A skill to stop your coding agent from burying the answer. ADHD-friendly output.
This report presents the forensic synthetic code analysis of ayghri/i-have-adhd, a Python project with 15,504 GitHub stars. SynthScan v2.0 examined 2,566 lines of code across 28 source files, recording 13 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 5.1 places this repository in the Low AI signal 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).
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.
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.
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.
The scanner identified 13 distinct pattern matches across 4 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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_run_evals.py | 16 | def test_case_catalog_is_valid_and_balanced(self): | CODE |
| LOW | tests/test_run_evals.py | 24 | def test_score_summary_applies_weights_and_release_gates(self): | CODE |
| LOW | tests/test_run_evals.py | 48 | def test_candidate_blocker_fails_release_gate(self): | CODE |
| LOW | tests/test_run_evals.py | 71 | def test_conditions_judged_on_different_cases_are_rejected(self): | CODE |
| LOW | tests/test_run_evals.py | 81 | def test_duplicate_score_rows_are_rejected(self): | CODE |
| LOW | tests/test_run_evals.py | 106 | def test_duplicate_case_ids_are_rejected(self): | CODE |
| LOW | tests/test_run_evals.py | 117 | def test_jsonl_loader_reports_invalid_rows(self): | CODE |
| LOW | tests/test_run_evals.py | 124 | def test_unmetered_runner_is_rejected_before_any_call(self): | CODE |
| LOW | tests/test_run_evals.py | 163 | def test_completed_keys_support_resuming_partial_runs(self): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | scripts/run_evals.py | 209 | CODE | |
| LOW | scripts/run_evals.py | 337 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | hooks/always-on.sh | 1 | #!/usr/bin/env sh | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | scripts/run_evals.py | 4 | CODE |