bitwize-music-studio/claude-ai-music-skills/plagiarism-checker

Claude Code and agent skill by bitwize-music-studio/claude-ai-music-skills

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Checking that the SKILL.md file is still in the repository.

Scans lyrics for phrases that may match existing songs using web search and LLM knowledge. Use before release to check for unintentional borrowing.

Install

npx vskill@latest install bitwize-music-studio/claude-ai-music-skills/plagiarism-checker

SKILL.md

Your Task

Target: $ARGUMENTS

  1. Get lyrics for the specified track(s)
  2. Extract distinctive phrases using MCP tool
  3. Web search top phrases for matches against known songs
  4. Use LLM knowledge to independently flag similarities
  5. Generate structured risk report

Plagiarism Checker

You scan lyrics for phrases that may unintentionally echo existing songs. This is a quality check, not a legal tool — it catches borrowing early so the writer can revise before release.


Workflow

Step 1: Get Lyrics
  • Use extract_section(album_slug, track_slug, "streaming") to get streaming lyrics (preferred — no phonetic spellings that confuse web searches)
  • If streaming lyrics empty, fall back to extract_section(album_slug, track_slug, "lyrics") for Suno lyrics
  • If raw text was provided instead of album/track reference, use that directly
Step 2: Extract Distinctive Phrases

Call extract_distinctive_phrases(text, max_phrases=15, include_raw_lines=False) MCP tool. This returns:

  • Distinctive 4-7 word n-grams ranked by section priority (top 15)
  • Pre-formatted search suggestions with quoted phrases + "lyrics"
  • Common cliches already filtered out
Step 3: Web Search
  • Search the top 10-15 search_suggestions returned by the tool using WebSearch
  • For short lyrics (<100 words), limit to 5-8 searches
  • Look for results that reference specific songs by title/artist
  • Skip results that are:
  • Lyrics aggregator sites listing hundreds of matches (too generic)
  • Dictionary/reference pages
  • The user's own published work
Step 4: Deep Compare

For any search result that names a specific song:

  1. WebFetch the lyrics page
  2. Compare the matching section against the user's lyrics
  3. Check if the match is:
  • Exact consecutive words (5+) — HIGH risk
  • Partial overlap (4 words) — MEDIUM risk
  • Thematic similarity only — LOW risk
Step 5: LLM Knowledge Check

Independently scan ALL lines of the lyrics (not just extracted phrases) using your training knowledge:

  • Flag any line that closely resembles a well-known song lyric
  • Include the suspected source song and artist
  • Note whether the similarity is in words, melody hook phrasing, or concept
Step 6: Generate Report

Risk Levels

LevelCriteriaAction
HIGH5+ consecutive matching words from a known song, especially chorus/hookRewrite the line immediately
MEDIUM4-word match from known song, or structural similarity flagged by LLMReview and consider rewording
LOWCommon phrasing overlap, likely coincidenceNote for awareness, no action needed

Output Format

PLAGIARISM CHECK REPORT
Album: [Album Name]
Track: [Track Title]
Date: [Scan Date]
PHRASES SEARCHED: [N]
WEB MATCHES FOUND: [N]
LLM FLAGS: [N]
FINDINGS:
------------------------------------------------------------------------
[HIGH] Line 12 (Chorus): "burning shadows fall tonight across the wire"
Match: "Shadows Fall Tonight" by [Artist] — 5 consecutive words match chorus
Source: [URL]
Recommendation: Rewrite this line to avoid direct overlap
[MEDIUM] Line 24 (Verse 2): "walking through the ruins of the empire"
Similarity: Resembles "Empire" by [Artist] — similar phrasing in bridge
Source: LLM knowledge
Recommendation: Consider rewording if concerned
[LOW] Line 8 (Verse 1): "the city sleeps beneath the stars"
Note: Generic night imagery, appears in many songs
Recommendation: No action needed
------------------------------------------------------------------------
SUMMARY:
HIGH risk findings: 1
MEDIUM risk findings: 1
LOW risk findings: 1
VERDICT: NEEDS REVIEW
1 high-risk match requires attention before release.
COMMON PHRASES FILTERED: [N] (not searched — too generic to flag)
Verdicts
VerdictCriteria
CLEARNo HIGH or MEDIUM findings
NEEDS REVIEWAny MEDIUM findings, or 1 HIGH finding
REWRITE REQUIRED2+ HIGH findings

Important Notes

  • This is not a legal tool. It catches likely borrowing, not copyright infringement. Only a lawyer can determine infringement.
  • Streaming lyrics preferred. Suno lyrics contain phonetic respellings (e.g., "Seh-KYOOR-ih-tee" for "security") that will produce garbage web search results.
  • Common cliches are pre-filtered. The MCP tool removes ~75 ubiquitous phrases ("break my heart", "falling in love", etc.) before returning results. These are too common to flag.
  • Web searches may fail. If WebSearch is unavailable or rate-limited, proceed with LLM knowledge check only and note the limitation in the report.
  • Not a pre-generation gate. This check is too slow (web searches) and too unreliable (search availability) to block generation. Run it before release, not before Suno.

Running for Full Album

When given an album slug without a specific track:

  1. List all tracks via list_tracks(album_slug)
  2. Run the check for each track with status "In Progress", "Generated", or "Final"
  3. Skip tracks with status "Not Started" or "Sources Pending"
  4. Aggregate findings into a single album-level report with per-track sections

Example Invocations

/plagiarism-checker dark-tide
/plagiarism-checker dark-tide 03-the-wire

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
59
Repository forks
13
7-day trend score
20

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 1 Apr 2026, 7 months ago.

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