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    Cross-Functional Release Readiness Gate

    1

    Converts scattered release evidence into a go, conditional-go, or no-go brief with explicit blockers and owners.

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    See it in action

    You say

    Analyze the attached QA report and deployment plan. Generate the readiness scorecard and blocker register. Cite all sources and list any missing observability metrics.

    Your agent does

    Readiness Scorecard

    • QA: Conditional (S1, p.4)
    • Ops: Blocker (Gap: Rollback threshold missing)

    Blocker Register

    1. Missing Rollback Thresholds | Owner: S. Chen | Impact: High
    2. Unresolved P2 Bug #402 | Owner: QA Team | Impact: Med

    Evidence Index

    [S1] QA_Final_Report.pdf [S2] Deploy_v2.md

    What you get

    Synthesize scattered evidence into a structured readiness scorecard.Identify and track explicit blockers with assigned owners and impact levels.Create an auditable evidence index mapping claims to source documents.Define rollback thresholds and authorities for production deployments.

    About this skill

    The problem

    Release cycles often stall because evidence for readiness is scattered across Slack threads, Jira tickets, and meeting notes. Stakeholders struggle to identify genuine blockers versus accepted risks, leading to delayed decisions or unsafe deployments.

    What it does

    • Analyzes raw release evidence to build a structured readiness scorecard.
    • Maps every material claim to specific source IDs and locators for full auditability.
    • Identifies explicit blockers, accepted risks, and follow-up actions with assigned owners.
    • Generates rollback reviews including specific thresholds and authority requirements.
    • Surfaces conflicting data and evidence gaps rather than smoothing them over.

    Why this beats prompting it yourself

    Standard LLM prompts tend to hallucinate missing information to be helpful. This skill enforces strict traceability rules that label every claim as source-backed, inferred, or unknown, ensuring you never sign off on a hallucination. It maintains stable item IDs across runs, allowing you to diff updates as new evidence arrives.

    Use cases

    • Synthesizing cross-functional sign-offs into a single Go/No-Go brief.
    • Creating an auditable evidence ledger for compliance-heavy production releases.
    • Triaging conflicting reports from QA, DevOps, and Product during a launch.
    • Building a gap register for missing documentation or test results prior to a deadline.

    Known limitations

    This skill provides a decision brief but will not execute deployments or grant final approvals. Security and legal reviews must remain human-owned.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Frequently Asked Questions