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All media must be accessible via public HTTPS URLs — local file paths are not supported except for text detection.'\n---\n\n# Resemble Detect — Deepfake Detection & Media Safety\n\nAnalyze audio, image, video, and text for synthetic manipulation, AI-generated content, watermarks, speaker identity, and media intelligence using the Resemble AI platform.\n\n## Core Principle — THE IRON LAW\n\n**\"NEVER DECLARE MEDIA AS REAL OR FAKE WITHOUT A COMPLETED DETECTION RESULT.\"**\n\nDo 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.\n\n## When to Use\n\nUse this skill whenever the user's request involves any of these:\n\n- Checking if audio, video, image, or text is AI-generated or manipulated\n- Detecting deepfakes in any media format\n- Verifying media authenticity or provenance\n- Identifying which AI platform synthesized audio (source tracing)\n- Applying or detecting watermarks on media\n- Analyzing media for speaker info, emotion, transcription, or misinformation\n- Asking natural-language questions about detection results\n- Matching or verifying speaker identity against known voice profiles\n- Detecting AI-generated or machine-written text\n- 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\"\n\n**Do NOT use** for text-to-speech generation, voice cloning, or speech-to-text transcription — those are separate Resemble capabilities.\n\n## Capability Decision Tree\n\n| User wants to...                                      | Use this                  | API endpoint                          |\n|-------------------------------------------------------|---------------------------|---------------------------------------|\n| Check if media is AI-generated \u002F deepfake             | **Deepfake Detection**    | `POST \u002Fdetect`                        |\n| Know *which AI platform* made fake audio              | **Audio Source Tracing**  | `POST \u002Fdetect` with flag              |\n| Get speaker info, emotion, transcription from media   | **Intelligence**          | `POST \u002Fintelligence`                  |\n| Ask questions about a completed detection             | **Detect Intelligence**   | `POST \u002Fdetects\u002F{uuid}\u002Fintelligence`   |\n| Apply an invisible watermark to media                 | **Watermark Apply**       | `POST \u002Fwatermark\u002Fapply`               |\n| Check if media contains a watermark                   | **Watermark Detect**      | `POST \u002Fwatermark\u002Fdetect`              |\n| Verify a speaker's identity against known profiles    | **Identity Search**       | `POST \u002Fidentity\u002Fsearch`               |\n| Check if text is AI-generated                         | **Text Detection**        | `POST \u002Ftext_detect`                   |\n| Create a voice identity profile for future matching   | **Identity Create**       | `POST \u002Fidentity`                      |\n\nWhen multiple capabilities apply (e.g., user wants deepfake detection AND intelligence), combine them in a single `POST \u002Fdetect` call using the `intelligence: true` flag rather than making separate requests.\n\n## Required Setup\n\n- **API Key**: Bearer token from the Resemble AI dashboard (set as `RESEMBLE_API_KEY`)\n- **Base URL**: `https:\u002F\u002Fapp.resemble.ai\u002Fapi\u002Fv2`\n- **Auth Header**: `Authorization: Bearer \u003CRESEMBLE_API_KEY>`\n- **Media Requirement**: All media must be at a publicly accessible HTTPS URL\n\nIf 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 \u002Ftext_detect` accepts text content inline.)\n\n## MCP Tools Available\n\nWhen the Resemble MCP server is connected, use these tools instead of raw API calls:\n\n| Tool                      | Purpose                                           |\n|---------------------------|---------------------------------------------------|\n| `resemble_docs_lookup`    | Get comprehensive docs for any detect sub-topic   |\n| `resemble_search`         | Search across all documentation                   |\n| `resemble_api_endpoint`   | Get exact OpenAPI spec for any endpoint           |\n| `resemble_api_search`     | Find endpoints by keyword                         |\n| `resemble_get_page`       | Read specific documentation pages                 |\n| `resemble_list_topics`    | List all available topics                         |\n\n**Tool usage pattern**: Use `resemble_docs_lookup` with topic `\"detect\"` to get the full picture, then `resemble_api_endpoint` for exact request\u002Fresponse schemas before making API calls.\n\n## Full API Reference\n\nDetailed request\u002Fresponse schemas for every endpoint are in **[references\u002Fapi-reference.md](references\u002Fapi-reference.md)**. Consult it before making any API call to verify exact parameter names and response shapes. The sections below cover decision-making; the reference covers exact field formats.