Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sergekostenchuk/mouse-trail-masking-reveal --skill image-layer-alignment-validatorgit clone --depth 1 https://github.com/sergekostenchuk/mouse-trail-masking-revealWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sergekostenchuk/mouse-trail-masking-reveal/image-layer-alignment-validator)<a href="https://agentmods.dev/skills/sergekostenchuk/mouse-trail-masking-reveal/image-layer-alignment-validator"><img src="https://agentmods.dev/badge/skills/sergekostenchuk/mouse-trail-masking-reveal/image-layer-alignment-validator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sergekostenchuk/mouse-trail-masking-reveal/image-layer-alignment-validator"><img src="https://agentmods.dev/badge/skills/sergekostenchuk/mouse-trail-masking-reveal/image-layer-alignment-validator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00092 | $0.00984 |
| Opus 5 | $0.00046 | $0.00492 |
| Sonnet 5 | $0.00018 | $0.00197 |
| Haiku 4.5 | $0.00009 | $0.00098 |
Grade A, and why
image-layer-alignment-validator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to image-layer-alignment-validator — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
-
Confirm there are exactly two intended layers: base and reveal/after.
-
Keep all analysis local by default. Do not upload private images to external services unless the user explicitly requests that.
-
Run
scripts/compare_layers.pywhen local image paths are available:python3 scripts/compare_layers.py \ --base /path/to/base.png \ --reveal /path/to/reveal.png \ --out /path/to/alignment-output -
Inspect the generated artifacts before giving a verdict. The script is a deterministic foreground/geometry heuristic; semantic judgment still matters.
-
If the main subject is ambiguous, read
references/subject-taxonomy.mdand state the chosen primary subject explicitly. -
Score alignment with
references/alignment-rubric.md. -
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.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 98 lines · 92 tokens per session scan A b2ef7ace990e
image-layer-alignment-validator is a skill published in the GitHub repository sergekostenchuk/mouse-trail-masking-reveal (2 stars, last pushed 2mo ago), licensed MIT. It adds 92 tokens to every session and 984 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to image-layer-alignment-validator, differing in 2 lines, and is treated as a copy.
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