Plagiarism Checker

Detect text originality and AI-generated content.

Security Scan

The skill mostly implements local heuristic checks for plagiarism/AI text, but the documentation and references claim more advanced methods and features (e.g., SimHash, sentence transformers, .docx and URL DB support) that the included code does not implement — the mismatch could mislead users.

Purpose & Capability

Name/description (plagiarism & AI detection) align with the included script, which provides heuristic AI-probability and trigram-based similarity checks. However, the reference docs and SKILL.md describe heavier capabilities (SimHash, sentence-transformer semantic similarity, SimHash fingerprints, external DB comparisons) that are not present in the code. This is a capability/documentation mismatch (overpromising).

Instruction Scope

SKILL.md instructs use of the script and mentions reading .docx files and supplying a DB of URLs; the CLI implementation only reads local text files via Path.read_text and does not fetch URLs or handle .docx formats robustly. SKILL.md and references imply networked/model-backed checks, but the runtime instructions only run the local script — the docs may mislead users about what data is sent or required.

Install Mechanism

No install spec and no external downloads; the skill is instruction-only with a contained Python script that uses only the standard library — low install risk.

Credentials

The skill requests no environment variables, no credentials, and no config paths. The script runs locally on provided text/files only and does not access network resources.

Persistence & Privilege

always:false and the skill does not request persistent presence or modify other skills/configurations. It runs as a normal, user-invoked utility.

What to consider before installing

This skill runs a local Python script with only standard-library code and does not contact external services, but the docs overstate its capabilities. Before installing or relying on results: 1) Be aware results are heuristic and limited (trigram similarity + simple AI heuristics), not model-backed semantic matches; 2) The --db flag expects a local file of reference texts — it does not fetch URLs despite wording that suggests it might; 3) .docx support is claimed but the script reads files as plain text and may not handle binary .docx correctly; 4) Test the tool with known samples to validate accuracy before using for decisions; 5) If you need stronger detection (web-similarity, transformer embeddings, or legal-grade reports), ask the author for a version that actually integrates those components or use a vetted commercial service. If these mismatches are unacceptable, request clarification or an updated implementation that matches the documentation.

Detect whether text is plagiarized or AI-generated. Provides content originality analysis to help identify copied content and machine-generated text.

Use Cases

Use this skill when users mention "plagiarism check," "detect plagiarism," "check AI rate," "article originality," "text originality detection," "content originality degree," or request similarity/AI rate analysis for a piece of text.

Core Script

Use scripts/check_plagiarism.py to perform detection:

python scripts/check_plagiarism.py --text "Text content to detect"

Optional parameters:

• --text: Text to detect (required, or use --file)
• --file: Read text from file (.txt, .md, .docx)
• --output: Output report path (prints to terminal by default)

Windows Example:

python scripts/check_plagiarism.py --text "Your text here..."

Output Format

The script returns a structured report:

=== Originality Detection Report ===
Originality Score: 92%
AI Generation Probability: 12%

[Paragraph Analysis]
  ✓ Paragraph 1: Original content, no match
  ⚠ Paragraph 2: 47% similarity, rewriting recommended
  ⚠ Paragraph 3: 31% AI generation probability

References

• references/detection-guide.md — Detection methods, threshold standards, and best practices
• references/report-template.md — Report template and examples

Workflow

1. Collect Text: User pastes text or uploads a file
2. Execute Detection: Run check_plagiarism.py
3. Interpret Report: Provide recommendations based on originality score and AI probability
4. Provide Rewriting Suggestions: Offer optimization advice for low-scoring paragraphs