templetongroup

AI Structure Audit — Security skill for Claude Code

Security community

Evidence-driven audit of AI systems across prompt, context, harness, loop, and graph layers.

How to install AI Structure Audit

This entry records only its repository, not the path inside it, so there is no exact command to give. Open templetongroup/ai-structure-audit and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What AI Structure Audit does

Evidence-driven audit of AI systems across prompt, context, harness, loop, and graph layers.

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README

AI Structure Audit

AI Structure Audit is an evidence-driven skill for reviewing AI assistants, agents, RAG systems, tool-using workflows, and multi-agent architectures.

It audits five nested layers:

  1. Prompt
  2. Context
  3. Harness
  4. Loop
  5. Graph

The skill diagnoses from the bottom up, identifies the lowest failing layer, recommends the smallest effective fixes, and offers to implement those fixes only after approval.

What it checks

  • prompt clarity, constraints, examples, output contracts, and evaluations
  • context visibility, retrieval, memory, provenance, freshness, and token discipline
  • tool schemas, permissions, sandboxing, retries, idempotency, verification, and observability
  • verifier quality, stop conditions, no-progress detection, budgets, and convergence
  • node responsibilities, routing, state flow, parallelism, aggregation, and graph justification
  • common cross-layer misdiagnoses, such as adding agents to compensate for a weak verifier

Install

Clone the repository into your agent's skills directory:

git clone https://github.com/templetongroup/ai-structure-audit.git ~/.codex/skills/ai-structure-audit

For OpenClaw, install it through your normal reviewed skill-install process and verify visibility with `openclaw skills check`.

Use

Examples:

Use $ai-structure-audit to review this agent system.
Audit this RAG workflow and tell me which structural layer is actually failing.
Review this multi-agent architecture before we add another node.
Run an AI Structure Audit, suggest fixes, and offer to implement them.

Audit mode is read-only. Implementation requires explicit approval after the report identifies the proposed scope, affected systems, validation plan, risk, and rollback.

Repository structure

  • SKILL.md: audit and implementation workflow
  • templates/audit-report.md: reusable report skeleton
  • references/source-map.md: provenance and interpretation notes

Provenance

The five-layer model was a