MCP HubMCP Hub
Volver a habilidades

plagiarism-checker

bitwize-music-studio
Actualizado 2 days ago
3 vistas
209
37
209
Ver en GitHub
Otroai

Acerca de

Esta habilidad escanea letras de canciones en busca de posibles plagios extrayendo frases distintivas y verificándolas contra resultados de búsqueda web y conocimiento de modelos de lenguaje. Genera un informe estructurado de riesgos para detectar préstamos involuntarios antes del lanzamiento. Los desarrolladores deben utilizarla como herramienta de control de calidad al preparar música para su publicación.

Instalación rápida

Claude Code

Recomendado
Principal
npx skills add bitwize-music-studio/claude-ai-music-skills -a claude-code
Comando PluginAlternativo
/plugin add https://github.com/bitwize-music-studio/claude-ai-music-skills
Git CloneAlternativo
git clone https://github.com/bitwize-music-studio/claude-ai-music-skills.git ~/.claude/skills/plagiarism-checker

Copia y pega este comando en Claude Code para instalar esta habilidad

Documentación

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

Repositorio GitHub

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

Habilidades relacionadas

llamaguard

Otro

LlamaGuard es el modelo de Meta de 7-8B parámetros para moderar las entradas y salidas de LLM en seis categorías de seguridad como violencia y discurso de odio. Ofrece una precisión del 94-95% y puede implementarse usando vLLM, Hugging Face o Amazon SageMaker. Utiliza esta skill para integrar fácilmente filtrado de contenido y barreras de seguridad en tus aplicaciones de IA.

Ver habilidad

cost-optimization

Otro

Esta Skill de Claude ayuda a los desarrolladores a optimizar los costes en la nube mediante el ajuste de tamaño de recursos, estrategias de etiquetado y análisis de gastos. Proporciona un marco para reducir los gastos en la nube e implementar una gobernanza de costes en AWS, Azure y GCP. Úsala cuando necesites analizar los costes de infraestructura, ajustar el tamaño de los recursos o cumplir con restricciones presupuestarias.

Ver habilidad

quantizing-models-bitsandbytes

Otro

Esta habilidad cuantiza LLMs a precisión de 8 o 4 bits utilizando bitsandbytes, logrando una reducción de memoria del 50-75% con pérdida mínima de precisión. Es ideal para ejecutar modelos más grandes en memoria GPU limitada o para acelerar la inferencia, admitiendo formatos como INT8, NF4 y FP4. La habilidad se integra con HuggingFace Transformers y permite entrenamiento QLoRA y optimizadores de 8 bits.

Ver habilidad

dispatching-parallel-agents

Otro

Esta Skill de Claude despliega múltiples agentes para investigar y solucionar 3 o más problemas independientes de forma concurrente. Está diseñada para escenarios que involucran fallos no relacionados que pueden resolverse sin estado compartido o dependencias. Su capacidad principal es la resolución paralela de problemas, asignando un agente por cada dominio problemático independiente para maximizar la eficiencia.

Ver habilidad