[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73F5C67PBCKHKYFEXVCZQBH":3,"skill-same:01M73F5C67PBCKHKYFEXVCZQBH":48},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":29,"ownerHtmlUrl":30,"ownerBlogUrl":31,"ownerDescription":32,"updatedAt":33,"install":34,"resources":47},"01M73F5C67PBCKHKYFEXVCZQBH","pixelle-video",null,"Use when asked to create or generate a video automatically from a topic, text, or idea. Triggers on: 'tạo video', 'generate video', 'make a video about', 'video từ chủ đề', 'short video', 'create reel', 'pixelle', 'video tự động', 'video AI'. Requires Pixelle-Video server running locally (docker-compose or pip install).","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fpixelle-video\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: pixelle-video\ndescription: \"Use when asked to create or generate a video automatically from a topic, text, or idea. Triggers on: 'tạo video', 'generate video', 'make a video about', 'video từ chủ đề', 'short video', 'create reel', 'pixelle', 'video tự động', 'video AI'. Requires Pixelle-Video server running locally (docker-compose or pip install).\"\n---\n\n# Pixelle-Video Skill\n\nTự động tạo video AI từ một chủ đề hoặc đoạn văn bản — script → ảnh AI → voice → BGM → video hoàn chỉnh.\nDùng [AIDC-AI\u002FPixelle-Video](https:\u002F\u002Fgithub.com\u002FAIDC-AI\u002FPixelle-Video) (REST API, FastAPI, ComfyUI backend).\n\n## Prerequisites\n\nPixelle-Video phải đang chạy trước khi gọi skill này:\n\n```bash\n# Kiểm tra server có đang chạy không\ncurl -s http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fhealth | jq .\n\n# Nếu chưa chạy — khởi động bằng Docker\ndocker compose up -d    # trong thư mục Pixelle-Video\n\n# Hoặc chạy thẳng\npip install -e .\npixelle-video start\n```\n\n## Workflow\n\n### Step 1 — Xác nhận input từ user\n\nThu thập 3 thông tin:\n- **text**: Chủ đề hoặc đoạn văn bản nguồn (vd: \"Atomic Habits dạy chúng ta thay đổi thói quen nhỏ\")\n- **mode**: `generate` (AI tự viết lời) hoặc `fixed` (dùng text nguyên)\n- **n_scenes**: Số cảnh, mặc định 5 (1–20)\n\nNếu user chỉ cung cấp chủ đề ngắn → dùng `mode: generate`.\nNếu user cung cấp script đầy đủ → dùng `mode: fixed`.\n\n### Step 2 — Kiểm tra server health\n\n```bash\ncurl -s http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fhealth\n```\n\nNếu server không phản hồi → báo user khởi động server, dừng.\n\n### Step 3 — Tạo video (async)\n\n```bash\ncurl -s -X POST http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fvideo\u002Fasync \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"text\": \"\u003Cuser_text>\",\n    \"mode\": \"generate\",\n    \"n_scenes\": 5,\n    \"frame_template\": \"1080x1920\u002Fimage_default.html\",\n    \"video_fps\": 30,\n    \"bgm_volume\": 0.3\n  }' | jq .\n```\n\nGhi lại `task_id` từ response.\n\n### Step 4 — Poll task status\n\n```bash\nTASK_ID=\"\u003Ctask_id_from_step3>\"\nwhile true; do\n  STATUS=$(curl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq -r '.status')\n  echo \"Status: $STATUS\"\n  if [[ \"$STATUS\" == \"completed\" || \"$STATUS\" == \"failed\" ]]; then break; fi\n  sleep 5\ndone\ncurl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq .