返回技能列表

plagiarism-checker

bitwize-music-studio
更新于 2 days ago
6 次查看
209
37
209
在 GitHub 上查看
其他ai

关于

This skill scans song lyrics for potential plagiarism by extracting distinctive phrases and checking them against web search results and LLM knowledge. It generates a structured risk report to catch unintentional borrowing before release. Developers should use it as a quality check tool when preparing music for publication.

快速安装

Claude Code

推荐
主要方式
npx skills add bitwize-music-studio/claude-ai-music-skills -a claude-code
插件命令备选方式
/plugin add https://github.com/bitwize-music-studio/claude-ai-music-skills
Git 克隆备选方式
git clone https://github.com/bitwize-music-studio/claude-ai-music-skills.git ~/.claude/skills/plagiarism-checker

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

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 仓库

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

相关推荐技能

llamaguard

其他

LlamaGuard是Meta推出的7-8B参数内容审核模型,专门用于过滤LLM的输入和输出内容。它能检测六大安全风险类别(暴力/仇恨、性内容、武器、违禁品、自残、犯罪计划),准确率达94-95%。开发者可通过HuggingFace、vLLM或Sagemaker快速部署,并能与NeMo Guardrails集成实现自动化安全防护。

查看技能

cost-optimization

其他

这个Claude Skill帮助开发者优化云成本,通过资源调整、标记策略和预留实例来降低AWS、Azure和GCP的开支。它适用于减少云支出、分析基础设施成本或实施成本治理策略的场景。关键功能包括提供成本可视化、资源规模调整指导和定价模型优化建议。

查看技能

quantizing-models-bitsandbytes

其他

这个Skill使用bitsandbytes库量化大语言模型,能在GPU内存有限时通过8位或4位量化减少50-75%内存占用,同时保持精度损失最小。它支持INT8、NF4、FP4等多种量化格式,可与HuggingFace Transformers无缝集成,适用于需要部署更大模型或加速推理的场景。还提供QLoRA训练和8位优化器支持,让开发者能轻松实现高效模型压缩。

查看技能

dispatching-parallel-agents

其他

该Skill用于并行处理3个以上无依赖关系的独立故障,可为每个问题域分派专属Claude代理同时执行调查修复。它通过并发处理多个独立问题显著提升故障排查效率,特别适用于测试文件、子系统等无共享状态的场景。

查看技能