ai-asset-pricing: Skill for Claude Code

Agent Creator & Auditor

Two modes: Create a new agent or Audit an existing one.

Examples

• /create-audit-agent my-data-expert -- create a new agent
• /create-audit-agent audit my-data-expert -- audit a specific agent
• /create-audit-agent audit all -- audit all agents with summary table

Mode Selection

Parse $ARGUMENTS:

• audit, audit <name>, or audit all → Audit mode
• Anything else (name, description, or empty) → Create mode

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CREATE MODE

Five phases: Gather → Reference → Generate → Register → Verify.

Phase 1: Gather Requirements

1a. Scan existing agents

List all .md files in .claude/agents/ to prevent name collisions and understand existing scope.

1b. Ask the user

If $ARGUMENTS contains a name or description, use it. Otherwise, use AskUserQuestion with all of the following:

1. Name: lowercase-with-hyphens identifier (e.g., my-data-expert)
2. Purpose: What domain does this agent cover? What databases, tables, or APIs does it work with?
3. Key routing terms: What keywords should trigger delegation? (database names, table names, acronyms, paper names)
4. Tools needed: What tools should the agent have? (read-only vs full access, specific tools)
5. Skills: Should any skills be injected at startup? (e.g., wrds-psql)
6. Model: inherit (default), sonnet, opus, or haiku?

1c. Skill vs Agent decision

Before proceeding, ask: "Is this a known, rigid workflow with fixed inputs?" If yes, recommend a skill (/create-skill) instead. Agents are for tasks requiring judgment, discovery, or error recovery. Skills run in the caller's context with zero spawn overhead (75-80% fewer tokens). See docs/AGENT_LESSONS_LEARNED.md for rationale.

1d. Check for overlap

Compare the user's answers against existing agents. If there's scope overlap (same tables, same databases), warn the user and ask whether to:

• Extend the existing agent instead
• Create a new focused agent with clear scope boundaries
• Proceed anyway