\n\n---\n\n## Phase 1: Deepfake Detection\n\nThe core capability. Submit audio, image, or video for AI-generated content analysis via `POST \u002Fdetect`.\n\n**Key flags to consider:**\n- `visualize: true` — generate heatmap\u002Fvisualization artifacts\n- `intelligence: true` — run multimodal intelligence alongside detection (saves a round-trip)\n- `audio_source_tracing: true` — identify which AI platform synthesized fake audio (only fires on `\"fake\"` audio)\n- `use_reverse_search: true` — enable reverse image search (image only)\n- `zero_retention_mode: true` — auto-delete media after analysis (for sensitive content)\n\nDetection is asynchronous. Poll `GET \u002Fdetect\u002F{uuid}` at 2s → 5s → 10s intervals until `status` is `\"completed\"` or `\"failed\"`. Most complete in 10–60 seconds.\n\n**Supported formats:** Audio (WAV, MP3, OGG, M4A, FLAC) · Video (MP4, MOV, AVI, WMV) · Image (JPG, PNG, GIF, WEBP)\n\n### Reading Results\n\n- **Audio** — verdict in `metrics` — use `label` and `aggregated_score`\n- **Image** — verdict in `image_metrics` — use `label` and `score`; `ifl` has an Invisible Frequency Layer heatmap\n- **Video** — verdict in `video_metrics` — hierarchical tree of frame\u002Fsegment results; video-with-audio returns both `metrics` and `video_metrics`\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#reading-results-by-media-type) for full response schemas.\n\n### Interpreting Scores\n\n| Score Range | Interpretation                                      |\n|-------------|-----------------------------------------------------|\n| 0.0 – 0.3  | Strong indication of authentic\u002Freal media            |\n| 0.3 – 0.5  | Inconclusive — recommend additional analysis         |\n| 0.5 – 0.7  | Likely synthetic — flag for review                   |\n| 0.7 – 1.0  | High confidence synthetic\u002FAI-generated               |\n\n**Always present scores with context.** Say \"The detection returned a score of 0.87, indicating high confidence that this audio is AI-generated\" — never just \"it's fake.\"\n\n---\n\n## Phase 2: Intelligence — Media Analysis\n\nRich structured insights about media: speaker info, emotion, transcription, translation, misinformation, abnormalities.\n\nTwo ways to run Intelligence:\n1. **Combined with detection** — add `intelligence: true` to `POST \u002Fdetect` (preferred; one call)\n2. **Standalone** — `POST \u002Fintelligence` with a URL (when you only need analysis, not a deepfake verdict)\n\n**Audio\u002Fvideo structured fields include:** `speaker_info`, `language`, `dialect`, `emotion`, `speaking_style`, `context`, `message`, `abnormalities`, `transcription`, `translation`, `misinformation`.\n\n**Image structured fields include:** `scene_description`, `subjects`, `authenticity_analysis`, `context_and_setting`, `abnormalities`, `misinformation`.\n\n### Detect Intelligence — Ask Questions About Results\n\nAfter a detection completes, ask natural-language questions via `POST \u002Fdetects\u002F{detect_uuid}\u002Fintelligence` with `{ \"query\": \"...\" }`. Returns a question UUID — poll `GET \u002Fdetects\u002F{detect_uuid}\u002Fintelligence\u002F{question_uuid}` until `completed`.\n\n**Good questions to suggest:**\n- \"Summarize the detection results in plain language\"\n- \"What specific indicators suggest this is AI-generated?\"\n- \"How do the audio and video detection results differ?\"\n- \"What is the confidence level and what does it mean?\"\n- \"Are there any inconsistencies in the analysis?\"\n\n**Prerequisite:** The detection must have `status: \"completed\"`. Submitting a question against a processing or failed detection returns 422.\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#intelligence) for full parameters.\n\n---\n\n## Phase 3: Audio Source Tracing\n\nWhen audio is labeled `\"fake\"`, identify which AI platform generated it.\n\n**Enable it** by setting `audio_source_tracing: true` in the `POST \u002Fdetect` request. Result appears in the detection response under `audio_source_tracing.label`.\n\nKnown labels: `resemble_ai`, `elevenlabs`, `real`, and others as the model expands.\n\n**Important:** Source tracing only runs on audio labeled `\"fake\"`. Real audio produces no source tracing result.\n\nStandalone queries: `GET \u002Faudio_source_tracings` and `GET \u002Faudio_source_tracings\u002F{uuid}`.\n\n---\n\n## Phase 4: Watermarking\n\nApply invisible watermarks to media for provenance tracking, or detect existing watermarks.\n\n- **Apply**: `POST \u002Fwatermark\u002Fapply` with `url`, optional `strength` (0.0–1.0), optional `custom_message`. Add `Prefer: wait` for synchronous response, or poll `GET \u002Fwatermark\u002Fapply\u002F{uuid}\u002Fresult`. Response includes `watermarked_media` URL.