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 agentmods add skills/gvkhosla/compound-engineering-pi/agent-native-auditnpx skills add gvkhosla/compound-engineering-pi --skill agent-native-auditgit clone --depth 1 https://github.com/gvkhosla/compound-engineering-piWrote 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/gvkhosla/compound-engineering-pi/agent-native-audit)<a href="https://agentmods.dev/skills/gvkhosla/compound-engineering-pi/agent-native-audit"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/agent-native-audit.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 | $0.00014 | $0.01999 |
| Opus 5 | $0.00007 | $0.01000 |
| Sonnet 5 | $0.00003 | $0.00400 |
| Haiku 4.5 | $0.00001 | $0.00200 |
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 6d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- agent-native-audit — 100% identical, 0 lines differ
- agent-native-audit — 95% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 279 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
- Action Parity - "Whatever the user can do, the agent can do"
- Tools as Primitives - "Tools provide capability, not behavior"
- Context Injection - "System prompt includes dynamic context about app state"
- Shared Workspace - "Agent and user work in the same data space"
- CRUD Completeness - "Every entity has full CRUD (Create, Read, Update, Delete)"
- UI Integration - "Agent actions immediately reflected in UI"
- Capability Discovery - "Users can discover what the agent can do"
- 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:
/compound-engineering: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 Task tool with subagent_type: Explore, one for each principle. Each agent should:
- Enumerate ALL instances in the codebase (user actions, tools, contexts, data stores, etc.)
- Check compliance against the principle
- Provide a SPECIFIC SCORE like "X out of Y (percentage%)"
- 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
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.
- 6d ago First seen · 279 lines · 14 tokens per session scan A fbda06f95197
agent-native-audit is a skill published in the GitHub repository gvkhosla/compound-engineering-pi (51 stars, last pushed 4mo ago), licensed MIT. It adds 14 tokens to every session and 1,999 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 2026-08-30.
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