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Architecture notes often leave attacker access and trust boundaries unclear. This skill turns existing Mantis knowledge into a structured threat model with deployment intent and snapshot provenance.

Large source trees make symbol navigation and caller discovery expensive. This skill guides structural indexing with reusable caches and coverage-aware queries while preserving lexical discovery.

Security fixes can fail when verification uses stale code or broken test harnesses. This skill guides minimal patches with snapshot checks, baseline tests, and independent re-attacks.

Long security reviews require coordinated specialist stages and reliable campaign state across repeated passes. This skill supervises Mantis subagents, preserves findings, and manages optional synchronization and snapshot provenance.

Past security fixes are difficult to track across repository history. This skill guides an AI agent to extract, classify, and cache historical vulnerabilities for later analysis.

Repeated security findings increase review effort and can obscure regressions. This skill guides consolidation while preserving provenance, active regression candidates, and transaction history.

Security findings need consistent priorities grounded in exploit evidence and architectural context. This skill applies a risk matrix and documented caps to existing Mantis findings.

ADK 代理專案需要在鷹架建立、測試、部署和營運之間完成許多相互連結的步驟。此技能提供分階段的 agents-cli 工作流程，協助程式設計代理一致地遵循這些步驟。

啟動 Google ADK agent 專案需要做出多項範本、部署與工作流程選擇。此 skill 會指引 agents-cli 腳手架流程，從原型設定到強化與升級規劃。

將 ADK 代理程式發布至 Gemini Enterprise 需要正確的註冊模式、資源 ID 和權限。此技能會引導 Claude、Codex 或 Claude Code 安全地完成 CLI 註冊與 Agent Registry 管理。

正式環境中的 ADK agents 需要追蹤、記錄與分析訊號，同時避免暴露提示資料。此技能會引導 Cloud Trace、提示記錄、BigQuery 分析與供應商選擇。

Agent 團隊在變更提示、工具或部署設定之前，需要可重複的證據。此技能會指引 Google ADK agents 的資料集設計、指標選擇、評分、比較與失敗分析。

在 Google Cloud 目標上部署 ADK 代理程式需要做出許多基礎架構選擇。此技能會引導目標選擇、部署命令、密鑰、CI/CD、測試與復原。

開發人員在建置 Python 代理程式時需要可靠的 ADK 模式。此技能提供工具、callbacks、狀態、多代理程式編排，以及工作流程程式碼的精簡參考。