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 commands/tajmahal226/compound-engineering-plugin/agent-native-auditgit clone --depth 1 https://github.com/tajmahal226/compound-engineering-pluginWrote 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/commands/tajmahal226/compound-engineering-plugin/agent-native-audit)<a href="https://agentmods.dev/commands/tajmahal226/compound-engineering-plugin/agent-native-audit"><img src="https://agentmods.dev/badge/commands/tajmahal226/compound-engineering-plugin/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.
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 command published in the GitHub repository tajmahal226/compound-engineering-plugin (4 stars, last pushed 6mo 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.