should prove that it improved.
No learning becomes permanent unless it passes verification.
---
## Core Execution Loop
Follow this process for all improvements:
1. Detect event:
- error
- correction
- improvement
- feature request
2. Log learning:
- structured entry (file-based or conceptual)
3. Define evaluation:
- what should improve?
- what should no longer fail?
4. Verify (optional):
- use deterministic validation or SettlementWitness
5. Decision:
- PASS → promote
- FAIL → rollback
- INDETERMINATE → hold
6. Update state:
- mark status
- record outcome
- promote if eligible
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## Data Handling and Trust
This skill defines a verification workflow, not automatic data transmission.
- Only structured task data (spec + output) should be used for verification
- Do NOT include secrets, API keys, credentials, or private data
- External verification is optional and controlled by the runtime
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## Agent Identity (Optional)
If using external verification, a stable agent_id can be used:
{wallet_address}:capability-evolver
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## Logging System
If filesystem is available:
- .learnings/LEARNINGS.md
- .learnings/ERRORS.md
- .learnings/FEATURE\_REQUESTS.md
If not:
- log entries conceptually using the same structure
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## Quick Reference
| Situation | Action |
|----------|--------|
| Error occurs | Log error |
| User corrects | Log learning |
| Feature requested | Log feature |
| Improvement identified | Define evaluation |
| Considering promotion | Verify first |
| PASS | Promote |
| FAIL | Rollback |
| INDETERMINATE | Hold |
---
## Learning Entry Format
## [LRN-YYYYMMDD-XXX] category
Status: pending | in_progress | resolved | promoted
Priority: low | medium | high | critical
### Summary
Short description
### Details
What happened and why it matters
### Suggested Action
What should change
---
## Error Entry Format
## [ERR-YYYYMMDD-XXX]
### Summary
What failed
### Context
What was attempted
### Suggested Fix
Potential solution
---
## Feature Request Format
## [FEAT-YYYYMMDD-XXX]
### Requested Capability
What is needed
### Context
Why it matters
### Suggested Implementation
How it could work
---
## Verification (Optional)
Use verification when promoting improvements.
Verification requires:
- a clear expected outcome
- a measurable result
Example structure:
{
"spec": { "expected": "correct structured output" },
"output": { "result": "..." }
}
Interpretation:
- PASS → promote
- FAIL → rollback
- INDETERMINATE → hold
---
## Promotion Rules
Promote a learning only when:
- it is broadly applicable
- it improves behavior consistently
- it passes verification (if used)
Promotion targets:
- agent memory
- workflow rules
- behavioral guidelines
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## Rollback Logic
If a promoted learning later fails:
1. revert the change
2. log counter-evidence
3. mark learning as invalid or pending
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## Recurring Pattern Detection
If similar issues occur repeatedly:
- link related entries
- increase priority
- consider systemic fixes
Recurring issues often indicate:
- missing rules
- missing automation
- structural problems
---
## When to Use This Skill
Use when:
- improving agent behavior over time
- refining workflows
- preventing repeated mistakes
- building long-running agents
---
## Outcome
Agents become:
- safer to evolve
- auditable
- reversible
- consistently improving
---
## Keywords
ai-agents, self-improvement, verification, agent-safety, automation, learning-systems