\n- **Detect**: `POST \u002Fwatermark\u002Fdetect` with `url`. Audio returns `{ has_watermark, confidence }`; image\u002Fvideo returns `{ has_watermark }`.\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#watermarking) for exact parameter rules.\n\n---\n\n## Phase 5: Identity — Speaker Verification (Beta)\n\nCreate voice identity profiles and match incoming audio against them.\n\n> **Beta feature** — requires joining the preview program. Inform the user if they encounter access errors.\n\n- **Create profile**: `POST \u002Fidentity` with `{ audio_url, name }`\n- **Search**: `POST \u002Fidentity\u002Fsearch` with `{ audio_url, top_k }`\n\nResponse returns ranked matches with `confidence` (higher = stronger) and `distance` (lower = closer match).\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#identity--speaker-verification-beta) for full schemas.\n\n---\n\n## Phase 6: Text Detection\n\nDetect whether text content is AI-generated or human-written via `POST \u002Ftext_detect`.\n\n> **Beta feature** — requires the `detect_beta_user` role or a billing plan that includes the `dfd_text` product.\n\n**Key parameters:**\n- `text` (required, max 100,000 chars)\n- `threshold` (default 0.5)\n- `privacy_mode: true` — text content not stored after analysis\n- `callback_url` — async notification webhook\n\nAdd `Prefer: wait` for synchronous response, or poll `GET \u002Ftext_detect\u002F{uuid}`. Response includes `prediction` (`\"ai\"` or `\"human\"`) and `confidence` (0.0–1.0).\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#text-detection) for full schema and callback format.\n\n---\n\n## Recommended Workflows\n\n### Full Media Forensics (Most Thorough)\n\nFor a comprehensive analysis, combine all capabilities:\n\n1. Submit detection with all flags enabled:\n   ```json\n   {\n     \"url\": \"https:\u002F\u002Fexample.com\u002Fsuspect.mp4\",\n     \"visualize\": true,\n     \"intelligence\": true,\n     \"audio_source_tracing\": true,\n     \"use_reverse_search\": true\n   }\n   ```\n2. Poll until `status: \"completed\"`\n3. Read `metrics` \u002F `image_metrics` \u002F `video_metrics` for the verdict\n4. Read `intelligence.description` for structured media analysis\n5. If audio labeled `\"fake\"`, check `audio_source_tracing.label` for the source platform\n6. Ask follow-up questions via Detect Intelligence if anything needs clarification\n7. Check for watermarks via `POST \u002Fwatermark\u002Fdetect` if provenance is relevant\n\n### Quick Authenticity Check (Fastest)\n\n1. Submit minimal detection: `{ \"url\": \"...\" }`\n2. Poll until complete\n3. Check `label` and `aggregated_score` (audio) or `label` and `score` (image\u002Fvideo)\n4. Report result with score context\n\n### Provenance Pipeline (Content Creators)\n\n1. Apply watermark to original content: `POST \u002Fwatermark\u002Fapply`\n2. Distribute watermarked media\n3. Later, verify provenance: `POST \u002Fwatermark\u002Fdetect` against any copy\n\n---\n\n## Red Flags — Stop and Reassess\n\n- **Declaring authenticity without a detection result** — Never say media is real or fake based on visual\u002Fauditory inspection alone\n- **Ignoring the score and reporting only the label** — A `\"fake\"` label with score 0.51 means something very different from score 0.95\n- **Submitting local file paths to the API** — The API requires publicly accessible HTTPS URLs (does not apply to text detection)\n- **Sending text longer than 100,000 characters to text detection** — Split into chunks or inform the user of the limit\n- **Polling too aggressively** — Start at 2s intervals, back off exponentially; do not loop at \u003C1s\n- **Asking Detect Intelligence questions before detection completes** — Results in 422 error\n- **Expecting source tracing on \"real\" audio** — Source tracing only runs on audio labeled `\"fake\"`\n- **Treating beta features (Identity, Text Detection) as production-ready** — Warn users about beta status\n- **Ignoring `zero_retention_mode` for sensitive media** — Always suggest this flag when the user indicates the media is sensitive or private\n- **Making multiple separate API calls when flags can combine** — Use `intelligence: true` and `audio_source_tracing: true` on the detection call instead of separate requests\n\n## Response Presentation Guidelines\n\nWhen presenting results to users:\n\n1. **Lead with the verdict** — \"The detection indicates this audio is likely AI-generated (score: 0.87)\"\n2. **Provide score context** — Use the score interpretation table above\n3. **Mention limitations** — Detection is probabilistic, not absolute proof\n4. **Include actionable next steps** — Suggest intelligence queries, source tracing, or watermark checks as appropriate\n5. **For inconclusive results (0.3–0.5)** — Explicitly state