\n```\n\n### Step 5 — Lấy kết quả\n\nKhi status = `completed`, response chứa `video_url`:\n\n```bash\ncurl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq '{\n  video_url: .result.video_url,\n  duration: .result.duration,\n  file_size: .result.file_size\n}'\n```\n\nDownload video:\n```bash\nVIDEO_URL=$(curl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq -r '.result.video_url')\ncurl -L \"$VIDEO_URL\" -o output_video.mp4\necho \"Video saved → output_video.mp4\"\n```\n\n### Step 6 — Báo cáo kết quả\n\nTrả về cho user:\n- Đường dẫn file video đã download\n- Thời lượng (duration) + kích thước file\n- URL nếu user muốn xem trực tiếp qua browser (`http:\u002F\u002Flocalhost:8000` + video_url)\n\n## Options nâng cao\n\n| Param | Mặc định | Mô tả |\n|-------|---------|--------|\n| `frame_template` | `1080x1920\u002Fimage_default.html` | Template kích thước + layout (portrait\u002Flandscape) |\n| `tts_workflow` | default config | Workflow TTS (giọng đọc) |\n| `ref_audio` | none | Audio mẫu để clone giọng |\n| `bgm_path` | none | Đường dẫn file nhạc nền tuỳ chỉnh |\n| `bgm_volume` | `0.3` | Âm lượng nhạc nền (0.0–1.0) |\n| `prompt_prefix` | none | Style prefix cho ảnh AI (vd: \"cinematic, 4K\") |\n| `template_params` | none | Màu sắc, background tuỳ chỉnh cho template |\n\n## Ví dụ nhanh\n\n```bash\n# Video 5 cảnh về thói quen tốt\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fvideo\u002Fasync \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"text\": \"Thói quen nhỏ mỗi ngày tạo nên sự khác biệt lớn trong cuộc đời bạn.\",\n    \"mode\": \"generate\",\n    \"n_scenes\": 5,\n    \"title\": \"Sức mạnh của thói quen nhỏ\"\n  }'\n```\n\n## Xử lý lỗi\n\n| Lỗi | Nguyên nhân | Fix |\n|-----|------------|-----|\n| `Connection refused` | Server chưa chạy | `docker compose up -d` |\n| `task status: failed` | ComfyUI chưa start \u002F thiếu model | Xem log: `docker compose logs` |\n| Video trống \u002F âm thanh không có | TTS workflow chưa config | Kiểm tra `config.yaml` → `tts_workflow` |\n| Ảnh không generate | ComfyUI API key \u002F model chưa cài | Xem README → Image Generation Setup |\n","# Pixelle-Video Skill\n\nTự động tạo video AI từ một chủ đề hoặc đoạn văn bản — script → ảnh AI → voice → BGM → video hoàn chỉnh.\nDùng [AIDC-AI\u002FPixelle-Video](https:\u002F\u002Fgithub.com\u002FAIDC-AI\u002FPixelle-Video) (REST API, FastAPI, ComfyUI backend).\n\n## Prerequisites\n\nPixelle-Video phải đang chạy trước khi gọi skill này:\n\n```bash\n# Kiểm tra server có đang chạy không\ncurl -s http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fhealth | jq .\n\n# Nếu chưa chạy — khởi động bằng Docker\ndocker compose up -d    # trong thư mục Pixelle-Video\n\n# Hoặc chạy thẳng\npip install -e .\npixelle-video start\n```\n\n## Workflow\n\n### Step 1 — Xác nhận input từ user\n\nThu thập 3 thông tin:\n- **text**: Chủ đề hoặc đoạn văn bản nguồn (vd: \"Atomic Habits dạy chúng ta thay đổi thói quen nhỏ\")\n- **mode**: `generate` (AI tự viết lời) hoặc `fixed` (dùng text nguyên)\n- **n_scenes**: Số cảnh, mặc định 5 (1–20)\n\nNếu user chỉ cung cấp chủ đề ngắn → dùng `mode: generate`.\nNếu user cung cấp script đầy đủ → dùng `mode: fixed`.\n\n### Step 2 — Kiểm tra server health\n\n```bash\ncurl -s http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fhealth\n```\n\nNếu server không phản hồi → báo user khởi động server, dừng.\n\n### Step 3 — Tạo video (async)\n\n```bash\ncurl -s -X POST http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fvideo\u002Fasync \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"text\": \"\u003Cuser_text>\",\n    \"mode\": \"generate\",\n    \"n_scenes\": 5,\n    \"frame_template\": \"1080x1920\u002Fimage_default.html\",\n    \"video_fps\": 30,\n    \"bgm_volume\": 0.3\n  }' | jq .\n```\n\nGhi lại `task_id` từ response.\n\n### Step 4 — Poll task status\n\n```bash\nTASK_ID=\"\u003Ctask_id_from_step3>\"\nwhile true; do\n  STATUS=$(curl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq -r '.status')\n  echo \"Status: $STATUS\"\n  if [[ \"$STATUS\" == \"completed\" || \"$STATUS\" == \"failed\" ]]; then break; fi\n  sleep 5\ndone\ncurl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq .