What makes Plagiarism Checker notable?

959 downloads. Trust evidence available

How should Plagiarism Checker be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Plagiarism Checker

Detect text originality and AI-generated content. Skill: Plagiarism Checker Owner: openlark Summary: Detect text originality and AI-generated content. Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-15T02:02:46.794Z | user - Initial release of the plagiarism-checker skill. - Detects text originality and identifies AI-generated content. - Provides structured reports with originality scores, AI generation probability, and paragraph-level analysis. - Accepts input

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

959

Updated

Oct 12, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 959 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 959 downloadsadoption · observed Oct 12, 2026
Latest release: 1.0.0release · observed Apr 15, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.0release
- Initial release of the plagiarism-checker skill. - Detects text originality and identifies AI-generated content. - Provides structured reports with originality scores, AI generation probability, and paragraph-level analysis. - Accepts input via direct text or file uploads (.txt, .md, .docx). - Includes recommendation and rewriting suggestions for low-scoring content.
Observed Apr 15, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s1727wv2g20pc729snzcm4nf8183hy72:plagiarism-checker

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-openlark-plagiarism-checker/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

9,086 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: plagiarism-checker
description: Detect text originality and AI-generated content.
---

# Plagiarism Checker

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:

```bash
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:**
```powershell
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

_meta.json

{
  "ownerId": "kn75qrtb885pznwsjwsh18dvf1813bv0",
  "slug": "plagiarism-checker",
  "version": "1.0.0",
  "publishedAt": 1776218566794
}

references/detection-guide.md

# Detection Guide

## Detection Threshold Standards

| Originality | AI Probability | Risk Level | Recommendation |
|-------------|----------------|------------|----------------|
| ≥ 85% | < 20% | 🟢 Low Risk | Pass, suitable for submission |
| 60–84% | 20–50% | 🟡 Medium Risk | Recommend rewriting high-similarity paragraphs |
| 30–59% | 50–75% | 🟠 High Risk | Requires substantial rewriting or addition of original content |
| < 30% | > 75% | 🔴 Extremely High Risk | Severe plagiarism or nearly pure AI generation |

## Detection Methods

### 1. String Matching (SimHash / n-gram)
- Segment text into n-gram phrases (n=3~5)
- Compute SimHash fingerprints
- Compare against pre-built database, calculate Hamming distance

### 2. AI Generation Detection (Statistical Features)
- **Perplexity**: The difficulty level for a language model to predict the text
- **Burstiness**: Variance in sentence length; high burstiness = more human-like
- **Vocabulary Distribution**: AI tends to use high-frequency words; human vocabulary is more dispersed

### 3. Semantic Similarity (Sentence Transformers)
- Use `all-MiniLM-L6-v2` to compute sentence vectors
- Cosine similarity > 0.85 is considered highly similar

## Best Practices

1. Text must be **at least 100 words** for statistical significance
2. **Quoted portions** will be flagged; sources must be noted in the report
3. **Technical terminology** may produce false positives (universally used academic terms)
4. **Hybrid detection** is more accurate than any single method

references/report-template.md

# Detection Report Template

## Report Structure

```
═══════════════════════════════════════════════════════════
                Content Originality & AI Detection Report
═══════════════════════════════════════════════════════════

📝 Text Overview
  Word Count: {word_count}
  Character Count: {char_count}
  Paragraph Count: {para_count}

📊 Comprehensive Score
  Originality: {originality}%
  AI Generation Probability: {ai_prob}%
  Risk Level: {risk_emoji} {risk_level}

───────────────────────────────────────────────────────────
Paragraph Analysis
───────────────────────────────────────────────────────────
{paragraph_analysis}

───────────────────────────────────────────────────────────
Recommendations
───────────────────────────────────────────────────────────
{recommendations}

═══════════════════════════════════════════════════════════
              Report Generated: {timestamp}
═══════════════════════════════════════════════════════════
```

## Example Report

```
═══════════════════════════════════════════════════════════
                Content Originality & AI Detection Report
═══════════════════════════════════════════════════════════

📝 Text Overview
  Word Count: 486
  Character Count: 2341
  Paragraph Count: 4

📊 Comprehensive Score
  Originality: 78%
  AI Generation Probability: 35%
  Risk Level: 🟡 Medium Risk

───────────────────────────────────────────────────────────
Paragraph Analysis
───────────────────────────────────────────────────────────
  ✅ Paragraph 1 (0-120 words): Original content, no significant match
  ⚠️ Paragraph 2 (121-280 words): 42% similarity
     Match Source: Web/article-123 (38%), Blog/tech-post (4%)
  ⚠️ Paragraph 3 (281-390 words): 51% AI probability
     Features: Low burstiness, high-frequency vocabulary, mechanical sentence patterns
  ✅ Paragraph 4 (391-486 words): Original content, no significant match

───────────────────────────────────────────────────────────
Recommendations
───────────────────────────────────────────────────────────
  1. Paragraph 2: Recommend rewriting, add personal analysis and unique perspective
  2. Paragraph 3: Recommend enriching sentence variety, incorporating specific examples
  3. Overall: Add more original viewpoints and firsthand experience

═══════════════════════════════════════════════════════════
              Report Generated: 2026-04-13 11:10 CST
═══════════════════════════════════════════════════════════
```

## Rewriting Suggestion Template

For each high-risk paragraph, provide:
1. **Problem Description**: "This paragraph has X% similarity with a certain source"
2. **Rewriting Direction**: "Recommend re-articulating from the perspective of personal experience / cases / data"
3. **Example Phrasing**: Provide 2-3 rewriting examples

skill-card.md

## Description:

Detect text originality and AI-generated content.

This skill is ready for commercial/non-commercial use.

## Publisher:

[openlark](https://clawhub.ai/user/openlark)

### License/Terms of Use:

MIT-0

## Use Case:

External users, developers, and content reviewers use this skill to screen text or local files for originality, AI-generation probability, paragraph-level risk, and rewriting recommendations.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Originality and AI-generation results are heuristic and may be inaccurate.

Mitigation: Use results as screening guidance only, and require human review before making academic, legal, or disciplinary decisions.

Risk: The skill reads user-provided local files.

Mitigation: Provide only files intended for analysis and avoid submitting sensitive or unrelated content.

Risk: Some documented options and detection methods may be inaccurate or not implemented.

Mitigation: Confirm supported command-line options before use and disclose any unsupported workflow steps to the user.

## Reference(s):

- [Detection Guide](references/detection-guide.md)
- [Detection Report Template](references/report-template.md)

## Skill Output:

**Output Type(s):** [text, markdown, shell commands, guidance]

**Output Format:** [Markdown guidance with terminal report text and inline shell commands]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Originality score, AI-generation probability, paragraph-level flags, and recommendations are heuristic and should be reviewed by a person.]

## Skill Version(s):

1.0.0 (source: release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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