the result is inconclusive and recommend additional analysis with different parameters or manual review\n6. **Never present detection as legal evidence** — Detection results are analytical tools, not forensic certifications\n\n## Error Handling\n\n| Error     | Cause                                      | Resolution                                      |\n|-----------|--------------------------------------------|-------------------------------------------------|\n| 400       | Invalid request body or missing `url`      | Check required parameters                       |\n| 401       | Invalid or missing API key                 | Verify `RESEMBLE_API_KEY`                       |\n| 404       | Detection UUID not found                   | Verify the UUID from the creation response     |\n| 422       | Detection not completed (for Intelligence) | Wait for detection to reach `completed` status |\n| 429       | Rate limited                               | Back off and retry with exponential delay       |\n| 500       | Server error                               | Retry once, then report to user                  |\n\n## Privacy & Compliance Notes\n\n- **Zero retention mode**: Set `zero_retention_mode: true` to auto-delete media after analysis. The URL is redacted and `media_deleted` is set to true post-completion.\n- **Text privacy mode**: Set `privacy_mode: true` on text detection to prevent text content from being stored after analysis.\n- **Data handling**: Media URLs and text content are stored by default. For GDPR\u002Fcompliance-sensitive workflows, enable zero retention (media) or privacy mode (text).\n- **Callback security**: If using `callback_url`, ensure the endpoint is HTTPS and authenticated on the receiving end.\n","# Resemble Detect — Deepfake Detection & Media Safety\n\nAnalyze audio, image, video, and text for synthetic manipulation, AI-generated content, watermarks, speaker identity, and media intelligence using the Resemble AI platform.\n\n## Core Principle — THE IRON LAW\n\n**\"NEVER DECLARE MEDIA AS REAL OR FAKE WITHOUT A COMPLETED DETECTION RESULT.\"**\n\nDo 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.\n\n## When to Use\n\nUse this skill whenever the user's request involves any of these:\n\n- Checking if audio, video, image, or text is AI-generated or manipulated\n- Detecting deepfakes in any media format\n- Verifying media authenticity or provenance\n- Identifying which AI platform synthesized audio (source tracing)\n- Applying or detecting watermarks on media\n- Analyzing media for speaker info, emotion, transcription, or misinformation\n- Asking natural-language questions about detection results\n- Matching or verifying speaker identity against known voice profiles\n- Detecting AI-generated or machine-written text\n- 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\"\n\n**Do NOT use** for text-to-speech generation, voice cloning, or speech-to-text transcription — those are separate Resemble capabilities.\n\n## Capability Decision Tree\n\n| User wants to...                                      | Use this                  | API endpoint                          |\n|-------------------------------------------------------|---------------------------|---------------------------------------|\n| Check if media is AI-generated \u002F deepfake             | **Deepfake Detection**    | `POST \u002Fdetect`                        |\n| Know *which AI platform* made fake audio              | **Audio Source Tracing**  | `POST \u002Fdetect` with flag              |\n| Get speaker info, emotion, transcription from media   | **Intelligence**          | `POST \u002Fintelligence`                  |\n| Ask questions about a completed detection             | **Detect Intelligence**   | `POST \u002Fdetects\u002F{uuid}\u002Fintelligence`   |\n| Apply an invisible watermark to media                 | **Watermark Apply**       | `POST \u002Fwatermark\u002Fapply`               |\n| Check if media contains a watermark                   | **Watermark Detect**      | `POST \u002Fwatermark\u002Fdetect`              |\n| Verify a speaker's identity against known profiles    | **Identity Search**       | `POST \u002Fidentity\u002Fsearch`               |\n| Check if text is AI-generated                         | **Text Detection**        | `POST \u002Ftext_detect`                   |\n| Create a voice identity profile for future matching   | **Identity Create**       | `POST \u002Fidentity`                      |\n\nWhen multiple capabilities apply (e.g., user wants deepfake detection AND intelligence), combine them in a single `POST \u002Fdetect` call using the `intelligence: true` flag rather than making separate requests.