AI Slop Code RIsk Monitor

zilong7728/Collect-IPTV

📡IPTV节目源收集,每4小时更新!!

5.4 Moderate pattern signal View on GitHub

Maintenance and Governance Review

This report summarizes scanner evidence for zilong7728/Collect-IPTV with 2,419 GitHub stars. SynthScan examined 3,781 lines of code across 40 source files, recording 19 pattern matches distributed across 3 syntactic categories. The legacy adjusted pattern signal is 5.4 (Moderate 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.

5.4
Adjusted Signal
5.4
Raw Pattern Score
100%
Time Factor
2026-10-11
Last Push
2.4K
Stars
—
Language
3.8K
Lines of Code
40
Files
19
Pattern Hits
2026-10-12
Scan Date
0.00
HC Hit Rate

Risk Review · v3

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

Cognitive Ownership Risk 16.4/100 · LOW

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

Hallucinated Dependency & Supply Chain Risk 0.0/100 · LOW

Are imports declared and package/API names verified? 0 finding(s).

Agent Over-Engineering Ratio 15.2/100 · LOW

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

PR Review Velocity Impact 26.2/100 · MEDIUM

Which changes need focused human review? 19 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 2LOW 17

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 19 distinct pattern matches across 3 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.

Deep Nesting8 hits · 8 pts
SeverityFileLineSnippetContext
LOW.github/workflows/iptv.py261CODE
LOW.github/workflows/iptv.py390CODE
LOW.github/workflows/iptv.py427CODE
LOW.github/workflows/iptv.py521CODE
LOW.github/workflows/iptv.py619CODE
LOW.github/workflows/iptv.py814CODE
LOW.github/workflows/iptv.py909CODE
LOW.github/workflows/iptv.py1009CODE
Excessive Try-Catch Wrapping5 hits · 7 pts
SeverityFileLineSnippetContext
MEDIUM.github/workflows/iptv.py271 print(f"Error: The file {file_path} was not found.")CODE
LOW.github/workflows/iptv.py488 except Exception:CODE
LOW.github/workflows/iptv.py855 except Exception:CODE
LOW.github/workflows/iptv.py904 except Exception:CODE
MEDIUM.github/workflows/iptv.py1023 print(f"Error: The file {file_path} was not found.")CODE
Hyper-Verbose Identifiers6 hits · 6 pts
SeverityFileLineSnippetContext
LOW.github/workflows/iptv.py317def strip_common_channel_suffixes(token: str) -> str:CODE
LOW.github/workflows/iptv.py427def collect_online_geo_tokens(geo_payload, province_channels: Dict[str, Set[str]]) -> Dict[str, Set[str]]:CODE
LOW.github/workflows/iptv.py619def _extract_channel_candidates(text: str) -> List[str]:STRING
LOW.github/workflows/iptv.py683def parse_group_title_from_extinf(extinf_line: str) -> Optional[str]:CODE
LOW.github/workflows/iptv.py698def infer_group_from_upstream_title(CODE
LOW.github/workflows/iptv.py727def deduplicate_candidate_entries(entries: Iterable[Dict[str, Any]]) -> List[Dict[str, Any]]:CODE