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kg-link-prediction SKILL.md skillA

kg-link-prediction from cuhk-aim-group/neurodiscovery is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned

mdr add cuhk-aim-group/neurodiscovery/kg-link-prediction@git:20260825.cbd02ff

Pins git:20260825.cbd02ff in mdr.lock. Pin the fingerprint instead.

Audit grade: A, 17 of 17 checks passed
Kind: SKILL.md skill
Repository: cuhk-aim-group/neurodiscovery
Path: skills/kg-link-prediction/SKILL.md
Versions recorded: 1
Latest version: git:20260825.cbd02ff, committed Aug 25, 2026
Size: 3,851 bytes
Repository stars: 93
License: MIT

Use this skill whenever the user wants knowledge-graph embeddings or graph neural link prediction over NeuroOracle: ComplEx, R-GCN, GraphSAGE, or GAT encoders; relation-aware triple scoring; filtered MRR/Hits evaluation; or hypothesis plausibility features. Triggers include 'KG embedding', 'link prediction', 'ComplEx', 'R-GCN', 'GraphSAGE', 'GAT', 'triple scoring', 'filtered ranking', 'MRR', 'Hits@K', and 'NeuroOracle GNN'.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# KG Link Prediction Skill

## Overview

`kg-link-prediction` trains embeddings or graph neural encoders on NeuroOracle
triples and scores candidate relations.

**Supported models**

| Model | Encoder | Decoder/evaluation |
|---|---|---|
| `complex` | complex-valued entity/relation embeddings | ComplEx score |
| `rgcn` | relation-specific graph convolution | relation-aware DistMult |
| `graphsage` | neighborhood aggregation | relation-aware DistMult |
| `gat` | graph attention | relation-aware DistMult |

GNN message passing uses training edges only. Validation and test edges are
excluded from encoder adjacency, and negative samples are checked against all
known positive triples.

---

## Installation

```bash
pip install numpy torch scikit-learn
```

The implementation uses native PyTorch operations and does not require
PyTorch Geometric.

---

## Workflows

### 1. Prepare a NeuroOracle graph

Input is a NeuroOracle `knowledge_graph.json` containing typed nodes and
confidence-bearing edges. Low-confidence edges can be excluded with
`--min-confidence`.

### 2. Train R-GCN

```bash
python skills/kg-link-prediction/scripts/train_reference.py \
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint

mdr add cuhk-aim-group/neurodiscovery/kg-link-prediction@sha256:8984a8bd65355eaa

The line at the top of this page pins the label git:20260825.cbd02ff, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

[![mdr](https://markdownregistry.com/badge/art_ttkj6qzpgw62gc7v.svg)](https://markdownregistry.com/a/art_ttkj6qzpgw62gc7v)

3 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Aug 25, 2026.

version	committed	commit	size	audit
git:20260825.cbd02ff latest	2026-08-25	cbd02ff	3,851 B	A	view

Audit of the latest version

A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.

pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (3851 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag

Source

GitHub

cuhk-aim-group/neurodiscovery · 93 stars · license MIT · pushed 2026-10-23 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_ttkj6qzpgw62gc7v
GET https://markdownregistry.com/api/v1/resolve?ref=cuhk-aim-group/neurodiscovery/kg-link-prediction
GET https://markdownregistry.com/api/v1/blob/8984a8bd65355eaa6414c3fda6b179361dd51056355a8da0b3fd9f4520568644

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.