\n```\n\n### Step 5 — Lấy kết quả\n\nKhi status = `completed`, response chứa `video_url`:\n\n```bash\ncurl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq '{\n  video_url: .result.video_url,\n  duration: .result.duration,\n  file_size: .result.file_size\n}'\n```\n\nDownload video:\n```bash\nVIDEO_URL=$(curl -s \"http:\u002F\u002Flocalhost:8000\u002Fapi\u002Ftasks\u002F$TASK_ID\" | jq -r '.result.video_url')\ncurl -L \"$VIDEO_URL\" -o output_video.mp4\necho \"Video saved → output_video.mp4\"\n```\n\n### Step 6 — Báo cáo kết quả\n\nTrả về cho user:\n- Đường dẫn file video đã download\n- Thời lượng (duration) + kích thước file\n- URL nếu user muốn xem trực tiếp qua browser (`http:\u002F\u002Flocalhost:8000` + video_url)\n\n## Options nâng cao\n\n| Param | Mặc định | Mô tả |\n|-------|---------|--------|\n| `frame_template` | `1080x1920\u002Fimage_default.html` | Template kích thước + layout (portrait\u002Flandscape) |\n| `tts_workflow` | default config | Workflow TTS (giọng đọc) |\n| `ref_audio` | none | Audio mẫu để clone giọng |\n| `bgm_path` | none | Đường dẫn file nhạc nền tuỳ chỉnh |\n| `bgm_volume` | `0.3` | Âm lượng nhạc nền (0.0–1.0) |\n| `prompt_prefix` | none | Style prefix cho ảnh AI (vd: \"cinematic, 4K\") |\n| `template_params` | none | Màu sắc, background tuỳ chỉnh cho template |\n\n## Ví dụ nhanh\n\n```bash\n# Video 5 cảnh về thói quen tốt\ncurl -X POST http:\u002F\u002Flocalhost:8000\u002Fapi\u002Fvideo\u002Fasync \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"text\": \"Thói quen nhỏ mỗi ngày tạo nên sự khác biệt lớn trong cuộc đời bạn.\",\n    \"mode\": \"generate\",\n    \"n_scenes\": 5,\n    \"title\": \"Sức mạnh của thói quen nhỏ\"\n  }'\n```\n\n## Xử lý lỗi\n\n| Lỗi | Nguyên nhân | Fix |\n|-----|------------|-----|\n| `Connection refused` | Server chưa chạy | `docker compose up -d` |\n| `task status: failed` | ComfyUI chưa start \u002F thiếu model | Xem log: `docker compose logs` |\n| Video trống \u002F âm thanh không có | TTS workflow chưa config | Kiểm tra `config.yaml` → `tts_workflow` |\n| Ảnh không generate | ComfyUI API key \u002F model chưa cài | Xem README → Image Generation Setup |\n",{"name":5,"description":7},"main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fpixelle-video",1,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":35,"prompt":45,"zipAvailable":46},[36,39,42],{"tool":37,"command":38},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill pixelle-video",{"tool":40,"command":41},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill pixelle-video",{"tool":43,"command":44},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill pixelle-video","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fpixelle-video\u002FSKILL.md",true,[],[49,57,64,72,80,88],{"skillNo":50,"name":51,"nameZh":6,"description":52,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":53,"licenseFileUrl":6,"sourceHtmlUrl":54,"categories":55,"keywords":56,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":63,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":68,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":19},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":76,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":19},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":84,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":89,"name":90,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]