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    ML CI/CD Gate Designer

    1

    Design evidence-bound model promotion gates, rollout criteria, owners, and rollback wiring as a reusable MODEL-GATE-SPEC.

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    ML CI/CD Gate Designer

    ML CI/CD Gate Designer

    Example session with this skill installed

    Design promotion gates for a binary classification model used to prioritize manual reviews. Errors have unequal business cost, true labels arrive after 30 days, the team can shadow traffic but cannot yet split live traffic, and deployments currently require human approval. We have a trusted offline baseline but no tuned online rollback threshold.

    • Read your context and instructions
    • Compiled the ml ci cd
    • Generated the UI component

    ARTIFACT: MODEL-GATE-SPEC
    VERSION: 1
    MODE: DESIGN
    SOURCE_SKILL: ml-cicd-gate-designer

    SUMMARY
    The current platform can support evidence-bound offline and shadow gates, but live canary promotion and automatic rollback remain blocked until traffic splitting and a fast operational baseline exist.

    ERROR-COST ASYMMETRY
    False negatives carry the higher declared business cost. [Verified]

    GATE SEQUENCE
    G1 — Data validation
    Criterion: required feature contract and missingness checks pass.
    Threshold: CALIBRATE from the last accepted production batch.
    Owner: Data platform owner.

    G2 — Offline evaluation
    Criterion: high-cost error metric does not regress against the trusted baseline.
    Threshold: derive from the supplied baseline before enforcement.
    Owner: Model owner.

    G3 — Operational readiness
    Criterion: prior model version remains available and the human approval owner is named.
    Threshold: required before promotion.
    Owner: Release owner.

    ROLLOUT
    Current state: shadow rollout because live traffic splitting is unavailable.
    Comparison: prediction agreement, fast operational proxies, and delayed true-label review. Shadow results are evidence for a later promotion decision; they do not alter production outcomes.

    ROLLBACK
    Automatic live rollback: BLOCKED by current traffic controls.
    Immediate safeguard: retain the prior version and require human promotion approval. If a fast operational proxy breaches its calibrated boundary, stop promotion and keep the prior version active.

    PHASE 1
    Implement data and offline gates, shadow comparison, named owners, retained prior version, and a documented manual stop decision.

    PHASE 2
    Add controlled traffic splitting and an automated rollback mechanism only after thresholds and observation windows are calibrated.

    OPEN CALIBRATE ITEMS

    • Accepted feature-missingness range from historical production batches
    • Fast proxy baseline and observation window
    • Minimum shadow comparison volume
    • Maximum promotion decision latency

    NEXT STEPS
    Confirm the gate owners, supply historical baselines, and verify the rollback mechanism before converting this specification into CI configuration.

    ml-ci-cd-gate-designer.tsx

    TSX · React component

    Generated

    Example file from a real run - the skill writes it into your workspace.

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

    What you get

    Design model promotion gates before productionChoose shadow, canary, or blue-green rolloutDefine rollback criteria and decision ownersPlan phased ML CI/CD adoption

    About this skill

    Turn a risky model release process into an explicit promotion contract

    ML CI/CD Gate Designer converts supplied model context, trusted baselines, rollout capabilities, and team constraints into a structured MODEL-GATE-SPEC. Instead of a generic checklist, it defines which gates apply, what each gate measures, who owns the decision, how rollout proceeds, and what triggers rollback.

    What it produces

    • Gate table covering offline evaluation, data validation, rollout checks, operational readiness, and rollback controls
    • Thresholds traced to supplied baselines, or marked CALIBRATE when evidence is missing
    • Shadow, canary, blue-green, or direct rollout decision with explicit constraints
    • Rollback conditions, observation windows, mechanisms, and escalation owners
    • Practical Phase 1 for current maturity and a separate Phase 2 target state
    • Consolidated list of unresolved calibration items

    Designed for real deployment constraints

    The workflow accounts for asymmetric error costs, delayed labels, traffic-splitting limitations, feedback loops, and manual deployment teams. It can use supplied artifacts from companion MLOps skills to keep serving, drift, and maturity recommendations consistent.

    Evidence boundary

    This skill designs a specification from information you provide. It does not inspect infrastructure, run evaluations, deploy models, change CI pipelines, monitor production, or execute rollback. Template thresholds are never presented as tuned values.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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      Ask your agent to use it

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