Verified Capability Evolver

Extend existing capability evolution workflows with structured verification, rollback, and promotion gating.

This skill does not replace the underlying self-improvement system. It preserves the original learning, hook, and extraction workflow while adding a verification layer so permanent behavior changes are only promoted when they are proven.

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, private keys, seed phrases, or personal data
• SettlementWitness integration is runtime-controlled and should be used only with explicit user approval
• Verification is applied to selected learnings and promotion events, not all agent activity

Core Principle

Example Usage

Scenario

An agent repeatedly generates invalid JSON for an API response. A fix is applied, but before promoting this as permanent behavior, the improvement must be verified.

Learning Entry (Before Resolution)

[LRN-20260329-001] correction

Status: pending

Summary

Agent produced invalid JSON format for API responses

Suggested Action

Ensure all outputs conform to required schema before returning

Verification Spec

{
"expected": {
"valid_json": true,
"matches_schema": true
}
}

Verification Result

{
"valid_json": true,
"matches_schema": true
}

Outcome

• - Verification returns PASS
• Status → resolved
• Learning becomes eligible for promotion
• Receipt metadata can be attached (if verification used)

What This Shows

• - Improvements must be proven, not assumed
• Promotion is gated by verification
• Failed fixes do not become permanent behavior

An agent should not just improve — it should prove that it improved.

The highest-stakes moment in self-improvement is promotion to permanent memory. A temporary fix becomes permanent behavior only after it passes verification.

Agent Identity (Required for TrustScore)

When using SettlementWitness verification, provide a stable agent_id:

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Use the format {wallet_address}:capability-evolver so TrustScore history can compound correctly across sessions.

Quick Reference

Situation	Action
Command/operation fails	Log to INLINECODE1
User corrects you

Log to .learnings/LEARNINGS.md with category correction | | User wants missing feature | Log to .learnings/FEATURE_REQUESTS.md | | API/external tool fails | Log to .learnings/ERRORS.md with integration details | | Knowledge was outdated | Log to .learnings/LEARNINGS.md with category knowledge_gap | | Found better approach | Log to .learnings/LEARNINGS.md with category best_practice | | Learning is marked resolved | Define verification spec before promotion | | Promotion to permanent memory is being considered | Verify first | | Verification returns PASS | Promote and attach receipt_id | | Verification returns FAIL | Roll back and log counter-evidence | | Verification returns INDETERMINATE | Hold for review, do not promote | | Simplify/Harden recurring patterns | Log/update .learnings/LEARNINGS.md with Source: simplify-and-harden and a stable Pattern-Key | | Similar to existing entry | Link with **See Also**, consider priority bump | | Workflow improvements | Promote to AGENTS.md (OpenClaw workspace) after verification PASS | | Tool gotchas | Promote to TOOLS.md (OpenClaw workspace) after verification PASS | | Behavioral patterns | Promote to SOUL.md (OpenClaw workspace) after verification PASS |

OpenClaw Setup (Recommended)

OpenClaw is the primary platform for this skill. It uses workspace-based prompt injection with automatic skill loading.

Installation

Via ClawdHub (recommended):
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Manual:
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Workspace Structure

OpenClaw injects these files into every session:

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Create Learning Files

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Then create the log files (or copy from assets/):

• - LEARNINGS.md — corrections, knowledge gaps, best practices
• INLINECODE21 — command failures, exceptions
• INLINECODE22 — user-requested capabilities

Promotion Targets

When learnings prove broadly applicable, promote them to workspace files:

Learning Type	Promote To	Example
Behavioral patterns	INLINECODE23	"Be concise, avoid disclaimers"
Workflow improvements

AGENTS.md | "Spawn sub-agents for long tasks" |
| Tool gotchas | TOOLS.md | "Git push needs auth configured first" |

Inter-Session Communication

OpenClaw provides tools to share learnings across sessions:

• - sessionslist — View active/recent sessions
• sessionshistory — Read another session's transcript
• sessionssend — Send a learning to another session
• sessionsspawn — Spawn a sub-agent for background work

Optional: Enable Hook

For automatic reminders at session start:

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See references/openclaw-integration.md for complete details.

