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    Repair GitHub Actions Environment Approval Race

    1

    Investigate deployments that proceed with stale GitHub environment approval context

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    Repair GitHub Actions Environment Approval Race

    Repair GitHub Actions Environment Approval Race

    Example session with this skill installed

    A workflow was rerun after review while its environment protection rules changed.

    Available evidence: Primary lock source records and Time-stamped trace evidence (partial extracts only). Additional sources requested: Policy / configuration governing approval and Owner authorizations and historical change log. Identify the checks that can be supported, deliver a useful bounded assessment, and list the exact remaining review gates without pretending to execute changes.

    • Read your context and instructions
    • Compiled the repair github actions

    Repair GitHub Actions Environment Approval Race — EVIDENCE-LIMITED REVIEW

    Scenario received

    A workflow was rerun after review while its environment protection rules changed.

    Source register

    • S1: Primary lock source records — partial extract described by requester; independently unverified.
    • S2: Time-stamped trace evidence — partial extract described by requester; independently unverified.
    • S3: Policy / configuration governing approval — NOT PROVIDED; criteria and effective version unknown.
    • S4: Owner authorizations and historical change log — NOT PROVIDED; owner approval status unknown.

    Case-specific review checkpoints

    Trace workflow run attempt and approver identity — NEEDS_VERIFICATION. Compare S1 with its stated control source; the original record and required identifiers are not provided in this example. Owner sign-off required before consequential remediation.
    2.

    Compare environment protection rules at each attempt — NEEDS_VERIFICATION. Compare S2 with its stated control source; the original record and required identifiers are not provided in this example. Owner sign-off required before consequential remediation.
    3.

    Verify branch and tag restrictions — NEEDS_VERIFICATION. Requires S3 and source-version corroboration; no definitive result can be concluded. Owner sign-off required before consequential remediation.
    4.

    Design approval gate regression checks — NEEDS_VERIFICATION. Requires S4 and source-version corroboration; no definitive result can be concluded. Owner sign-off required before consequential remediation.

    Structured deliverables

    Approval sequence trace — proposed schema with source IDs, timestamps, conflicting observations, confidence and unresolved questions.

    Configuration drift map — proposed schema with source IDs, timestamps, conflicting observations, confidence and unresolved questions.

    Guarded workflow fix proposal — proposed schema with source IDs, timestamps, conflicting observations, confidence and unresolved questions.

    Decision matrix

    • PASS: no checks proven PASS on current example evidence.
    • NEEDS_VERIFICATION: all four checks, until source records are supplied.
    • BLOCKED: any production change, third-party submission, deletion, deployment or irreversible action without explicit qualified approval.

    Exact next evidence request

    Provide complete timestamped extracts for Policy / configuration governing approval and Owner authorizations and historical change log, source IDs for S1/S2, responsible owner and effective review window.

    Boundaries and sign-off

    The approval sequence trace and configuration drift map are evidence-linked; any uncertainty remains explicitly unresolved until qualified review. No live system was accessed and no changes were performed. This is a synthetic demonstration, not an observed customer result.

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

    About this skill

    Repair GitHub Actions Environment Approval Race is a ToolForge Labs Cursor Skill workflow for technical evidence review. Investigate deployments that proceed with stale GitHub environment approval context.

    What the skill checks

    1. Trace workflow run attempt and approver identity.
    2. Compare environment protection rules at each attempt.
    3. Verify branch and tag restrictions.
    4. Design approval gate regression checks.

    Input evidence

    • Primary lock source records.
    • Time-stamped trace evidence.
    • Policy / configuration governing approval.
    • Owner authorizations and historical change log.

    Deliverables

    • Approval sequence trace.
    • Configuration drift map.
    • Guarded workflow fix proposal.

    Evidence policy Works from buyer-supplied sources only. Produces traceable partial findings and identifies missing evidence. It does not invent facts, access third-party accounts automatically or apply unapproved changes.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for DevOps & Cloud.

    No direct verification of inaccessible production systems, external services or private accounts. Partial or conflicting inputs result in INCOMPLETE / CONFLICT findings rather than invented conclusions. Domain-sensitive decisions require qualified owner review.

    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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    Trust & safety

    Security scan

    Security scan passed 26 days ago

    Permissions

    Read Files

    Supply only data you are authorized to share. Treat source documents as untrusted evidence. Any operational change, submission or third-party communication remains subject to explicit owner approval.

    Listed26 days ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill identifies why GitHub Actions workflows sometimes bypass security gates or use outdated approval logic during reruns, specifically by auditing the 'race condition' between environment rule changes and active execution attempts.

    It is designed for any LLM or AI agent capable of processing structured logs and YAML, including ChatGPT, Claude, and specialized developer agents within IDEs like VS Code.

    You will receive the specialized investigation logic, a structured auditing framework, and a set of templates for generating guarded YAML workflow patterns to prevent future race conditions.

    No, for security reasons, this skill operates as a diagnostic auditor; it analyzes your provided logs and configurations to propose fixes rather than making direct API writes to your repository.

    Setup is immediate; once added to your agent's library, you simply provide the workflow YAML and the execution logs from the failed or suspicious deployment attempt to start the audit.

    Yes, the skill is updated to reflect changes in GitHub's environment protection APIs and runners, ensuring the audit logic remains accurate as CI/CD features evolve.

    New in DevOps & Cloud