Repository Analysis

Piebald-AI/claude-code-system-prompts

All parts of Claude Code's system prompt, 27 builtin tool descriptions, sub agent prompts (Plan/Explore/Task), utility prompts (CLAUDE.md, compact, statusline, magic docs, WebFetch, Bash cmd, security review, agent creation). Updated for each Claude Code version.

2.6 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of Piebald-AI/claude-code-system-prompts, a JavaScript project with 11,823 GitHub stars. SynthScan v2.0 examined 37,900 lines of code across 581 source files, recording 32 pattern matches distributed across 7 syntactic categories. The overall adjusted score of 2.6 places this repository in the Likely human-written 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).

2.6
Adjusted Score
2.6
Raw Score
100%
Time Factor
2026-07-14
Last Push
11.8K
Stars
JavaScript
Language
37.9K
Lines of Code
581
Files
32
Pattern Hits
2026-07-14
Scan Date
0.02
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

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.

No multi-scan history yet — run the scanner again to build trend data.

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 13MEDIUM 8LOW 11

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 32 distinct pattern matches across 7 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.

Magic Placeholder Names13 hits · 65 pts
SeverityFileLineSnippetContext
HIGHtools/updatePrompts.js20 console.error('Set it with: export ANTHROPIC_API_KEY=your-api-key');CODE
HIGH…em-prompts/data-managed-agents-reference-typescript.md29const client = new Anthropic({ apiKey: "your-api-key" });CODE
HIGHsystem-prompts/data-managed-agents-reference-ruby.md27client = Anthropic::Client.new(api_key: "your-api-key")CODE
HIGHsystem-prompts/data-claude-api-reference-typescript.md31const client = new Anthropic({ apiKey: "your-api-key" });CODE
HIGHsystem-prompts/data-managed-agents-reference-php.md27$client = new Client(apiKey: 'your-api-key');CODE
HIGHsystem-prompts/data-managed-agents-reference-curl.md13export ANTHROPIC_API_KEY="your-api-key"CODE
HIGHsystem-prompts/data-claude-api-reference-curl.md13export ANTHROPIC_API_KEY="your-api-key"CODE
HIGHsystem-prompts/data-managed-agents-reference-python.md29client = anthropic.Anthropic(api_key="your-api-key")CODE
HIGHsystem-prompts/data-managed-agents-reference-go.md33 option.WithAPIKey("your-api-key"),CODE
HIGHsystem-prompts/data-claude-api-reference-python.md25client = anthropic.Anthropic(api_key="your-api-key")CODE
HIGHsystem-prompts/data-claude-api-reference-java.md76 .apiKey("your-api-key")CODE
HIGHsystem-prompts/data-claude-api-reference-ruby.md25client = Anthropic::Client.new(api_key: "your-api-key")CODE
HIGHsystem-prompts/data-claude-api-reference-go.md29 option.WithAPIKey("your-api-key"),CODE
Modern AI Meta-Vocabulary6 hits · 16 pts
SeverityFileLineSnippetContext
MEDIUMsystem-prompts/data-tool-use-reference-typescript.md36// The tool runner handles the agentic loop and returns the final messageCOMMENT
MEDIUMsystem-prompts/skill-model-migration-guide.md923output_config={"effort": "high"}, # or "xhigh" for the hardest coding/agentic tasksCODE
MEDIUMsystem-prompts/data-tool-use-concepts.md90# Handle pause_turn in your agentic loopCOMMENT
MEDIUMsystem-prompts/data-claude-model-catalog.md18m.max_input_tokens # context window (int)CODE
MEDIUM…m-prompts/data-managed-agents-self-hosted-sandboxes.md134## Container orchestration (mid-level)COMMENT
MEDIUMsystem-prompts/data-tool-use-reference-python.md33# The tool runner handles the agentic loop automaticallyCOMMENT
Structural Annotation Overuse6 hits · 9 pts
SeverityFileLineSnippetContext
LOWsystem-prompts/skill-model-migration-guide.md37## Step 0: Confirm the migration scopeCOMMENT
LOWsystem-prompts/skill-model-migration-guide.md70## Step 1: Classify each fileCOMMENT
LOW…stem-prompts/system-prompt-skillify-current-session.md14### Step 1: Analyze the SessionCOMMENT
LOW…stem-prompts/system-prompt-skillify-current-session.md26### Step 2: Interview the UserCOMMENT
LOW…stem-prompts/system-prompt-skillify-current-session.md64### Step 3: Write the SKILL.mdCOMMENT
LOW…stem-prompts/system-prompt-skillify-current-session.md122### Step 4: Confirm and SaveCOMMENT
Excessive Try-Catch Wrapping3 hits · 5 pts
SeverityFileLineSnippetContext
MEDIUMsystem-prompts/data-tool-use-reference-python.md444 print(f"Error: {result.stderr}")CODE
LOWsystem-prompts/data-managed-agents-webhooks.md45 except Exception:CODE
MEDIUMsystem-prompts/data-managed-agents-webhooks.md39def webhook():CODE
Fake / Example Data2 hits · 2 pts
SeverityFileLineSnippetContext
LOWsystem-prompts/data-tool-use-reference-typescript.md537console.log(response.parsed_output!.name); // "Jane Doe"CODE
LOWsystem-prompts/data-tool-use-reference-python.md535print(contact.name) # "Jane Doe"CODE
Over-Commented Block1 hit · 1 pts
SeverityFileLineSnippetContext
LOWsystem-prompts/data-governed-github-cli-shim-header.md1<!--COMMENT
Hyper-Verbose Identifiers1 hit · 1 pts
SeverityFileLineSnippetContext
LOW…tem-prompts/data-managed-agents-events-and-steering.md154def connect_with_consolidation(client, session_id):CODE