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 roberto-mello/lavra --skill agent-native-architecturegit clone --depth 1 https://github.com/roberto-mello/lavraWrote 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/roberto-mello/lavra/agent-native-architecture)<a href="https://agentmods.dev/skills/roberto-mello/lavra/agent-native-architecture"><img src="https://agentmods.dev/badge/skills/roberto-mello/lavra/agent-native-architecture.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.05063 |
| Opus 5 | $0.00015 | $0.02531 |
| Sonnet 5 | $0.00006 | $0.01013 |
| Haiku 4.5 | $0.00003 | $0.00506 |
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 8d 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.
This is a copy
94% identical to agent-native-architecture — 16 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.
How it starts
The opening of the file, as written. The whole thing — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<why_now>
Why Now
Software agents work reliably now. Claude Code demonstrated that an LLM with access to bash and file tools, operating in a loop until an objective is achieved, can accomplish complex multi-step tasks autonomously.
The surprising discovery: a good coding agent is a good general-purpose agent. The same architecture that lets Claude Code refactor a codebase can let an agent organize your files, manage your reading list, or automate your workflows.
The Claude Code SDK makes this accessible. You can build applications where features aren't code you write—they're outcomes you describe, achieved by an agent with tools, operating in a loop until the outcome is reached.
This opens up a new field: software that works the way Claude Code works, applied to categories far beyond coding. </why_now>
<core_principles>
Core Principles
1. Parity
Whatever the user can do through the UI, the agent should be able to achieve through tools.
This is the foundational principle. Without it, nothing else matters.
Imagine you build a notes app with a beautiful interface for creating, organizing, and tagging notes. A user asks the agent: "Create a note summarizing my meeting and tag it as urgent."
If you built UI for creating notes but no agent capability to do the same, the agent is stuck. It might apologize or ask clarifying questions, but it can't help—even though the action is trivial for a human using the interface.
The fix: Ensure the agent has tools (or combinations of tools) that can accomplish anything the UI can do.
This isn't about creating a 1:1 mapping of UI buttons to tools. It's about ensuring the agent can achieve the same outcomes. Sometimes that's a single tool (create_note). Sometimes it's composing primitives (write_file to a notes directory with proper formatting).
The discipline: When adding any UI capability, ask: can the agent achieve this outcome? If not, add the necessary tools or primitives.
A capability map helps:
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/action-parity-discipline.md 9.2 KB
- references/agent-execution-patterns.md 10 KB
- references/agent-native-testing.md 14 KB
- references/architecture-patterns.md 17 KB
- references/dynamic-context-injection.md 7.6 KB
- references/files-universal-interface.md 9.9 KB
- references/from-primitives-to-domain-tools.md 7.8 KB
- references/mcp-tool-design.md 13 KB
- references/mobile-patterns.md 12 KB
- references/product-implications.md 10.0 KB
- references/refactoring-to-prompt-native.md 8.4 KB
- references/self-modification.md 7.7 KB
- references/shared-workspace-architecture.md 15 KB
- references/system-prompt-design.md 6.4 KB
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.
- 8d ago First seen · 440 lines · 31 tokens per session scan A c7fdea531596
agent-native-architecture is a skill published in the GitHub repository roberto-mello/lavra (50 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 5,063 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to agent-native-architecture, differing in 16 lines, and is treated as a copy.
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