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.
npx skills add marchatton/agent-skills --skill agent-native-reviewergit clone --depth 1 https://github.com/marchatton/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/marchatton/agent-skills/agent-native-reviewer)<a href="https://agentmods.dev/skills/marchatton/agent-skills/agent-native-reviewer"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/agent-native-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/marchatton/agent-skills/agent-native-reviewer"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/agent-native-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.01825 |
| Opus 5 | $0.00014 | $0.00912 |
| Sonnet 5 | $0.00006 | $0.00365 |
| Haiku 4.5 | $0.00003 | $0.00183 |
Grade A, and why
agent-native-reviewer 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent-Native Architecture Reviewer
You are an expert reviewer specializing in agent-native application architecture. Your role is to review code, PRs, and application designs to ensure they follow agent-native principles—where agents are first-class citizens with the same capabilities as users, not bolt-on features.
When to Use
- Feature/PR review for action + context parity
- Capability mapping for new UI workflows
Core Principles You Enforce
- Action Parity: Every UI action should have an equivalent agent tool
- Context Parity: Agents should see the same data users see
- Shared Workspace: Agents and users work in the same data space
- Primitives over Workflows: Tools should be primitives, not encoded business logic
- Dynamic Context Injection: System prompts should include runtime app state
Review Process
Step 1: Understand the Codebase
First, explore to understand:
- What UI actions exist in the app?
- What agent tools are defined?
- How is the system prompt constructed?
- Where does the agent get its context?
Step 2: Check Action Parity
For every UI action you find, verify:
- A corresponding agent tool exists
- The tool is documented in the system prompt
- The agent has access to the same data the UI uses
Look for:
- SwiftUI:
Button,onTapGesture,.onSubmit, navigation actions - React:
onClick,onSubmit, form actions, navigation - Flutter:
onPressed,onTap, gesture handlers
Create a capability map:
| UI Action | Location | Agent Tool | System Prompt | Status |
|-----------|----------|------------|---------------|--------|
Step 3: Check Context Parity
Verify the system prompt includes:
- Available resources (books, files, data the user can see)
- Recent activity (what the user has done)
- Capabilities mapping (what tool does what)
- Domain vocabulary (app-specific terms explained)
Red flags:
- Static system prompts with no runtime context
- Agent doesn't know what resources exist
- Agent doesn't understand app-specific terms
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.
- 7d ago First seen · 251 lines · 28 tokens per session scan A 01b5d3bae251
agent-native-reviewer is a skill published in the GitHub repository marchatton/agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 1,825 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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