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owainlewis/awesome-artificial-intelligence

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

10.7 Moderate pattern signal View on GitHub

Maintenance and Governance Review

This report summarizes scanner evidence for owainlewis/awesome-artificial-intelligence, a Python project with 16,641 GitHub stars. SynthScan examined 1,525 lines of code across 16 source files, recording 13 pattern matches distributed across 3 syntactic categories. The legacy adjusted pattern signal is 10.7 (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.

10.7
Adjusted Signal
10.7
Raw Pattern Score
100%
Time Factor
2026-08-15
Last Push
16.6K
Stars
Python
Language
1.5K
Lines of Code
16
Files
13
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 39.4/100 · MEDIUM

Can maintainers explain intent, assumptions, and trade-offs? 12 finding(s).

Hallucinated Dependency & Supply Chain Risk 3.2/100 · LOW

Are imports declared and package/API names verified? 1 finding(s).

Agent Over-Engineering Ratio 13.0/100 · LOW

Are wrappers, guards, and repeated structures necessary? 7 finding(s).

PR Review Velocity Impact 53.0/100 · HIGH

Which changes need focused human review? 13 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 5LOW 8

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 13 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.

Modern AI Meta-Vocabulary5 hits · 11 pts
SeverityFileLineSnippetContext
MEDIUMREADME.md40- [AI Engineer](https://aiengineer.co/): A paid program for agentic coding and building, testing, and shipping productioCODE
MEDIUMREADME.md68- [Building Effective Agents](https://www.anthropic.com/engineering/building-effective-agents): Anthropic's practical paCODE
MEDIUMREADME.md69- [A Practical Guide to Building Agents](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-buildCODE
MEDIUMREADME.md89- [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/): A small SDK for tools, handoffs, guardrails, tracCODE
MEDIUMREADME.md143### Software factories and agent orchestrationCOMMENT
Hyper-Verbose Identifiers7 hits · 4 pts
SeverityFileLineSnippetContext
LOWtests/test_validate_readme.py34 def test_duplicate_title_and_normalized_url(self):STRING
LOWtests/test_validate_readme.py48 def test_level_two_heading_resets_category(self):STRING
LOWtests/test_validate_readme.py53 def test_same_category_name_in_different_sections(self):STRING
LOWtests/test_validate_readme.py67 def test_description_needs_period(self):STRING
LOW⚡tests/test_validate_readme.py79 def test_invalid_url_is_an_error(self):STRING
LOW⚡tests/test_validate_readme.py85 def test_link_status_classification(self):STRING
LOW⚡tests/test_validate_readme.py94 def test_link_exception_classification(self):STRING
Unused Imports1 hit · 1 pts
SeverityFileLineSnippetContext
LOWscripts/validate_readme.py4CODE