Image Matching WebUI is a graphical tool for finding corresponding keypoints between two images with different image-matching algorithms. Users can select local or webcam images, choose an algorithm, and inspect the matching result through a Gradio interface.
Borrowing it
Nothing to install: this file belongs to Vincentqyw/image-matching-webui. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Vincentqyw/image-matching-webui/main/.claude/skills/integrate-matcher/SKILL.mdgit clone --depth 1 https://github.com/Vincentqyw/image-matching-webuiWrote 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/vincentqyw/image-matching-webui/integrate-matcher)<a href="https://agentmods.dev/skills/vincentqyw/image-matching-webui/integrate-matcher"><img src="https://agentmods.dev/badge/skills/vincentqyw/image-matching-webui/integrate-matcher/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/vincentqyw/image-matching-webui/integrate-matcher"><img src="https://agentmods.dev/badge/skills/vincentqyw/image-matching-webui/integrate-matcher.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 | $0.00044 | $0.05184 |
| Opus 5.5 | $0.00018 | $0.02074 |
| Sonnet 5.5 | $0.00009 | $0.01037 |
| Haiku 4.5 | $0.00004 | $0.00518 |
Grade C, and why
integrate-matcher scanned grade C with 1 finding 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 2mo 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf .git/modules/imcui/third_party/<RepoName> How it starts
The opening of the file, as written. The whole thing — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integrate Matcher into image-matching-webui
This skill automates the integration of a new local feature matching method into the image-matching-webui project. Given a GitHub repository URL, it follows the project's established patterns to add the matcher as a fully functional option in the WebUI.
Prerequisites
- The target repository must be a local feature matching method (sparse or standalone)
- You must be working inside the
image-matching-webuiproject root
Step-by-Step Integration Guide
Step 1: Analyze the Target Repository
- Clone the repo to
/tmp/<repo-name>for analysis (do NOT add as submodule yet) - Identify the matcher type:
- Dense/Standalone matcher: Takes raw images as input, performs detect+describe+match internally (e.g., LoMa, RoMa, LoFTR).
required_inputs = ["image0", "image1"] - Sparse matcher: Takes keypoints+descriptors as input, performs matching only (e.g., LightGlue, SuperGlue).
required_inputsincludeskeypoints0,descriptors0, etc.
- Dense/Standalone matcher: Takes raw images as input, performs detect+describe+match internally (e.g., LoMa, RoMa, LoFTR).
- Find the core model class and its API:
- How to initialize the model (constructor args, config options)
- How to run inference (forward method signature)
- What the model outputs (keypoints, matches, scores, etc.)
- Model weight download URLs
- Check dependencies in
pyproject.tomlorrequirements.txt - Identify model variants (e.g., different sizes: B/L/G/R)
- Check for device/amp issues:
- Does the model use
torch.autocastor mixed precision (mp,amp)? - Does it manage its own device placement (like LoMa's
loma.device)? - On MPS/CPU, does it produce dtype mismatches?
- Does the model use
Step 2: Add Git Submodule
git submodule add <repo-url> imcui/third_party/<RepoName>
- Use the original repo name (PascalCase) as the submodule directory name
- If the repo has a fork in the project's org (e.g.,
Vincentqyw/xxxoragipro/xxx), prefer the fork
⚠️ CRITICAL: Never modify third_party code directly
imcui/third_party/ contains pinned third-party submodules — you are NOT the owner of this code. If a dependency needs a compatibility fix (e.g., API changes in PyTorch/kornia, import path changes):
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.
- 2mo ago First seen · 483 lines · 44 tokens per session scan C 1035088ea7c1
integrate-matcher is a skill published in the GitHub repository Vincentqyw/image-matching-webui (1,314 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 5,184 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
integrate-matcher compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
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