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plagiarism-checker

bitwize-music-studio
업데이트됨 2 days ago
5 조회
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기타ai

정보

이 스킬은 노래 가사에서 특징적인 구문을 추출하여 웹 검색 결과와 LLM 지식과 대조함으로써 표절 가능성을 검사합니다. 출시 전 의도치 않은 차용을 포착하기 위해 구조화된 위험 보고서를 생성합니다. 개발자는 음악을 출판 준비할 때 품질 검증 도구로 활용해야 합니다.

빠른 설치

Claude Code

추천
기본
npx skills add bitwize-music-studio/claude-ai-music-skills -a claude-code
플러그인 명령대체
/plugin add https://github.com/bitwize-music-studio/claude-ai-music-skills
Git 클론대체
git clone https://github.com/bitwize-music-studio/claude-ai-music-skills.git ~/.claude/skills/plagiarism-checker

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

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

GitHub 저장소

bitwize-music-studio/claude-ai-music-skills
경로: skills/plagiarism-checker
0
ai-musicai-music-toolsaudio-masteringclaudeclaude-codeclaude-code-plugin

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