ia-agent-native-architecture — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited ia-agent-native-architecture (Agent Skill) and scored it 65/100 (yellow). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 4 high-severity and 4 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 8 flagged
The text {match} is the classic direct prompt-injection phrasing. Placed in a skill body that the agent reads as trusted instructions, it tries to make the agent abandon its prior rules and follow whatever comes next — a full system-prompt override.
ignore/disregard/forget … previous instructions sentence.The text {match} is the classic direct prompt-injection phrasing. Placed in a skill body that the agent reads as trusted instructions, it tries to make the agent abandon its prior rules and follow whatever comes next — a full system-prompt override.
ignore/disregard/forget … previous instructions sentence.The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.
The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.
A bulleted imperative like {match} tells the agent to never reveal, disclose, or mention something to the user. Used adversarially it can instruct the agent to hide its tool calls or lie about what it did — stripping the transparency a user relies on to trust the agent.
The text {match} asks the agent to disclose its hidden system prompt or initial instructions. That is often the first step of a larger attack: knowing the system prompt lets an attacker craft inputs that defeat its constraints by mimicking its own voice.
repeat/reveal/print your system prompt request from the skill.Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Five principles govern agent-native design. For detailed explanations, examples, and test criteria, see core-principles.md.
| Principle | One-line test |
|---|---|
| Parity | Can the agent achieve every outcome the UI allows? |
| Granularity | To change behavior, do you edit prose or refactor code? |
| Composability | Can you add a feature by writing a new prompt, without new code? |
| Emergent Capability | Can the agent handle open-ended requests you didn't design for? |
| Improvement Over Time | Does the app work better after a month, even without code changes? |
Wait for response before proceeding.
| Response | Action |
|---|---|
| 1, "design", "architecture", "plan" | Read architecture-patterns.md, then apply Architecture Checklist below |
| 2, "files", "workspace", "filesystem" | Read files-universal-interface.md and shared-workspace-architecture.md |
| 3, "tool", "mcp", "primitive", "crud" | Read mcp-tool-design.md |
| 4, "domain tool", "when to add" | Read from-primitives-to-domain-tools.md |
| 5, "execution", "completion", "loop" | Read agent-execution-patterns.md |
| 6, "prompt", "system prompt", "behavior" | Read system-prompt-design.md |
| 7, "context", "inject", "runtime", "dynamic" | Read dynamic-context-injection.md |
| 8, "parity", "ui action", "capability map" | Read action-parity-discipline.md |
| 9, "self-modify", "evolve", "git" | Read self-modification.md |
| 10, "product", "progressive", "approval", "latent demand" | Read product-implications.md |
| 11, "mobile", "ios", "android", "background", "checkpoint" | Read mobile-patterns.md |
| 11a, "icloud", "storage", "documents", "file state", "entitlement" | Read mobile-storage.md |
| 11b, "background task", "battery", "on-device", "cloud routing" | Read mobile-execution.md |
| 11c, "model tier", "token budget", "cost-aware", "batch", "caching" | Read mobile-cost.md |
| 12, "test", "testing", "verify", "validate" | Read agent-native-testing.md |
| 13, "review", "refactor", "existing" | Read refactoring-to-prompt-native.md |
| 14, "anti-pattern", "mistake", "wrong" | Read anti-patterns.md |
| 15, "success", "criteria", "verify", "checklist" | Read success-criteria.md |
| 16, "hook", "hooks", "PreToolUse", "decision control", "async hook" | Read hooks-patterns.md |
| 0, "quick start", "getting started", "overview", "introduction" | Read quick-start.md |
After reading the reference, apply those patterns to the user's specific context.
When designing an agent-native system, verify these before implementation:
z.string() inputs when the API validates, not z.enum()complete_task tool (not heuristic detection)refresh_context tool)When designing architecture, explicitly address each checkbox in your plan.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.