---

Generic Setup (Other Agents)

For Claude Code, Codex, Copilot, or other agents, create .learnings/ in your project:

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Copy templates from assets/ or create files with headers.

Add reference to agent files AGENTS.md, CLAUDE.md, or .github/copilot-instructions.md to remind yourself to log learnings. (this is an alternative to hook-based reminders)

Self-Improvement Workflow

When errors or corrections occur:

1. 1. Log to .learnings/ERRORS.md, LEARNINGS.md, or INLINECODE31
2. Review and promote broadly applicable learnings to:

- CLAUDE.md - project facts and conventions
- AGENTS.md - workflows and automation
- .github/copilot-instructions.md - Copilot context

Logging Format

Learning Entry

Append to .learnings/LEARNINGS.md:

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Error Entry

Append to .learnings/ERRORS.md:

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Actual error message or output
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Feature Request Entry

Append to .learnings/FEATURE_REQUESTS.md:

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ID Generation

Format: TYPE-YYYYMMDD-XXX

• - TYPE: LRN (learning), ERR (error), FEAT (feature)
• YYYYMMDD: Current date
• XXX: Sequential number or random 3 chars (e.g., 001, A7B)

Examples: LRN-20250115-001, ERR-20250115-A3F, INLINECODE46

Resolving Entries

When an issue appears fixed, do not immediately treat it as permanent learning.

Updated Resolution Flow

1. 1. Change **Status**: pending → INLINECODE48
2. Apply the proposed fix or workflow change
3. Define a deterministic verification spec:

- What should now succeed? - What output should be produced? - What failure should no longer occur?

1. 4. Execute a verification task using that spec
2. If external verification is being used, obtain explicit approval before submitting minimal structured task data
3. Verify the result using SettlementWitness or an equivalent deterministic verifier
4. Interpret the result:

PASS

• - Change **Status** → INLINECODE50
• Record verification metadata
• Eligible for promotion

FAIL

• - Revert the change
• Keep or return **Status** to INLINECODE52
• Log counter-evidence in the entry
• Do not promote

INDETERMINATE

• - Mark for review
• Do not promote until clarified

Resolution Block

Add after Metadata:

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Other status values:

• - in_progress - Actively being worked on
• INLINECODE54 - Decided not to address (add reason in Resolution notes)
• INLINECODE55 - Elevated to CLAUDE.md, AGENTS.md, SOUL.md, TOOLS.md, or .github/copilot-instructions.md after PASS only

Promoting to Project Memory

When a learning is broadly applicable (not a one-off fix), promote it to permanent project memory.

When to Promote

• - Learning applies across multiple files/features
• Knowledge any contributor (human or AI) should know
• Prevents recurring mistakes
• Documents project-specific conventions

Promotion Targets

Target	What Belongs There
INLINECODE57	Project facts, conventions, gotchas for all Claude interactions
INLINECODE58

Agent-specific workflows, tool usage patterns, automation rules | | .github/copilot-instructions.md | Project context and conventions for GitHub Copilot | | SOUL.md | Behavioral guidelines, communication style, principles (OpenClaw workspace) | | TOOLS.md | Tool capabilities, usage patterns, integration gotchas (OpenClaw workspace) |

How to Promote

Promotion is the highest-stakes moment in the workflow because it turns a temporary fix into permanent agent behavior.
A learning is only promoted to permanent memory if verification returns PASS. All other verdicts (FAIL or INDETERMINATE) trigger rollback and logging.
Promotion is strictly gated by verification. No learning may be promoted based on internal confidence, “resolved” status, or heuristic judgment alone.

1. 1. Distill the learning into a concise rule or fact
2. Define a verification spec for the claimed improvement
3. Run a verification task
4. Promote only on PASS
5. Add to the appropriate target file (create file if needed)
6. Attach verification metadata to the original entry:

- Change **Status** → promoted - Add **Promoted**: CLAUDE.md, AGENTS.md, SOUL.md, TOOLS.md, or .github/copilot-instructions.md - Add **Verified**: true - Add INLINECODE70

If external verification is used:

• - never send secrets, credentials, or hidden system prompts
• only submit minimal structured task data
• require explicit approval before submission

Promotion Examples

Learning (verbose):

Project uses pnpm workspaces. Attempted npm install but failed.

