Repository Analysis

agentskills/agentskills

Specification and documentation for Agent Skills

14.0 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of agentskills/agentskills, a Python project with 23,022 GitHub stars. SynthScan v2.0 examined 2,214 lines of code across 26 source files, recording 29 pattern matches distributed across 5 syntactic categories. The overall adjusted score of 14.0 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).

14.0
Adjusted Score
14.0
Raw Score
100%
Time Factor
2026-07-10
Last Push
23.0K
Stars
Python
Language
2.2K
Lines of Code
26
Files
29
Pattern Hits
2026-07-14
Scan Date
0.08
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

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

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 0HIGH 2MEDIUM 0LOW 27

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 29 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.

Hyper-Verbose Identifiers17 hits · 10 pts
SeverityFileLineSnippetContext
LOWskills-ref/tests/test_parser.py35def test_unclosed_frontmatter():STRING
LOWskills-ref/tests/test_parser.py55def test_non_dict_frontmatter():STRING
LOWskills-ref/tests/test_parser.py128def test_find_skill_md_prefers_uppercase(tmp_path):STRING
LOWskills-ref/tests/test_parser.py139def test_find_skill_md_accepts_lowercase(tmp_path):CODE
LOWskills-ref/tests/test_parser.py150def test_find_skill_md_returns_none_when_missing(tmp_path):CODE
LOWskills-ref/tests/test_parser.py158def test_read_properties_with_lowercase_skill_md(tmp_path):CODE
LOWskills-ref/tests/test_parser.py173def test_read_with_allowed_tools(tmp_path):STRING
LOWskills-ref/tests/test_prompt.py55def test_special_characters_escaped(tmp_path):STRING
LOWskills-ref/tests/test_validator.py41def test_invalid_name_uppercase(tmp_path):STRING
LOWskills-ref/tests/test_validator.py81def test_name_consecutive_hyphens(tmp_path):STRING
LOWskills-ref/tests/test_validator.py94def test_name_invalid_characters(tmp_path):STRING
LOWskills-ref/tests/test_validator.py107def test_name_directory_mismatch(tmp_path):STRING
LOWskills-ref/tests/test_validator.py134def test_valid_with_all_fields(tmp_path):STRING
LOWskills-ref/tests/test_validator.py150def test_allowed_tools_accepted(tmp_path):STRING
LOWskills-ref/tests/test_validator.py221def test_description_too_long(tmp_path):STRING
LOWskills-ref/tests/test_validator.py251def test_compatibility_too_long(tmp_path):STRING
LOWskills-ref/src/skills_ref/validator.py104def _validate_metadata_fields(metadata: dict) -> list[str]:CODE
Docstring Block Structure2 hits · 10 pts
SeverityFileLineSnippetContext
HIGHskills-ref/src/skills_ref/parser.py31Parse YAML frontmatter from SKILL.md content. Args: content: Raw content of SKILL.md file Returns: STRING
HIGHskills-ref/src/skills_ref/parser.py68Read skill properties from SKILL.md frontmatter. This function parses the frontmatter and returns properties. ISTRING
Unused Imports8 hits · 8 pts
SeverityFileLineSnippetContext
LOWskills-ref/src/skills_ref/__init__.py3CODE
LOWskills-ref/src/skills_ref/__init__.py3CODE
LOWskills-ref/src/skills_ref/__init__.py3CODE
LOWskills-ref/src/skills_ref/__init__.py4CODE
LOWskills-ref/src/skills_ref/__init__.py5CODE
LOWskills-ref/src/skills_ref/__init__.py5CODE
LOWskills-ref/src/skills_ref/__init__.py6CODE
LOWskills-ref/src/skills_ref/__init__.py7CODE
Redundant / Tautological Comments1 hit · 2 pts
SeverityFileLineSnippetContext
LOW.claude/hooks/session-start.sh5# Check if Mintlify CLI is installedCOMMENT
Modern Structural Boilerplate1 hit · 1 pts
SeverityFileLineSnippetContext
LOWskills-ref/src/skills_ref/__init__.py9__all__ = [CODE