A specialized instruction-set skill designed to keep AI agents focused on explicitly requested tasks while preventing unauthorized data access or mission creep.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aana-task-scope-guardrail
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install aana-task-scope-guardrail using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AANA Task Scope Guardrail Skill?
The AANA Task Scope Guardrail Skill serves as a critical logic layer for autonomous agents, ensuring they stay within the boundaries of a user's request. It functions as an instruction-only framework that guides an agent to differentiate between necessary actions and unnecessary task expansion. By implementing this within your Openclaw Skills setup, you provide agents with a structured methodology to evaluate their own proposed actions before execution, which significantly improves safety and operational efficiency.
This skill is particularly valuable for developers who need to ensure that their agents do not inadvertently access sensitive information, perform unrequested refactoring, or continue processing after a goal has been reached. It promotes a philosophy of minimal viable action, requiring the agent to justify its data usage and task boundaries at every step of the workflow.
AANA Task Scope Guardrail Skill Use Cases
- Restricting an agent to a specific code patch to prevent unwanted global refactoring.
- Preventing automated research agents from accessing unrelated private files or logs.
- Ensuring a purchasing agent stops immediately after a transaction is confirmed.
- Guarding against the reuse of sensitive context from one task in a completely different task.
- Managing complex workflows where clear stop conditions are required to prevent tool-use loops.
How AANA Task Scope Guardrail Skill Works
- The agent identifies the current user request to establish the baseline intent.
- A smallest useful completion target is defined to set a clear boundary for success.
- The agent categorizes its proposed next action into specific scope tiers like in_scope or necessary_support.
- A data relevance check is performed to ensure only task-relevant context is utilized.
- The agent verifies authorization, especially if the action affects external states or private accounts.
- A final decision is made to proceed, narrow the scope, ask for clarification, or stop entirely.
AANA Task Scope Guardrail Skill Setup
This is an instruction-only skill and does not require traditional package installation. To integrate it into your Openclaw Skills environment, include the AANA principles in your agent's system instructions.
# No CLI installation required
# Simply incorporate the AANA Scope Gate logic into your agent prompt configuration
AANA Task Scope Guardrail Skill Data Schema & Taxonomy
The skill utilizes a structured evaluation pattern to manage task boundaries. The data schema for the scope gate is organized as follows:
| Attribute | Description |
|---|---|
| user_request | The raw input or goal provided by the user. |
| completion_target | The specific evidence or state that signifies task completion. |
| scope_status | Classification: in_scope, necessary_support, clarification_needed, optional_followup, out_of_scope, or stop. |
| data_relevance_status | Verification that the data used is relevant or if it contains sensitive extra data. |
| authorization_status | The level of user permission (explicit, implicit, or not authorized). |
| recommended_action | The final tactical decision (proceed, narrow, ask, suggest, stop, or refuse). |
AANA Task Scope Guardrail Skill Advanced Features
- Redacted Review Payloads: Enables sending minimal metadata to external checkers without exposing raw secrets or private transcripts.
- Granular Scope Categories: Supports complex decision-making by distinguishing between necessary support and optional follow-ups.
- Task Expansion Rules: Built-in logic to handle ambiguous boundaries and high-impact follow-up changes.
- Automated Completion Logic: Specific rules to force an agent to cease tool use once the primary artifact is delivered.
SKILL.md
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