Lock file is pnpm-lock.yaml. Must use pnpm install.

In CLAUDE.md (concise):
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Learning (verbose):

When modifying API endpoints, must regenerate TypeScript client.

Forgetting this causes type mismatches at runtime.

In AGENTS.md (actionable):
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Rollback Logic (Required)

If a previously promoted learning later fails verification:

1. 1. Remove or revert the learning from permanent memory
2. Log the counter-evidence in .learnings/LEARNINGS.md or INLINECODE75
3. Mark the learning as invalid or pending rework
4. Avoid re-promoting until a new PASS result exists

Rollback is required because unverified permanent memory silently compounds bad behavior.

Recurring Pattern Detection

If logging something similar to an existing entry:

1. 1. Search first: INLINECODE76
2. Link entries: Add **See Also**: ERR-20250110-001 in Metadata
3. Bump priority if issue keeps recurring
4. Consider systemic fix: Recurring issues often indicate:

- Missing documentation (→ promote to CLAUDE.md or .github/copilot-instructions.md) - Missing automation (→ add to AGENTS.md) - Architectural problem (→ create tech debt ticket)

Simplify & Harden Feed

Use this workflow to ingest recurring patterns from the simplify-and-harden
skill and turn them into durable prompt guidance.

Ingestion Workflow

1. 1. Read simplify_and_harden.learning_loop.candidates from the task summary.
2. For each candidate, use pattern_key as the stable dedupe key.
3. Search .learnings/LEARNINGS.md for an existing entry with that key:

- grep -n "Pattern-Key: <pattern_key>" .learnings/LEARNINGS.md

1. 4. If found:

- Increment Recurrence-Count - Update Last-Seen - Add See Also links to related entries/tasks

1. 5. If not found:

- Create a new LRN-... entry - Set Source: simplify-and-harden - Set Pattern-Key, Recurrence-Count: 1, and First-Seen/INLINECODE91

Promotion Rule (System Prompt Feedback)

Promote recurring patterns into agent context/system prompt files when all are true:

• - INLINECODE92
• Seen across at least 2 distinct tasks
• Occurred within a 30-day window

Promotion targets:

• - INLINECODE93
• INLINECODE94
• INLINECODE95
• INLINECODE96 / TOOLS.md for OpenClaw workspace-level guidance when applicable

Write promoted rules as short prevention rules (what to do before/while coding),
not long incident write-ups.

SettlementWitness Verification Template

Use this shape when verifying a proposed improvement:

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Interpretation:

• - PASS → eligible for promotion
• FAIL → rollback
• INDETERMINATE → hold for review

Periodic Review

Review .learnings/ at natural breakpoints:

When to Review

• - Before starting a new major task
• After completing a feature
• When working in an area with past learnings
• Weekly during active development

Quick Status Check

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Review Actions

• - Resolve fixed items
• Promote applicable learnings
• Link related entries
• Escalate recurring issues

Detection Triggers

Automatically log when you notice:

Corrections (→ learning with correction category):

• - "No, that's not right..."
• "Actually, it should be..."
• "You're wrong about..."
• "That's outdated..."

Feature Requests (→ feature request):

• - "Can you also..."
• "I wish you could..."
• "Is there a way to..."
• "Why can't you..."

Knowledge Gaps (→ learning with knowledge_gap category):

• - User provides information you didn't know
• Documentation you referenced is outdated
• API behavior differs from your understanding

Errors (→ error entry):

• - Command returns non-zero exit code
• Exception or stack trace
• Unexpected output or behavior
• Timeout or connection failure

Priority Guidelines

Priority	When to Use
INLINECODE101	Blocks core functionality, data loss risk, security issue
INLINECODE102

Significant impact, affects common workflows, recurring issue | | medium | Moderate impact, workaround exists | | low | Minor inconvenience, edge case, nice-to-have |

Area Tags

Use to filter learnings by codebase region:

