Identify 3DMigoto mod folders using a four-layer metadata matching pipeline.
npx skills add https://github.com/reynalivan/EMMM2 --skill deep-matcher
Please help me install this Agent Skill. Skill: deep-matcher Source: https://github.com/reynalivan/EMMM2/tree/main/.agent/skills/deep-matcher Command: npx skills add https://github.com/reynalivan/EMMM2 --skill deep-matcher
Logic for identifying Mods using a 4-Layer Pipeline (Name -> Token -> Content -> Fuzzy). Use when: (1) Scanning a new directory like /Mods, (2) Re-scanning a specific folder for metadata updates, (3) Identifying if a folder is a valid "3DMigoto Mod" based on content, or (4) Debugging why a mod isn't matching correctly.
Run Deep Matcher on a mods directory to score and identify valid mods against the known metadata.
High-intent search queries and answers about installing and using this skill.
To identify 3DMigoto mod folders, apply a structured 4-layer pipeline that maps directories to known metadata by matching names, tokens, content, and fuzzy patterns. This process classifies and scores folders to determine valid 3DMigoto mods.
A 3DMigoto mod folder might not match due to naming mismatches or invalid content. Debugging the mod identification process involves checking the 4-layer pipeline to see where normalization, token matching, content scanning, or fuzzy matching fails.
The 4-layer matching pipeline works by sequentially applying Name, Token, Content, and Fuzzy matching to map mod directories to known metadata. It normalizes directory data first, then scores folders through each layer to classify valid 3DMigoto mods.
Yes, you can scan large mod directories without performance issues by using tokio::spawn_blocking for thread offloading, enforcing max depth limits, and utilizing in-memory caching. These performance guards ensure scalable scanning within the Rust pipeline.
The best way to re-scan a specific mod folder for metadata updates is to execute the mod identification pipeline on that single directory. The pipeline re-evaluates the folder's content and structure against the known metadata database to update its classification.
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