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    Image Prompt Consistency Guard

    2

    Audits AI image prompt sets for drift in style, identity, lighting, and composition.

    Free

    24 uses5.0 (2 reviews)
    Image Prompt Consistency Guard

    Image Prompt Consistency Guard

    Example session with this skill installed

    Audit these two prompts for a high-end watch brand. Model: Midjourney.

    1. Close up of a silver watch on a marble table, soft morning light.
    2. Man wearing a gold watch in a dark office, moody lighting.
    • Read your context and instructions
    • Compiled the image prompt consistency

    I have audited the prompt set for the high-end watch series. The set requires restructuring due to a fundamental mismatch in product material (silver vs. gold) and environmental lighting, which will break visual continuity.

    Consistency Audit & Corrected Prompts

    Consistency Verdict

    The set needs restructuring. The primary issue is the change in the product's base material and the total lack of shared lighting logic. Without alignment, these will appear to be from two different brands or collections rather than a unified campaign.

    Scorecard

    MetricScoreEvidence-based Reasoning
    Identity/Geometry40Material switches from silver to gold; product consistency is lost.
    Style & Medium80Both imply a high-end photography medium.
    Composition60One is a product close-up, the other is a lifestyle shot; acceptable but needs alignment.
    Lighting30"Soft morning light" vs "moody office" creates an irreconcilable contrast.
    Palette40Marble/Silver vs Dark/Gold creates two different color stories.
    Prompt Clarity70Instructions are simple but lack specific technical camera or material descriptors.

    Drift Table

    PromptIssueSeverityWhy it MattersRepair
    1Ambiguous materialCritical"Silver" vs "Gold" in P2 breaks the product line.Standardize to Polished Silver.
    2Lighting conflictMajorMoody office light clashes with morning marble setup.Shift to "low-key morning light" for continuity.
    2Subject driftCriticalGold material contradicts the primary product SKU.Update to Polished Silver.

    Locked Visual DNA

    [Subject: Polished Silver Luxury Watch, sapphire crystal, brushed steel lugs] 
[Medium: Commercial product photography, 85mm lens, f/4.0] 
[Lighting: Soft directional light, high micro-contrast, clean shadows]
[Palette: Cool neutrals, silver, slate, white marble accents]

    Corrected Prompt Set

    Prompt 1: Close up of a polished silver luxury watch on a white marble table, soft directional morning light, 85mm lens, f/4.0, high micro-contrast --ar 4:5
    2.

    Prompt 2: Medium shot of a man in a charcoal suit wearing a polished silver luxury watch, standing in a dim office with soft directional morning light hitting the wrist, 85mm lens, f/4.0, high micro-contrast --ar 4:5

    Next steps

    • Run both prompts using the same --seed value to verify the silver material rendering.
    • Check if the "morning light" in the office setting requires more specific window-light keywords.
    • Confirm if the watch face design needs a specific "complication" description to maintain geometry.

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

    About this skill

    The problem

    Generating a series of AI images often results in visual drift where subjects, lighting, and product geometry change unexpectedly between prompts. Manually aligning these variables across a large set is error-prone and leads to inconsistent brand assets or storyboards.

    What it does

    • Identifies intended constants and variables across a set of image prompts.
    • Extracts a visual DNA covering subject identity, lighting, palette, and medium.
    • Audits prompts for critical mismatches in product geometry and rendering style.
    • Repairs prompt syntax to maintain consistency while preserving intentional scene variations.
    • Generates a verification plan to isolate variables during test generations.

    Why this beats prompting it yourself

    Instead of guessing why image three looks different than image one, this skill systematically decomposes the visual attributes. It treats your prompt set as a coherent system, ensuring that shared elements like material treatment and camera family remain locked across the entire series.

    Use cases

    • Maintaining product geometry across a series of e-commerce lifestyle shots.
    • Ensuring character identity remains stable across storyboard frames.
    • Aligning lighting and palette for a multi-channel social media ad campaign.
    • Developing consistent prompt packs for stock photography or design systems.

    Known limitations

    Cannot guarantee identical outputs due to the probabilistic nature of image models. Requires at least two related prompts to perform a meaningful consistency audit.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Image Generation Skills.

    Cannot guarantee identical outputs due to model variance. Best results require providing the target image model. Does not audit single, unrelated prompts.

    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.

    Reviews

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    This skill has been used 24 times.

    Trust & safety

    Security scan

    Security scan passed 1 month ago

    • Free with an Agensi account

    Permissions

    No special permissions declared or detected

    Listed1 month ago

    What's inside

    Frequently Asked Questions

    This skill acts as a visual auditor that analyzes your prompt sets to identify 'drift' in elements like lighting, character identity, and camera angles. It then repairs the syntax to ensure your subject remains consistent across an entire series of generations.

    Yes, the skill is model-agnostic and works with prompt structures for Midjourney, DALL-E 3, Stable Diffusion, and Flux. It focuses on the linguistic and thematic DNA of the prompt rather than model-specific parameters.

    To perform a meaningful audit, you should provide at least two related prompts. The skill works best when analyzing a batch of 3-10 prompts intended to share a common visual theme or subject.

    You will receive the 'Consistency Guard' logic set, a guide on how to input your prompt batches, and a template for generating verification plans to test your results.

    While the skill significantly reduces variance by locking in visual attributes, it cannot bypass the inherent randomness of AI seeds. It ensures the 'instructions' are identical, but the image generator's probabilistic nature may still produce minor differences.

    New in Image & Video

    Free