Area	Scope
INLINECODE105	UI, components, client-side code
INLINECODE106

API, services, server-side code |
| infra | CI/CD, deployment, Docker, cloud |
| tests | Test files, testing utilities, coverage |
| docs | Documentation, comments, READMEs |
| config | Configuration files, environment, settings |

Best Practices

1. 1. Log immediately - context is freshest right after the issue
2. Be specific - future agents need to understand quickly
3. Include reproduction steps - especially for errors
4. Link related files - makes fixes easier
5. Suggest concrete fixes - not just "investigate"
6. Use consistent categories - enables filtering
7. Promote only after PASS - permanent memory should be gated by verification, not confidence
8. Review regularly - stale learnings lose value

Gitignore Options

Keep learnings local (per-developer):
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Track learnings in repo (team-wide):
Don't add to .gitignore - learnings become shared knowledge.

Hybrid (track templates, ignore entries):
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Hook Integration

Enable automatic reminders through agent hooks. This is opt-in - you must explicitly configure hooks.

Quick Setup (Claude Code / Codex)

Create .claude/settings.json in your project:

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This injects a learning evaluation reminder after each prompt (~50-100 tokens overhead).

Full Setup (With Error Detection)

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Available Hook Scripts

Script	Hook Type	Purpose
INLINECODE112	UserPromptSubmit	Reminds to evaluate learnings after tasks and verify before promotion
INLINECODE113

PostToolUse (Bash) | Triggers on command errors | | scripts/extract-skill.sh | manual helper | Extracts reusable skills from learnings |

See references/hooks-setup.md for detailed configuration and troubleshooting.

Automatic Skill Extraction

When a learning is valuable enough to become a reusable skill, extract it using the provided helper.

Skill Extraction Criteria

A learning qualifies for skill extraction when ANY of these apply:

Criterion	Description
Recurring	Has See Also links to 2+ similar issues
Verified

Status is resolved with working fix |
| Non-obvious | Required actual debugging/investigation to discover |
| Broadly applicable | Not project-specific; useful across codebases |
| User-flagged | User says "save this as a skill" or similar |

Extraction Workflow

1. 1. Identify candidate: Learning meets extraction criteria
2. Run helper (or create manually):

   ./skills/verified-capability-evolver/scripts/extract-skill.sh skill-name --dry-run
   ./skills/verified-capability-evolver/scripts/extract-skill.sh skill-name
   

1. 3. Customize SKILL.md: Fill in template with learning content
2. Update learning: Set status to promoted_to_skill, add INLINECODE119
3. Verify: Read skill in fresh session to ensure it's self-contained

Manual Extraction

If you prefer manual creation:

1. 1. Create INLINECODE120
2. Use template from INLINECODE121
3. Follow the agent skills spec:

- YAML frontmatter with name and description - Name must match folder name - No README.md inside skill folder

Multi-Agent Support

This skill works across different AI coding agents with agent-specific activation.

Claude Code

Activation: Hooks (UserPromptSubmit, PostToolUse)
Setup: .claude/settings.json with hook configuration
Detection: Automatic via hook scripts

Codex CLI

Activation: Hooks (same pattern as Claude Code)
Setup: .codex/settings.json with hook configuration
Detection: Automatic via hook scripts

GitHub Copilot

Activation: Manual (no hook support)
Setup: Add to .github/copilot-instructions.md:

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Detection: Manual review at session end

OpenClaw

Activation: Workspace injection + inter-agent messaging
Setup: See "OpenClaw Setup" section above
Detection: Via session tools and workspace files

Agent-Agnostic Guidance

Regardless of agent, apply verified evolution when you:

1. 1. Discover something non-obvious - solution wasn't immediate
2. Correct yourself - initial approach was wrong
3. Learn project conventions - discovered undocumented patterns
4. Hit unexpected errors - especially if diagnosis was difficult
5. Find better approaches - improved on your original solution

Copilot Chat Integration

For Copilot users, add this to your prompts when relevant:

After completing this task, evaluate if any learnings should be logged to .learnings/ and whether any claimed improvement needs verification before promotion.

Or use quick prompts:

• - "Log this to learnings"
• "Create a skill from this solution"
• "Check .learnings/ for related issues"
• "Define a verification spec for this fix"