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    Software Service Decommission & Dependency Readiness Review

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    Software Service Decommission & Dependency Readiness Review is a bounded ToolForge Labs workflow.

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    Software Service Decommission & Dependency Readiness Review

    Software Service Decommission & Dependency Readiness Review

    Example session with this skill installed

    We want to retire an internal report service after moving users to a replacement. It still has cron jobs, one export endpoint, old dashboards, service credentials, and an S3 archive. Review whether it is safe to decommission and what evidence is missing.

    • Read your context and instructions
    • Compiled the software service decommission

    The likely issue is a contract mismatch around software and service decommissioning, callers, jobs, data ownership, queues, DNS, credentials, dashboards, alerts, documentation, billing, compliance evidence, archival needs, sequencing, rollback limits, and operational ownership. I would first define the invariant: A software component is ready to retire only when every known dependency, data obligation, operational control, credential, scheduled path, and fallback/rollback decision has an owner and evidence. Then I would trace the smallest owning boundary, keep VERIFIED facts separate from LIKELY/UNKNOWN state, apply or propose the narrowest repair, and add deterministic normal, boundary, failure, retry/cancellation, and recovery tests. The workflow preserves authorization, privacy, security policy, public interfaces, user-visible behavior, and documented ownership. With repository/workspace access, findings can map to exact paths and focused local validation; with partial evidence, the result remains an explicit diagnosis/evidence map with no invented execution claims.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Software Service Decommission & Dependency Readiness Review is a bounded ToolForge Labs workflow. Diagnose and repair software and service decommissioning, callers, jobs, data ownership, queues, DNS, credentials, dashboards, alerts, documentation, billing, compliance evidence, archival needs, sequencing, rollback limits, and operational ownership. Works from repositories/workspaces or partial evidence. Produces bounded fixes and deterministic validation while preserving security, public behavior, and documented ownership without inventing executed work. It works from a full repository/workspace or partial evidence such as snippets, logs, configuration, traces, documents, tickets, and architecture notes. The skill separates verified facts from assumptions, avoids invented execution claims, and produces concrete repair/review steps with focused deterministic validation or traceable evidence.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for LLM Ops.

    Requires local files, logs, or traces for verification. Cannot access live production billing systems or external cloud consoles directly.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Use a clean branch or controlled review workspace. Supply repository/workspace files, logs, traces, configuration, documents, or evidence needed for the task. Review patches/findings before merge or operational action; keep production credentials and destructive production actions out of the workflow.

    Listed1 month ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill automates the complex audit of service dependencies—such as DNS, queues, and credentials—to ensure a safe decommissioning process without accidental outages or data loss.

    The skill can process full repository workspaces or partial evidence like logs, traces, and architecture notes to identify hidden dependencies.

    Yes, it generates concrete repair steps and deterministic validation checks while strictly avoiding the invention of execution claims or 'hallucinated' work.

    The skill is designed to integrate with AI agents that have access to your CI/CD environment or cloud console, ensuring all security and compliance boundaries are maintained.

    You receive the complete ToolForge Labs workflow, including the diagnostic logic for dependency mapping and the validation frameworks for decommissioning sequencing.

    New in LLM Ops