Openclaw AI Agent Reconciler

OpenClaw Agent Reconciliation Skill

Overview

This skill enables systematic reconciliation between the filesystem agent definitions (AGENTS.md files under agent-companies-core/agents/openclawforge-ai/agents/) and the OpenClaw SQLite database tables (openclaw_agents, openclaw_agent_skills, openclaw_agent_models, openclaw_agent_capabilities) that power the OpenClaw control plane.

Trigger Conditions

• User asks to "reconcile agents"
• User asks to "check agents against database"
• User asks to "verify agent registrations"
• User asks to "audit agent names"
• User asks to "validate naming conventions"
• User asks to "find missing agents" or "find extra agents"
• User asks to "generate agent fix SQL"
• User asks to "sync agents to database"

Execution Workflow

Step 0: Schema Validation (MANDATORY)

Before generating ANY SQL, validate the schema:

1. Query the openclaw_agents table schema:

   PRAGMA table_info(openclaw_agents);
   

2. Verify columns match expected from schema/create-openclaw-tables.sql:

   • id, name, slug, role, reports_to, description, skills, company, status, adapter_type, adapter_config, runtime_config, budget_monthly_cents, spent_monthly_cents, last_heartbeat_at, capabilities, metadata, created_at, updated_at

3. CRITICAL: The openclaw_agents table has:

   • name = human-readable agent name (e.g., "OpenClaw Agent Reconciler")
   • slug = agent identifier (e.g., "openclaw-ai-agent-reconciler")
   • company = company namespace (e.g., "openclawforge-ai")

Step 1: Filesystem Discovery

1. Scan agent-companies-core/agents/openclawforge-ai/agents/**/AGENTS.md
2. Parse YAML frontmatter for each agent
3. Extract: name, slug, reportsTo, role, description, skills
4. Build filesystem agent index

Step 2: Database Discovery

1. Query openclaw_agents table → all rows
2. Query openclaw_agent_skills table → skill assignments by agent_id
3. Query openclaw_agent_models table → model assignments by agent_id
4. Build database agent index

Step 3: Reconciliation Match

1. Match filesystem agents to database agents by slug ↔ slug
2. Identify missing agents: in filesystem, not in database
3. Identify extra agents: in database, not in filesystem
4. Identify field mismatches: name, role, reports_to, description differences
5. Identify missing skill assignments
6. Identify missing model assignments
7. Identify naming convention violations

Step 4: SQL Fix Generation

Generate SQL INSERT/UPDATE/DELETE statements:

Step 5: Collaboration Delegation

• Database operations → delegate to openclaw-ai-sqlite-manager
• Agent creation logic → delegate to openclaw-ai-agent-creator
• Model assignment logic → delegate to openclaw-ai-model-assigner

Step 6: Report Output

• Save SQL to reconciliation_fixes.sql
• Save JSON report to reconciliation_report.json
• Print summary with counts per table

Input Parameters

Error Handling

• Missing agents directory: Report error and exit
• Database connection failure: Report error with connection details
• Malformed AGENTS.md: Log warning, skip file, continue
• No database agents: Report warning but continue with filesystem scan

Skill Name: openclaw-ai-agent-reconciler Version: 1.0 Owner: openclaw-ai-agent-reconciler Created: 2026-05-01