A set of checks for graph-based fraud detection, where connected data such as users, transactions, or accounts is examined for suspicious patterns. It follows the BWGNN research protocol on four named datasets: YelpChi, Amazon, T-Finance, and T-Social.

Use it to audit or fix dataset code and configuration, add abundant or scarce training splits, and run ten model re-initializations on one fixed split with mean and standard deviation results.

It prevents data loading, train/test splits, and repeated evaluations from being set up inconsistently. This makes results easier to compare with the BWGNN protocol.

graph-fraud — Skill for Claude Code by ConmuYan/my-academic-skills

Install

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.

Any agent

npx skills add ConmuYan/my-academic-skills --skill graph-fraud

Clone the repo

git clone --depth 1 https://github.com/ConmuYan/my-academic-skills

Made for: Claude Code.

Or install academic-skills, the plugin that ships this one along with the rest of its 3 skills.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/conmuyan/my-academic-skills/graph-fraud/github.svg)](https://agentmods.dev/skills/conmuyan/my-academic-skills/graph-fraud)

Your own site

<a href="https://agentmods.dev/skills/conmuyan/my-academic-skills/graph-fraud"><img src="https://agentmods.dev/badge/skills/conmuyan/my-academic-skills/graph-fraud/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/conmuyan/my-academic-skills/graph-fraud"><img src="https://agentmods.dev/badge/skills/conmuyan/my-academic-skills/graph-fraud.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 139 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,876 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00139	$0.01876
Opus 5.5	$0.00056	$0.00750
Sonnet 5.5	$0.00028	$0.00375
Haiku 4.5	$0.00014	$0.00188

Measured 2mo ago against content hash 355501a5ff5c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

graph-fraud 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 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/audit_gfd_protocol.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/graph-fraud/SKILL.md · 173 lines

How it starts

The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Graph fraud protocol (BWGNN)

Align the current repo to the field-default GFD setup from BWGNN (squareRoot3/Rethinking-Anomaly-Detection). Numbers and loaders live in references/bwgnn-protocol.md. Read it before editing.

random_state=2 is the split seed, not model init. --run 10 is 10 re-inits on that fixed split. Do not implement “10 different splits”.

When to use

Check dataset.py / data.py / configs against YelpChi, Amazon, T-Finance, T-Social
Add or fix abundant (40%) / scarce (1%, T-Social 0.01%) splits
Add BWGNN-style --run 10 mean±std
User says 对齐 BWGNN / GFD 通用设置 / 10 seeds

When not to use

Request	Do this instead
Align to GADBench precomputed `train_masks`	Follow GADBench; do not rewrite to `random_state=2`
CARE-GNN 40/60 no-val split	Only if the user names CARE-GNN
Invent a new dataset protocol	Stop; this skill only standardizes the four GFD datasets

Modes

User signal	Mode
检查 / audit / 是否符合	`audit`
对齐 / 改数据 / 配 10-run / 修 dataset.py	`fix` (default if they want the repo usable)

audit does not write files. fix patches the smallest data/train entry points and re-audits.

Workflow

1. Locate

Find, do not assume names:

Loaders: dataset.py, data.py, data_loader.py, DataHelper/
Entry: train.py, main.py, run_*.py
Configs: configs/*.yaml, config/*.yml
Data: YelpChi.mat, Amazon.mat, DGL binaries named tfinance / tsocial

Run scripts/audit_gfd_protocol.py from this skill directory (the folder that contains this SKILL.md):

python scripts/audit_gfd_protocol.py <repo-root>

Then read the hit files. The script only finds markers; you judge the code.

2. Audit (required in both modes)

Check every box against references/bwgnn-protocol.md. A failed box in fix mode is a patch target.

[ ] Amazon eligible starts at 3305; those nodes never enter train/val/test
[ ] Yelp / T-Finance / T-Social use all labeled nodes
[ ] T-Finance labels go through argmax(1) when loaded from DGL
[ ] T-Finance / T-Social use load_graphs, not .mat
[ ] Split is stratified train_test_split, random_state=2 both times
[ ] Remainder uses test_size=0.67 (val:test ≈ 1:2)
[ ] abundant train_ratio=0.4; scarce=0.01; T-Social scarce=0.0001
[ ] Split is computed once (or identically) and reused for all runs
[ ] --run N (default 1; protocol 10) rebuilds the model each time
[ ] run index is not passed to train_test_split
[ ] No --seed flag that changes the split
[ ] Comments cite BWGNN (Tang et al., ICML 2022)
[ ] Homo vs hetero is a model choice, not a second split
[ ] F1: val search linspace(0.05, 0.95, 19) unless the user keeps 0.5 and labels it

Read the full file on GitHub · 173 lines

Files

What ships with it

3 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.

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 · 173 lines · 139 tokens per session scan A 355501a5ff5c

Subscribe to this mod's changes

graph-fraud is a skill published in the GitHub repository ConmuYan/my-academic-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 139 tokens to every session and 1,876 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Data and AI PyTorch Scientific academic agent-skills agentskills skills-sh

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