\n\n## Required Setup\n\n- **API Key**: Bearer token from the Resemble AI dashboard (set as `RESEMBLE_API_KEY`)\n- **Base URL**: `https:\u002F\u002Fapp.resemble.ai\u002Fapi\u002Fv2`\n- **Auth Header**: `Authorization: Bearer \u003CRESEMBLE_API_KEY>`\n- **Media Requirement**: All media must be at a publicly accessible HTTPS URL\n\nIf 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 \u002Ftext_detect` accepts text content inline.)\n\n## MCP Tools Available\n\nWhen the Resemble MCP server is connected, use these tools instead of raw API calls:\n\n| Tool                      | Purpose                                           |\n|---------------------------|---------------------------------------------------|\n| `resemble_docs_lookup`    | Get comprehensive docs for any detect sub-topic   |\n| `resemble_search`         | Search across all documentation                   |\n| `resemble_api_endpoint`   | Get exact OpenAPI spec for any endpoint           |\n| `resemble_api_search`     | Find endpoints by keyword                         |\n| `resemble_get_page`       | Read specific documentation pages                 |\n| `resemble_list_topics`    | List all available topics                         |\n\n**Tool usage pattern**: Use `resemble_docs_lookup` with topic `\"detect\"` to get the full picture, then `resemble_api_endpoint` for exact request\u002Fresponse schemas before making API calls.\n\n## Full API Reference\n\nDetailed request\u002Fresponse schemas for every endpoint are in **[references\u002Fapi-reference.md](references\u002Fapi-reference.md)**. Consult it before making any API call to verify exact parameter names and response shapes. The sections below cover decision-making; the reference covers exact field formats.\n\n---\n\n## Phase 1: Deepfake Detection\n\nThe core capability. Submit audio, image, or video for AI-generated content analysis via `POST \u002Fdetect`.\n\n**Key flags to consider:**\n- `visualize: true` — generate heatmap\u002Fvisualization artifacts\n- `intelligence: true` — run multimodal intelligence alongside detection (saves a round-trip)\n- `audio_source_tracing: true` — identify which AI platform synthesized fake audio (only fires on `\"fake\"` audio)\n- `use_reverse_search: true` — enable reverse image search (image only)\n- `zero_retention_mode: true` — auto-delete media after analysis (for sensitive content)\n\nDetection is asynchronous. Poll `GET \u002Fdetect\u002F{uuid}` at 2s → 5s → 10s intervals until `status` is `\"completed\"` or `\"failed\"`. Most complete in 10–60 seconds.\n\n**Supported formats:** Audio (WAV, MP3, OGG, M4A, FLAC) · Video (MP4, MOV, AVI, WMV) · Image (JPG, PNG, GIF, WEBP)\n\n### Reading Results\n\n- **Audio** — verdict in `metrics` — use `label` and `aggregated_score`\n- **Image** — verdict in `image_metrics` — use `label` and `score`; `ifl` has an Invisible Frequency Layer heatmap\n- **Video** — verdict in `video_metrics` — hierarchical tree of frame\u002Fsegment results; video-with-audio returns both `metrics` and `video_metrics`\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#reading-results-by-media-type) for full response schemas.\n\n### Interpreting Scores\n\n| Score Range | Interpretation                                      |\n|-------------|-----------------------------------------------------|\n| 0.0 – 0.3  | Strong indication of authentic\u002Freal media            |\n| 0.3 – 0.5  | Inconclusive — recommend additional analysis         |\n| 0.5 – 0.7  | Likely synthetic — flag for review                   |\n| 0.7 – 1.0  | High confidence synthetic\u002FAI-generated               |\n\n**Always present scores with context.** Say \"The detection returned a score of 0.87, indicating high confidence that this audio is AI-generated\" — never just \"it's fake.\"\n\n---\n\n## Phase 2: Intelligence — Media Analysis\n\nRich structured insights about media: speaker info, emotion, transcription, translation, misinformation, abnormalities.\n\nTwo ways to run Intelligence:\n1. **Combined with detection** — add `intelligence: true` to `POST \u002Fdetect` (preferred; one call)\n2. **Standalone** — `POST \u002Fintelligence` with a URL (when you only need analysis, not a deepfake verdict)\n\n**Audio\u002Fvideo structured fields include:** `speaker_info`, `language`, `dialect`, `emotion`, `speaking_style`, `context`, `message`, `abnormalities`, `transcription`, `translation`, `misinformation`.\n\n**Image structured fields include:** `scene_description`, `subjects`, `authenticity_analysis`, `context_and_setting`, `abnormalities`, `misinformation`.\n\n### Detect Intelligence — Ask Questions About Results\n\nAfter a detection completes, ask natural-language questions via `POST \u002Fdetects\u002F{detect_uuid}\u002Fintelligence` with `{ \"query\": \"...\" }`. Returns a question UUID — poll `GET \u002Fdetects\u002F{detect_uuid}\u002Fintelligence\u002F{question_uuid}` until `completed`.