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competition-agent-cloud

Trace prompt-to-tool flows and provenance across cloud and CI/CD workflows.

4|Updated Apr 9, 2026
Search Tags:#provenance#ci-cd#sandbox#cloud-security#prompt-injection#competition-agent-cloud
One-click install
npx skills add https://github.com/xjtu-wang/DigAgent --skill competition-agent-cloud
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: competition-agent-cloud
Source: https://github.com/xjtu-wang/DigAgent/tree/main/.agents/skills/competition-agent-cloud
Command: npx skills add https://github.com/xjtu-wang/DigAgent --skill competition-agent-cloud

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill analyzes and traces the end-to-end flow of prompts, tools, and cloud-based components to uncover prompt-injection risks, provenance gaps, and deployment drift within sandboxed AI workflows.

Core Features & Use Cases

  • Downstream specialization after sandbox orchestration to assess prompt-to-tool interactions and evidence provenance.
  • Evaluate prompt-injection, retrieval poisoning, and deployment drift across cloud, container, and CI/CD layers.
  • Capture compact evidence blocks including prompts, tool arguments, manifests, mounts, and provenance steps for auditing.

Quick Start

Decide whether the dominant path is agentic or infrastructure-driven, map a minimal control chain from untrusted input to a tool or deployment side effect, distinguish checked-in intent from live runtime truth, keep prompts and provenance steps in compact evidence blocks, and reproduce the exploit or misconfiguration with minimal context.

Frequently Asked Questions about competition-agent-cloud

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

FAQPage Schema
How do I trace prompt-to-tool flows and provenance in cloud environments?▼

Analyzing prompt-injection risks involves evaluating prompt-to-tool interactions and evidence provenance within sandboxed AI workflows. It distinguishes checked-in intent from live runtime truth to uncover retrieval poisoning and deployment drift across cloud and container layers.

When do I need provenance tracking for sandboxed AI workflows?▼

Provenance tracking is needed when auditing sandboxed AI workflows for prompt-injection risks and deployment drift. It provides evidence blocks, manifests, and provenance steps that ensure auditability for prompt-to-tool interactions across cloud, container, and CI/CD pipelines.

Does this CI/CD provenance analysis require a sandboxed orchestrator context?▼

Yes, assessing prompt-to-tool interactions and evidence provenance requires a sandboxed orchestrator context. This environment allows you to map the control chain from untrusted input to deployment side effects safely and reproduce exploits with minimal context.

What is the best way to identify deployment drift in containerized AI pipelines?▼

Identifying deployment drift involves distinguishing checked-in intent from live runtime truth across container and CI/CD layers. By mapping a minimal control chain from untrusted input to side effects, you can evaluate drift and capture the evidence in compact manifests.

How do I reproduce prompt injection exploits with minimal context?▼

Reproducing prompt injection exploits requires mapping a minimal control chain from untrusted input to a tool or deployment side effect. You keep prompts and provenance steps in compact evidence blocks to reproduce the misconfiguration with minimal context.

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