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This report summarizes scanner evidence for EthicalML/awesome-production-machine-learning with 20,991 GitHub stars. SynthScan examined 1,248 lines of code across 8 source files, recording 20 pattern matches distributed across 3 syntactic categories. The legacy adjusted pattern signal is 43.3 (High 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.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 20 finding(s).
Are imports declared and package/API names verified? 0 finding(s).
Are wrappers, guards, and repeated structures necessary? 5 finding(s).
Which changes need focused human review? 20 finding(s).
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.
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.
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.
The scanner identified 20 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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | README.md | 23 | | [🔩 Model Storage Optimisation](#model-storage-optimisation) | [🏁 Model Training & Orchestration](#model-training-and-o | CODE |
| MEDIUM | README.md | 74 | Also check out the <a href="https://github.com/EthicalML/awesome-production-agentic-systems/">Awesome Productio | CODE |
| MEDIUM | README.md | 77 | <a href="https://github.com/EthicalML/awesome-production-agentic-systems/"><img src="images/list.jpg"></a> | CODE |
| MEDIUM | README.md | 268 | * [GoModel](https://github.com/ENTERPILOT/GoModel)           : | CODE |
| LOW⚡ | .github/scripts/test_check_currency.py | 134 | def test_repository_github_says_is_gone_is_reported(self): | CODE |
| LOW⚡ | .github/scripts/test_check_currency.py | 140 | def test_one_failure_then_an_answer_resolves(self): | CODE |
| LOW | .github/scripts/test_check_currency.py | 155 | def test_not_found_that_resolves_on_confirmation_is_dropped(self): | CODE |
| LOW | .github/scripts/test_check_currency.py | 168 | def test_a_query_wide_error_does_not_delete_the_whole_list(self): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | .github/scripts/check_currency.py | 84 | CODE |