\n\n**Good questions to suggest:**\n- \"Summarize the detection results in plain language\"\n- \"What specific indicators suggest this is AI-generated?\"\n- \"How do the audio and video detection results differ?\"\n- \"What is the confidence level and what does it mean?\"\n- \"Are there any inconsistencies in the analysis?\"\n\n**Prerequisite:** The detection must have `status: \"completed\"`. Submitting a question against a processing or failed detection returns 422.\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#intelligence) for full parameters.\n\n---\n\n## Phase 3: Audio Source Tracing\n\nWhen audio is labeled `\"fake\"`, identify which AI platform generated it.\n\n**Enable it** by setting `audio_source_tracing: true` in the `POST \u002Fdetect` request. Result appears in the detection response under `audio_source_tracing.label`.\n\nKnown labels: `resemble_ai`, `elevenlabs`, `real`, and others as the model expands.\n\n**Important:** Source tracing only runs on audio labeled `\"fake\"`. Real audio produces no source tracing result.\n\nStandalone queries: `GET \u002Faudio_source_tracings` and `GET \u002Faudio_source_tracings\u002F{uuid}`.\n\n---\n\n## Phase 4: Watermarking\n\nApply invisible watermarks to media for provenance tracking, or detect existing watermarks.\n\n- **Apply**: `POST \u002Fwatermark\u002Fapply` with `url`, optional `strength` (0.0–1.0), optional `custom_message`. Add `Prefer: wait` for synchronous response, or poll `GET \u002Fwatermark\u002Fapply\u002F{uuid}\u002Fresult`. Response includes `watermarked_media` URL.\n- **Detect**: `POST \u002Fwatermark\u002Fdetect` with `url`. Audio returns `{ has_watermark, confidence }`; image\u002Fvideo returns `{ has_watermark }`.\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#watermarking) for exact parameter rules.\n\n---\n\n## Phase 5: Identity — Speaker Verification (Beta)\n\nCreate voice identity profiles and match incoming audio against them.\n\n> **Beta feature** — requires joining the preview program. Inform the user if they encounter access errors.\n\n- **Create profile**: `POST \u002Fidentity` with `{ audio_url, name }`\n- **Search**: `POST \u002Fidentity\u002Fsearch` with `{ audio_url, top_k }`\n\nResponse returns ranked matches with `confidence` (higher = stronger) and `distance` (lower = closer match).\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#identity--speaker-verification-beta) for full schemas.\n\n---\n\n## Phase 6: Text Detection\n\nDetect whether text content is AI-generated or human-written via `POST \u002Ftext_detect`.\n\n> **Beta feature** — requires the `detect_beta_user` role or a billing plan that includes the `dfd_text` product.\n\n**Key parameters:**\n- `text` (required, max 100,000 chars)\n- `threshold` (default 0.5)\n- `privacy_mode: true` — text content not stored after analysis\n- `callback_url` — async notification webhook\n\nAdd `Prefer: wait` for synchronous response, or poll `GET \u002Ftext_detect\u002F{uuid}`. Response includes `prediction` (`\"ai\"` or `\"human\"`) and `confidence` (0.0–1.0).\n\nSee [references\u002Fapi-reference.md](references\u002Fapi-reference.md#text-detection) for full schema and callback format.\n\n---\n\n## Recommended Workflows\n\n### Full Media Forensics (Most Thorough)\n\nFor a comprehensive analysis, combine all capabilities:\n\n1. Submit detection with all flags enabled:\n   ```json\n   {\n     \"url\": \"https:\u002F\u002Fexample.com\u002Fsuspect.mp4\",\n     \"visualize\": true,\n     \"intelligence\": true,\n     \"audio_source_tracing\": true,\n     \"use_reverse_search\": true\n   }\n   ```\n2. Poll until `status: \"completed\"`\n3. Read `metrics` \u002F `image_metrics` \u002F `video_metrics` for the verdict\n4. Read `intelligence.description` for structured media analysis\n5. If audio labeled `\"fake\"`, check `audio_source_tracing.label` for the source platform\n6. Ask follow-up questions via Detect Intelligence if anything needs clarification\n7. Check for watermarks via `POST \u002Fwatermark\u002Fdetect` if provenance is relevant\n\n### Quick Authenticity Check (Fastest)\n\n1. Submit minimal detection: `{ \"url\": \"...