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whitebox

Expose AI run state, decisions, and gates as a file-based surface.

17|3|Updated Jan 23, 2026
Search Tags:#governance#recovery#control#visibility#approvals#gate#whitebox
One-click install
npx skills add https://github.com/insightflo/claude-impl-tools --skill whitebox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: whitebox
Source: https://github.com/insightflo/claude-impl-tools/tree/main/plugin/skills/whitebox
Command: npx skills add https://github.com/insightflo/claude-impl-tools --skill whitebox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

AI-driven code execution often runs behind the scenes with little transparency. Whitebox introduces a file-based surface that shows current run state, blockers, and approvals, enabling quick understanding and safe intervention. It also provides a canonical control path for approving or rejecting gates and resuming execution.

Core Features & Use Cases

  • Observability: surface live run state, agents, and gates in a single dashboard-like surface.
  • Governance: manage approvals, blockages, and risk acknowledgments with deterministic operator input.
  • Recovery: persist and restore mission state and intervention queues across interruptions.
  • Use Case: When a gate blocks progress, operator can inspect evidence, make a decision, and resume with a validated plan.

Quick Start

Launch Whitebox to observe and control ongoing AI runs.

Frequently Asked Questions about whitebox

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get visibility into AI coding run state and decisions?▼

Visibility into AI coding run state is achieved through a file-based surface that exposes live run state, decisions, and gates. This enables quick observation and explanation of ongoing execution.

What is the best way to manage approvals and gates for AI code execution?▼

Governance for AI code execution is managed through deterministic operator commands that enforce a canonical artifact contract. Operators inspect evidence, approve or reject gates, and resume execution.

How do I safely recover AI agent state after an interruption?▼

Recovery after an interruption is handled by persisting and restoring mission state alongside intervention queues. This mechanism ensures validated plans and gate outcomes remain auditable across disruptions.

Can I use file-based artifacts to control AI execution workflows?▼

File-based artifacts can control AI execution workflows by enforcing a canonical control path for operator inputs. This allows operators to acknowledge risks, make decisions, and safely resume progress.

When do I need deterministic intervention for software development workflows?▼

Deterministic intervention is needed when software development workflows require visible governance and auditable gate outcomes. It allows operators to safely intervene when a gate blocks progress and resume execution.

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