agent-native-architecture

agent-native-architecture is a skill for Claude Code, Codex from aegntic/compound-engineering. It costs 50 tokens per session (1,018 once invoked), scanned A, original, MIT.

Architecture guidance for building software in which AI agents can use important features as first-class operators. It covers tools, prompts, shared data, interfaces, and explicit contracts.

In plain words
What is it for?
Use it when designing agent applications, MCP tools, system prompts, context injection, self-modifying systems, or checks for matching user and agent actions.
Why use it?
It helps identify what an agent must be able to do and how those actions should connect to the product. This reduces the risk of building agent features that cannot match the user experience.

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/aegntic/compound-engineering/agent-native-architecture
Any agent
npx skills add aegntic/compound-engineering --skill agent-native-architecture
Clone the repo
git clone --depth 1 https://github.com/aegntic/compound-engineering

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agent-native-architecture

README.md
[![agentmods](https://agentmods.dev/badge/skills/aegntic/compound-engineering/agent-native-architecture.svg)](https://agentmods.dev/skills/aegntic/compound-engineering/agent-native-architecture)
Your own site
<a href="https://agentmods.dev/skills/aegntic/compound-engineering/agent-native-architecture"><img src="https://agentmods.dev/badge/skills/aegntic/compound-engineering/agent-native-architecture.svg" alt="Measured on agentmods" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,018 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00050 $0.01018
Opus 5 $0.00025 $0.00509
Sonnet 5 $0.00010 $0.00204
Haiku 4.5 $0.00005 $0.00102

Measured 4d ago against content hash 3b48aea94bd0, 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-architecture 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 4d 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.

skills/agent-native-architecture/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

agent-native-architecture Skill

Design software where agents are first-class operators, not an afterthought. Start with parity, primitive tools, and explicit contracts so new features can be delivered as prompts, not hard-coded workflows.

When to use

  • Designing or refactoring an agent-native application.
  • Creating MCP tools, tool contracts, or system prompts.
  • Reviewing action parity between UI and agent capabilities.
  • Producing the architecture artifact consumed by the compound workflow chain.

Workflow

  1. Identify the user's goal: design, files/workspace, tool design, domain tools, execution patterns, system prompts, context injection, action parity, self-modification, product design, mobile patterns, testing, or refactoring.
  2. Load only the reference files needed for that topic, then map the advice onto the current product, data model, and deployment constraints.
  3. Run the architecture checklist below before recommending new abstractions.
  4. If this is part of the compound workflow chain, turn the guidance into a concrete architecture artifact under docs/architecture/.

Core principles

  • Parity: if the user can do it, the agent can achieve the same outcome.
  • Granularity: tools are primitives; features are prompt-defined outcomes.
  • Composability: new capabilities should come from new prompts before new code.
  • Emergent capability: open-ended requests should reveal gaps and product demand.
  • Improvement over time: context, prompt refinement, and safe self-modification should make the system better with use.

Reference router

  • Design from scratch -> references/architecture-patterns.md
  • Files and workspace design -> references/files-universal-interface.md, references/shared-workspace-architecture.md
  • MCP and primitive tool design -> references/mcp-tool-design.md
  • Domain-tool thresholds -> references/from-primitives-to-domain-tools.md
  • Execution and completion loops -> references/agent-execution-patterns.md
  • System prompt design -> references/system-prompt-design.md
  • Dynamic context injection -> references/dynamic-context-injection.md
  • Action parity reviews -> references/action-parity-discipline.md
  • Self-modification -> references/self-modification.md
  • Product implications -> references/product-implications.md
  • Mobile patterns -> references/mobile-patterns.md
  • Testing -> references/agent-native-testing.md
  • Refactoring legacy systems -> references/refactoring-to-prompt-native.md

Read the full file on GitHub · 93 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. 4d ago First seen · 93 lines · 50 tokens per session scan A 3b48aea94bd0

Subscribe to this mod's changes

agent-native-architecture is a skill published in the GitHub repository aegntic/compound-engineering (2 stars, last pushed 2mo ago), licensed MIT. It adds 50 tokens to every session and 1,018 once invoked, about $0.0003 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 2026-08-31.

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