AI Slop Code Risk Monitor

sahat/hackathon-starter

Name: sahat/hackathon-starter — JavaScript Code Quality & Security Review | AI Slop Code Risk Monitor
Creator: AI Slop Code Risk Monitor

Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

A boilerplate for Node.js web applications

1.5 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for sahat/hackathon-starter, a JavaScript project with 35,260 GitHub stars. SynthScan examined 28,694 lines of code across 71 source files, recording 37 pattern matches distributed across 4 syntactic categories. The legacy adjusted pattern signal is 1.5 (Low pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

1.5

Adjusted Signal

1.5

Raw Pattern Score

100%

Time Factor

2026-10-30

Last Push

35.3K

Stars

JavaScript

Language

28.7K

Lines of Code

Files

Pattern Hits

2026-10-31

Scan Date

0.00

HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 3.0/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 14 finding(s).

Hallucinated Dependency & Supply Chain Risk 4.3/100 · LOW

Are imports declared and package/API names verified? 23 finding(s).

Agent Over-Engineering Ratio 2.1/100 · LOW

Are wrappers, guards, and repeated structures necessary? 11 finding(s).

PR Review Velocity Impact 6.6/100 · LOW

Which changes need focused human review? 37 finding(s).

What These Metrics Mean

Adjusted Signal: Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score: Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor: A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits: Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate: High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files: Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 0MEDIUM 1LOW 36

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 37 distinct pattern matches across 4 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Fake / Example Data23 hits · 25 pts

Severity	File	Line	Snippet	Context
LOW	test/user.test.js	217	body: { email: 'user@example.com', password: '' },	CODE
LOW	test/user.test.js	240	req.body.email = 'user@example.com';	CODE
LOW	test/user.test.js	252	req.body.email = 'user@example.com';	CODE
LOW	test/user.test.js	283	req.body.email = 'user@example.com';	CODE
LOW	test/user.test.js	296	req.body.email = 'user@example.com';	CODE
LOW	test/user.test.js	309	req.body.email = 'user@example.com';	CODE
LOW	test/user.test.js	321	req.body.email = 'user@example.com';	CODE
LOW⚡	test/passport.test.js	142	const fakeUser = new User({ email: 'user@example.com', password: 'hashedpass' });	CODE
LOW⚡	test/passport.test.js	145	invokeLocal('user@example.com', 'wrongpass', (err, user, info) => {	CODE
LOW⚡	test/passport.test.js	154	const fakeUser = new User({ email: 'user@example.com', password: 'correcthash' });	CODE
LOW⚡	test/passport.test.js	157	invokeLocal('user@example.com', 'correctpass', (err, user) => {	CODE
LOW	test/passport.test.js	173	const fakeUser = new User({ email: 'user@example.com', password: 'hash' });	CODE
LOW	test/passport.test.js	177	invokeLocal('user@example.com', 'pass', (err) => {	CODE
LOW	test/passport.test.js	779	name: 'John Doe',	CODE
LOW	test/passport.test.js	832	name: 'John Doe',	CODE
LOW	test/passport.test.js	861	name: 'John Doe',	CODE
LOW	test/passport.test.js	878	name: 'John Doe',	CODE
LOW	test/passport.test.js	918	name: 'John Doe',	CODE
LOW	test/passport.test.js	1171	email: 'user@example.com',	CODE
LOW	test/passport.test.js	1196	email: 'user@example.com',	CODE
LOW	test/passport.test.js	1221	email: 'user@example.com',	CODE
LOW	controllers/api.js	1046	recipientName = 'John Doe';	CODE
LOW	controllers/api.js	1050	address_line1: '123 Main Street',	CODE

Hyper-Verbose Identifiers11 hits · 12 pts

Severity	File	Line	Snippet	Context
LOW	test/tools/nock-server-fixtures.js	266	async function installNockReplayFixtures({ mode } = {}) {	CODE
LOW	test/e2e-nokey/github-api.e2e.test.js	8	async function gotoGithubWithRateLimitRetry(sharedPage, request) {	CODE
LOW⚡	models/User.js	85	userSchema.virtual('isPasswordResetExpired').get(function checkPasswordResetExpiration() {	CODE
LOW⚡	models/User.js	89	userSchema.virtual('isEmailVerificationExpired').get(function checkEmailVerificationExpiration() {	CODE
LOW⚡	models/User.js	93	userSchema.virtual('isLoginExpired').get(function checkLoginTokenExpiration() {	CODE
LOW	models/User.js	154	userSchema.pre('save', function updateGravatarOnEmailChange() {	CODE
LOW	controllers/ai.js	124	function createHuggingFaceEmbeddings({ apiKey, model, provider }) {	CODE
LOW	controllers/ai.js	175	function initializeEmbeddingCaches(mongoUri) {	CODE
LOW	controllers/ai.js	271	async function createCollectionForVectorSearch(db, collectionName, indexes) {	CODE
LOW	controllers/user.js	166	async function sendPasswordlessLoginLinkIfUserExists(user, req) {	CODE
LOW	controllers/user.js	202	async function sendPasswordlessSignupLink(user, req) {	CODE

Slop Phrases1 hit · 3 pts

Severity	File	Line	Snippet	Context
MEDIUM	app.js	38	* This is a basic rate limiting configuration. You may want to adjust the settings	COMMENT

Over-Commented Block2 hits · 2 pts

Severity	File	Line	Snippet	Context
LOW	models/User.js	261	return false;	COMMENT
LOW	public/css/main.scss	1	// Use Bootstrap 5.3's `data` color mode: dark mode is applied by setting	COMMENT