agent-native-audit

A review workflow that scores an application against principles for making an AI agent a first-class user of it.

In plain words
What is it for?
Use it to audit action parity, tools, context, shared data, complete entity operations, interface updates, capability discovery, and prompt-defined features.
Why use it?
It reveals gaps such as missing agent actions, incomplete create/read/update/delete operations, or poor sharing between the agent and the user interface.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/marcusrbrown/systematic/agent-native-audit
Any agent
npx skills add marcusrbrown/systematic --skill agent-native-audit
Clone the repo
git clone --depth 1 https://github.com/marcusrbrown/systematic

Made for: Claude Code, Codex.

Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,004 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00014 $0.02004
Opus 5 $0.00007 $0.01002
Sonnet 5 $0.00003 $0.00401
Haiku 4.5 $0.00001 $0.00200

Measured 3d ago against content hash 16d4128d6c2c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agent-native-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

95% identical to agent-native-audit — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/agent-native-audit/SKILL.md · 280 lines

How it starts

The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent-Native Architecture Audit

Conduct a comprehensive review of the codebase against agent-native architecture principles, launching parallel sub-agents for each principle and producing a scored report.

Core Principles to Audit

  1. Action Parity - "Whatever the user can do, the agent can do"
  2. Tools as Primitives - "Tools provide capability, not behavior"
  3. Context Injection - "System prompt includes dynamic context about app state"
  4. Shared Workspace - "Agent and user work in the same data space"
  5. CRUD Completeness - "Every entity has full CRUD (Create, Read, Update, Delete)"
  6. UI Integration - "Agent actions immediately reflected in UI"
  7. Capability Discovery - "Users can discover what the agent can do"
  8. Prompt-Native Features - "Features are prompts defining outcomes, not code"

Workflow

Step 1: Load the Agent-Native Skill

First, invoke the agent-native-architecture skill to understand all principles:

/systematic:agent-native-architecture

Select option 7 (action parity) to load the full reference material.

Step 2: Launch Parallel Sub-Agents

Launch 8 parallel sub-agents using the bundled systematic:research:repo-research-analyst agent, one for each principle. Each agent should:

  1. Enumerate ALL instances in the codebase (user actions, tools, contexts, data stores, etc.)
  2. Check compliance against the principle
  3. Provide a SPECIFIC SCORE like "X out of Y (percentage%)"
  4. List specific gaps and recommendations

Agent 1: Action Parity

Audit for ACTION PARITY - "Whatever the user can do, the agent can do."

Tasks:
1. Enumerate ALL user actions in frontend (API calls, button clicks, form submissions)
   - Search for API service files, fetch calls, form handlers
   - Check routes and components for user interactions
2. Check which have corresponding agent tools
   - Search for agent tool definitions
   - Map user actions to agent capabilities
3. Score: "Agent can do X out of Y user actions"

Format:
## Action Parity Audit
### User Actions Found
| Action | Location | Agent Tool | Status |
### Score: X/Y (percentage%)
### Missing Agent Tools
### Recommendations

Read the full file on GitHub · 280 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 280 lines · 14 tokens per session scan A 16d4128d6c2c

Subscribe to this mod's changes

agent-native-audit is a skill published in the GitHub repository marcusrbrown/systematic (24 stars, last pushed 3d ago), licensed MIT. It adds 14 tokens to every session and 2,004 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to agent-native-audit, differing in 7 lines, and is treated as a copy.