\" }`\n2. Poll until complete\n3. Check `label` and `aggregated_score` (audio) or `label` and `score` (image\u002Fvideo)\n4. Report result with score context\n\n### Provenance Pipeline (Content Creators)\n\n1. Apply watermark to original content: `POST \u002Fwatermark\u002Fapply`\n2. Distribute watermarked media\n3. Later, verify provenance: `POST \u002Fwatermark\u002Fdetect` against any copy\n\n---\n\n## Red Flags — Stop and Reassess\n\n- **Declaring authenticity without a detection result** — Never say media is real or fake based on visual\u002Fauditory inspection alone\n- **Ignoring the score and reporting only the label** — A `\"fake\"` label with score 0.51 means something very different from score 0.95\n- **Submitting local file paths to the API** — The API requires publicly accessible HTTPS URLs (does not apply to text detection)\n- **Sending text longer than 100,000 characters to text detection** — Split into chunks or inform the user of the limit\n- **Polling too aggressively** — Start at 2s intervals, back off exponentially; do not loop at \u003C1s\n- **Asking Detect Intelligence questions before detection completes** — Results in 422 error\n- **Expecting source tracing on \"real\" audio** — Source tracing only runs on audio labeled `\"fake\"`\n- **Treating beta features (Identity, Text Detection) as production-ready** — Warn users about beta status\n- **Ignoring `zero_retention_mode` for sensitive media** — Always suggest this flag when the user indicates the media is sensitive or private\n- **Making multiple separate API calls when flags can combine** — Use `intelligence: true` and `audio_source_tracing: true` on the detection call instead of separate requests\n\n## Response Presentation Guidelines\n\nWhen presenting results to users:\n\n1. **Lead with the verdict** — \"The detection indicates this audio is likely AI-generated (score: 0.87)\"\n2. **Provide score context** — Use the score interpretation table above\n3. **Mention limitations** — Detection is probabilistic, not absolute proof\n4. **Include actionable next steps** — Suggest intelligence queries, source tracing, or watermark checks as appropriate\n5. **For inconclusive results (0.3–0.5)** — Explicitly state the result is inconclusive and recommend additional analysis with different parameters or manual review\n6. **Never present detection as legal evidence** — Detection results are analytical tools, not forensic certifications\n\n## Error Handling\n\n| Error     | Cause                                      | Resolution                                      |\n|-----------|--------------------------------------------|-------------------------------------------------|\n| 400       | Invalid request body or missing `url`      | Check required parameters                       |\n| 401       | Invalid or missing API key                 | Verify `RESEMBLE_API_KEY`                       |\n| 404       | Detection UUID not found                   | Verify the UUID from the creation response     |\n| 422       | Detection not completed (for Intelligence) | Wait for detection to reach `completed` status |\n| 429       | Rate limited                               | Back off and retry with exponential delay       |\n| 500       | Server error                               | Retry once, then report to user                  |\n\n## Privacy & Compliance Notes\n\n- **Zero retention mode**: Set `zero_retention_mode: true` to auto-delete media after analysis. The URL is redacted and `media_deleted` is set to true post-completion.\n- **Text privacy mode**: Set `privacy_mode: true` on text detection to prevent text content from being stored after analysis.\n- **Data handling**: Media URLs and text content are stored by default. For GDPR\u002Fcompliance-sensitive workflows, enable zero retention (media) or privacy mode (text).\n- **Callback security**: If using `callback_url`, ensure the endpoint is HTTPS and authenticated on the receiving end.\n",{"name":5,"license":15,"description":7,"compatibility":53},"Requires a Resemble AI API key (https:\u002F\u002Fapp.resemble.ai) set as RESEMBLE_API_KEY. All media must be accessible via public HTTPS URLs — local file paths are not supported except for text detection.","master","e3489bc4e504668e9a2ff8fb73dc086da71328ab","01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fresemble-detect","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills","https:\u002F\u002Fgithub.com\u002FTorchiaHub","2026-06-21 20:40:46",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add TorchiaHub\u002FSkills --skill resemble-detect",{"tool":66,"command":67},"bunx","bunx skills add TorchiaHub\u002FSkills --skill resemble-detect",{"tool":69,"command":70},"pnpm","pnpm dlx skills add TorchiaHub\u002FSkills --skill resemble-detect","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fresemble-detect\u002FSKILL.md",true,[74,78],{"path":75,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":16},"LICENSE","许可证",11357,{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fapi-reference.md","REFERENCE","参考文档",10147,"https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fresemble-detect\u002Freferences\u002Fapi-reference.md",[85,117,148,176,200,221],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":98,"publishedAt":48},"01M71VBN3ZPGZT29DK7N7VCT4P","refactor","Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.","MIT","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Frefactor\u002FSKILL.md",[92,95],{"categoryNo":93,"categoryName":94,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":96,"categoryName":97,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000015","视觉设计",[87,99,100,101,102,103,104,105,106,32,107,108,109,110,111,112,113,114,115,116],"design