MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Resemble-Detect

Resemble-Detect是一款data方向的AI技能,核心价值是Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resem,可用于解决开发者在data领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resem

Last verified on: 2026-05-30
mkdir -p ./skills/resemble-detect && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/resemble-detect/SKILL.md -o ./skills/resemble-detect/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# Resemble Detect — Deepfake Detection & Media Safety


Analyze audio, image, video, and text for synthetic manipulation, AI-generated content, watermarks, speaker identity, and media intelligence using the Resemble AI platform.


Core Principle — THE IRON LAW


**"NEVER DECLARE MEDIA AS REAL OR FAKE WITHOUT A COMPLETED DETECTION RESULT."**


Do not guess, infer, or speculate about media authenticity. Every authenticity claim must be backed by a completed Resemble detect job with a returned `label`, `score`, and `status: "completed"`. If the detection is still `processing`, wait. If it `failed`, say so — do not substitute your own judgment.


When to Use


Use this skill whenever the user's request involves any of these:


- Checking if audio, video, image, or text is AI-generated or manipulated

- Detecting deepfakes in any media format

- Verifying media authenticity or provenance

- Identifying which AI platform synthesized audio (source tracing)

- Applying or detecting watermarks on media

- Analyzing media for speaker info, emotion, transcription, or misinformation

- Asking natural-language questions about detection results

- Matching or verifying speaker identity against known voice profiles

- Detecting AI-generated or machine-written text

- Any mention of: "deepfake", "fake detection", "synthetic media", "voice verification", "watermark", "media forensics", "authenticity check", "source tracing", "is this real", "AI-written text", "text detection"


**Do NOT use** for text-to-speech generation, voice cloning, or speech-to-text transcription — those are separate Resemble capabilities.


Capability Decision Tree


| User wants to... | Use this | API endpoint |

|-------------------------------------------------------|---------------------------|---------------------------------------|

| Check if media is AI-generated / deepfake | **Deepfake Detection** | `POST /detect` |

| Know *which AI platform* made fake audio | **Audio Source Tracing** | `POST /detect` with flag |

| Get speaker info, emotion, transcription from media | **Intelligence** | `POST /intelligence` |

| Ask questions about a completed detection | **Detect Intelligence** | `POST /detects/{uuid}/intelligence` |

| Apply an invisible watermark to media | **Watermark Apply** | `POST /watermark/apply` |

| Check if media contains a watermark | **Watermark Detect** | `POST /watermark/detect` |

| Verify a speaker's identity against known profiles | **Identity Search** | `POST /identity/search` |

| Check if text is AI-generated | **Text Detection** | `POST /text_detect` |

| Create a voice identity profile for future matching | **Identity Create** | `POST /identity` |


When multiple capabilities apply (e.g., user wants deepfake detection AND intelligence), combine them in a single `POST /detect` call using the `intelligence: true` flag rather than making separate requests.


Required Setup


- **API Key**: Bearer token from the Resemble AI dashboard (set as `RESEMBLE_API_KEY`)

- **Base URL**: `https://app.resemble.ai/api/v2`

- **Auth Header**: `Authorization: Bearer <RESEMBLE_API_KEY>`

- **Media Requirement**: All media must be at a publicly accessible HTTPS URL


If the user provides a local file path instead of a URL, inform them the file must be hosted at a public HTTPS URL first. Do not attempt to upload local files to the API. (Exception: `POST /text_detect` accepts text content inline.)


MCP Tools Available


When the Resemble MCP server is connected, use these tools instead of raw API calls:


| Tool | Purpose |

|---------------------------|-----------

🎯 Best For

  • Developers scaffolding new projects
  • Prototype builders
  • Data analysts
  • Business intelligence teams
  • Claude users

💡 Use Cases

  • Bootstrapping React components
  • Creating API route handlers
  • Finding patterns in customer data
  • Creating automated dashboards

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Resemble-Detect to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Can I customize the generated output?

Yes — modify the skill's prompt instructions to match your project conventions and coding style.

Can this connect to my database directly?

Most data skills accept CSV or JSON input. Database connectors are listed in the Works With section.

How do I install Resemble-Detect?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/resemble-detect/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Using generated code without understanding

Understand what generated code does before shipping it to production.

Not validating data quality

AI analysis is only as good as your input data. Profile and clean data before analysis.

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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