Repository Analysis

asyncapi/spec

The AsyncAPI specification allows you to create machine-readable definitions of your asynchronous APIs.

2.5 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of asyncapi/spec, a JavaScript project with 5,237 GitHub stars. SynthScan v2.0 examined 15,974 lines of code across 84 source files, recording 21 pattern matches distributed across 5 syntactic categories. The overall adjusted score of 2.5 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

2.5
Adjusted Score
2.5
Raw Score
100%
Time Factor
2026-07-11
Last Push
5.2K
Stars
JavaScript
Language
16.0K
Lines of Code
84
Files
21
Pattern Hits
2026-07-14
Scan Date
0.01
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 1HIGH 0MEDIUM 1LOW 19

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 21 distinct pattern matches across 5 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. 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 diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Structural Annotation Overuse17 hits · 26 pts
SeverityFileLineSnippetContext
LOWRELEASE_PROCESS.md65### Step 1 - kick off callCOMMENT
LOWRELEASE_PROCESS.md77### Step 2 - create a release issueCOMMENT
LOWRELEASE_PROCESS.md87### Step 3 - update release branchesCOMMENT
LOWRELEASE_PROCESS.md155### Step 4 - prepare announcement blog postCOMMENT
LOWRELEASE_PROCESS.md174### Step 5 - create pull requestsCOMMENT
LOWRELEASE_PROCESS.md188### Step 6 - bring updates into release branchCOMMENT
LOWRELEASE_PROCESS.md206### Step 7 - update announcement blog postCOMMENT
LOWRELEASE_PROCESS.md215### Step 8 - prepare release notesCOMMENT
LOWRELEASE_PROCESS.md229### Step 9 - notify people the release is comingCOMMENT
LOWRELEASE_PROCESS.md242### Step 10 - reviewsCOMMENT
LOWRELEASE_PROCESS.md247### Step 11 - release candidatesCOMMENT
LOWRELEASE_PROCESS.md263### Step 12 - Notify code owners of critical repositories about the pre-releasesCOMMENT
LOWRELEASE_PROCESS.md273### Step 13 - merge the release branchesCOMMENT
LOWRELEASE_PROCESS.md288### Step 14 - publish releasesCOMMENT
LOWRELEASE_PROCESS.md293### Step 15 - notify tool maintainersCOMMENT
LOWRELEASE_PROCESS.md333### Step 16 - notify the communityCOMMENT
LOWRELEASE_PROCESS.md342### Step 17 - improve the release processCOMMENT
Hallucination Indicators1 hit · 10 pts
SeverityFileLineSnippetContext
CRITICAL.github/workflows/automerge-for-humans-merging.yml40 const commitOpts = github.rest.pulls.listCommits.endpoint.merge({CODE
Modern AI Meta-Vocabulary1 hit · 2 pts
SeverityFileLineSnippetContext
MEDIUMspec/asyncapi.md127An application is any kind of computer program or a group of them. It MUST be a [sender](#definitionsSender), a [receiveCODE
Over-Commented Block1 hit · 1 pts
SeverityFileLineSnippetContext
LOW.markdownlint.yaml1# MD013/line-length - Line lengthCOMMENT
Hyper-Verbose Identifiers1 hit · 1 pts
SeverityFileLineSnippetContext
LOWscripts/validation/embedded-examples-validation.js12function extractCommentsAndExamples(content) {CODE