resemble-detect
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 Resemble AI
npx skills add Prat011/awesome-llm-skills --skill resemble-detect --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Analyze audio, image, video, and text for synthetic manipulation, AI-generated content, watermarks, speaker identity, and media intelligence using the Resemble AI platform.
How it works
- It instructs to perform deepfake detection by submitting media to endpoints like POST /detect and then poll GET /detect/{uuid} for an asynchronous result labeled as either "completed" or "failed" with a corresponding score, label, and status. If a detection is completed, results include per-type metrics (audio, image, video) and may include fields such as
metrics,image_metrics, orvideo_metricswith alabelandscoreand additional detail likeaggregated_score,consistency, andheatmapURLs. - It supports combined analysis by using an
intelligenceflag in the same detect call to run multimodal analysis alongside detection. - It can perform Audio Source Tracing when
audio_source_tracingis set to true in POST /detect, yielding a label such aselevenlabsorresemble_aiwhen audio is detected as fake. - It can apply or detect watermarks via POST /watermark/apply and POST /watermark/detect, returning a
watermarked_mediaURL or{ "has_watermark": true, ... }responses depending on the endpoint. - It offers standalone Intelligence via POST /intelligence to extract structured fields such as
speaker_info,language,emotion,transcription,misinformation, and image-specific fields likescene_description,authenticity_analysis, andmisinformation. - It enables Identity verification (beta) with POST /identity to create profiles and POST /identity/search to compare unknown audio against known profiles, returning matches with
uuid,name,confidence, anddistance. - It includes a Text Detection module (beta) via POST /text_detect to determine whether text is AI-generated or human-written, with options to wait synchronously or poll asynchronously.
When to use it
Use this skill when the user needs: deepfake detection across media types, authenticity verification, provenance via source tracing, watermarking for provenance, speaker identity verification, or media intelligence (emotion, transcription, misinformation). It is not to be used for text-to-speech generation, voice cloning, or speech-to-text transcription, which are covered by separate capabilities.
What it can touch
- API endpoints: POST /detect, GET /detect/{uuid}, POST /watermark/apply, POST /watermark/detect, POST /intelligence, POST /audio_source_tracings (standalone), POST /identity, POST /identity/search, POST /text_detect, POST /detects/{detect_uuid}/intelligence
- Media URLs must be publicly accessible over HTTPS; local files must be hosted publicly first.
Caveats
- The system requires a valid Resemble API key and base URL https://app.resemble.ai/api/v2 with Authorization: Bearer <RESEMBLE_API_KEY>.
- Results must be completed (status: "completed") before making claims about authenticity; if status is "processing" or "failed", no autonomous judgment is allowed.
- Text and some features (Intelligence, Text Detection, Identity) are Beta features and may require special access or billing plans.
- The skill advises returning explicit scores and labels (e.g.,
labelandscore) as part of results and not making authenticity claims without a completed detection result.
# 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 prof
- Core Principle — THE IRON LAW
- When to Use
- Capability Decision Tree
- Required Setup
- MCP Tools Available
- Phase 1: Deepfake Detection
- Submit a Detection
- Poll for Results
- Reading Results by Media Type
- Interpreting Scores
- Phase 2: Intelligence — Media Analysis
- Standalone Intelligence
- Detect Intelligence — Ask Questions About Results
- Phase 3: Audio Source Tracing
What does the resemble-detect skill do?
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 Resemble AI
How do I install it?
Run `npx skills add Prat011/awesome-llm-skills --skill resemble-detect --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Prat011/awesome-llm-skills, a repository with 1,435 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