patterns","extracting","improve","design","Variables","code","refactoring","Type Safety","patterns","surgical","code refactoring","down","for","repo","use","type","functions","behavior",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":121,"sourceHtmlUrl":122,"categories":123,"keywords":128,"publishedAt":48},"01M71VA75R4MGJXGR0JS727PBV","quality-playbook","Explore any codebase from scratch and generate six quality artifacts: a quality constitution (QUALITY.md), spec-traced functional tests, a code review protocol with regression test generation, an integration testing protocol, a multi-model spec audit (Council of Three), and an AI bootstrap file (AGENTS.md). Includes state machine completeness analysis and missing safeguard detection. Works with any language (Python, Java, Scala, TypeScript, Go, Rust, etc.). Use this skill whenever the user asks to set up a quality playbook, generate functional tests from specifications, create a quality constitution, build testing protocols, audit code against specs, or establish a repeatable quality system for a project. Also trigger when the user mentions 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', 'coverage theater', or wants to go beyond basic test generation to build a full quality system grounded in their actual codebase.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[124,125],{"categoryNo":93,"categoryName":94,"taxonomyType":22,"taxonomyTypeDesc":23},{"categoryNo":126,"categoryName":127,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000008","测试与质量",[129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,111,147],"playbook","quality playbook","quality","project","code review","integration testing","explore","testing","create","functional","generate","spec audit","typescript","go","state machine","full","multi-model","three","system",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":159,"publishedAt":48},"01M71V7TYJZ1C5XD0RRH2P340K","prd","Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fprd\u002FSKILL.md",[154],{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[150,160,111,161,162,163,164,165,166,167,168,139,169,170,131,171,172,173,174,175],"risk","technical","documents","requirements","analysis","stories","summaries","high","product","specifications","risk analysis","features","systems","software","user stories","Product Requirements",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":48},"01M71V7JFHSJ61847CEY1EM48A","power-platform-architect","Use this skill when the user needs to transform business requirements, use case descriptions, or meeting transcripts into a technical Power Platform solution architecture, including component selection and Mermaid.js diagrams.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fpower-platform-architect\u002FSKILL.md",[],[183,184,185,186,187,188,189,161,113,190,191,163,192,193,194,195,196,197,198,199],"power","platform","power platform","architect","power platform architect","diagrams","selection","business","when","architecture","needs","case","solution architecture","component","solution","mermaid","meeting",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":89,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":206,"publishedAt":48},"01M71V65KR84TMS1WARE3R11VZ","plantuml-ascii","Generate ASCII art diagrams using PlantUML text mode. Use when user asks to create ASCII diagrams, text-based diagrams, terminal-friendly diagrams, or mentions plantuml ascii, text diagram, ascii art diagram. Supports: Converting PlantUML diagrams to ASCII art, Creating sequence diagrams, class diagrams, flowcharts in ASCII format, Generating Unicode-enhanced ASCII art with -utxt flag","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fplantuml-ascii\u002FSKILL.md",[],[207,208,209,188,210,211,212,191,213,34,137,214,139,215,216,217,218,219,220,113],"plantuml","ascii","plantuml ascii","Unicode","mode","Art","Bash","diagram","terminal","read","write","sequence","plantuml diagrams","ascii art",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":227,"publishedAt":48},"01M71V5WJ2Q6SRVV7Q4JXCR8EM","phoenix-tracing","OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fphoenix-tracing\u002FSKILL.md",[],[228,229,230,231,111,232,233,234,235,236,113,191,237,141,238,239,240,241,242,243],"phoenix tracing","tracing","Phoenix","observability","deploying to production","otel","python","implementing","custom","server","conventions","Production","instrumentation","oss","semantic","Arize"]