# Image Layer Alignment Validator > Validate whether two raster image layers are suitable for reveal, before/after, morph, mask-compositing, or interactive cursor reveal effects. Use when comparing base/reveal images, checking whether the main subject stayed in the same position, separating primary subject drift from background or secondary-object differences, producing annotated overlays/difference maps, or deciding whether an ima… - **Type:** Skill - **Install:** `agentstack add skill-sergekostenchuk-ui-ux-agent-skill-system-image-layer-alignment-validator` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [sergekostenchuk](https://agentstack.voostack.com/s/sergekostenchuk) - **Installs:** 0 - **Category:** [Content & Media](https://agentstack.voostack.com/c/content-and-media) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [sergekostenchuk](https://github.com/sergekostenchuk) - **Source:** https://github.com/sergekostenchuk/ui-ux-agent-skill-system/tree/main/core/skills/image-layer-alignment-validator ## Install ```sh agentstack add skill-sergekostenchuk-ui-ux-agent-skill-system-image-layer-alignment-validator ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Image Layer Alignment Validator Use this skill to check whether two image layers can be composited as the same scene or subject. The goal is not generic image critique; the goal is to decide whether a base layer and reveal layer are spatially compatible. ## Modes - `compare`: analyze two local image files and produce visual/report artifacts. - `diagnose`: inspect an existing report or screenshots and explain why a reveal/morph looks misaligned. - `advise`: convert measured drift into concrete fixes: shift, scale, crop, regenerate, or accept. - `threshold`: tune acceptance thresholds for strict product/portrait work versus looser creative reveal effects. ## Workflow 1. Confirm there are exactly two intended layers: base and reveal/after. 2. Keep all analysis local by default. Do not upload private images to external services unless the user explicitly requests that. 3. Run `scripts/compare_layers.py` when local image paths are available: ```bash python3 $CODEX_SKILLS_DIR/image-layer-alignment-validator/scripts/compare_layers.py \ --base /path/to/base.png \ --reveal /path/to/reveal.png \ --out /path/to/alignment-output ``` 4. Inspect the generated artifacts before giving a verdict. The script is a deterministic foreground/geometry heuristic; semantic judgment still matters. 5. If the main subject is ambiguous, read `references/subject-taxonomy.md` and state the chosen primary subject explicitly. 6. Score alignment with `references/alignment-rubric.md`. 7. Report measured drift and a concrete next action. ## Evidence Artifacts The comparison script writes: - `alignment-report.md`: human-readable metrics, verdict, and suggested fixes. - `alignment-metrics.json`: machine-readable dimensions, boxes, drift, IoU, and verdict. - `annotated-base.png`: detected primary and secondary boxes on the base layer. - `annotated-reveal.png`: detected primary and secondary boxes on the reveal layer. - `side-by-side.png`: visual comparison with boxes. - `overlay.png`: reveal blended over base for quick inspection. - `difference.png`: amplified pixel difference map. ## Decision Rules - Treat the measured primary subject box as evidence, not truth. Override it when visual inspection clearly finds a different main object. - Prefer normalized measurements for verdicts so different canvas sizes are comparable. - For cursor reveal or mask reveal, the main subject should usually be stricter than the background. Background, lighting, texture, and small accessory differences can change without failing the pair. - If the subject center drift is visible in the intended reveal area, recommend image correction before frontend work. - If the reveal image is a genuinely different subject, do not try to hide it with CSS/canvas tuning. ## Safety And Privacy - Process local images locally by default. - Do not upload private, client, face, identity, or unpublished creative images to external services unless the user explicitly requests that route. - Do not overwrite source images. Write reports and derived artifacts to a separate output directory. - When recommending regeneration, describe the intended geometry constraints instead of embedding private image details into reusable skill files. ## Validation And Eval - Validate the script with representative pairs before trusting new thresholds. - Check `alignment-report.md` and at least one visual artifact before returning a verdict. - Treat `inconclusive` as a valid outcome when foreground detection fails. - For strict effects, re-run the script after any shift, crop, scale, or regeneration step. - Forward-test the skill with examples that include aligned pairs, small subject drift, different canvas sizes, and unrelated reveal subjects. ## Output Format Return: ```text Verdict: aligned | minor drift | misaligned | different subject | inconclusive Evidence: - Primary subject: ... - Center drift: ... px (... normalized) - BBox IoU: ... - Scale delta: ... - Secondary-object notes: ... Action: - ... Artifacts: - /absolute/path/alignment-report.md - /absolute/path/side-by-side.png - /absolute/path/overlay.png ``` ## Resources - Read `references/subject-taxonomy.md` when deciding what counts as the primary subject versus secondary objects. - Read `references/alignment-rubric.md` when grading output or tuning thresholds. - Use `scripts/compare_layers.py` for local deterministic evidence. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [sergekostenchuk](https://github.com/sergekostenchuk) - **Source:** [sergekostenchuk/ui-ux-agent-skill-system](https://github.com/sergekostenchuk/ui-ux-agent-skill-system) - **License:** Apache-2.0 Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-sergekostenchuk-ui-ux-agent-skill-system-image-layer-alignment-validator - Seller: https://agentstack.voostack.com/s/sergekostenchuk - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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