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/bangumi-frames

@cc9ff4c
by Agents365.aiagents365-ai/365-skills57 stars
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Extract and organize frames from a Bilibili video (bangumi episode, UP upload, or a local file) into scenery shots and per-character image groups, using anime-specific person detection + CCIP character-identity embeddings. Two modes β€” cluster everyone, or pull out one (or several) named characters via reference folders. Use when the user wants to collect, extract, or organize anime frames/screenshots by character or by scenery from a Bilibili video. Read-only download for personal viewing/analysis; uploads nothing.

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Use this Skill: https://skilld.dev/gh/agents365-ai/365-skills/bangumi-frames

This session only. Nothing lands on disk.

SKILL.md

β‰ˆ135 tokens always: the name and description. β‰ˆ1.4k when used: this file. β‰ˆ2.9k more on demand in 2 files.

bangumi-frames β€” Bilibili Anime Frame & Character Organizer

Overview

Give a Bilibili video (a bangumi ep link, a UP-upload BV link/id, or a local video file); it downloads β†’ extracts scene-change keyframes β†’ splits scenery vs character frames β†’ organizes the character crops. One pass, two modes:

  • no --ref (cluster mode) β€” group every character crop by CCIP identity into characters/char_NN/.
  • with --ref DIR (one-vs-rest mode) β€” given ONE character's reference folder, pull every crop in the video that matches it into matched/, filenames prefixed with distance (closest first) so a tight threshold yields a pure set.

Models are anime-specific (deepghs anime person detection + CCIP character-identity embeddings) β€” they do not work on live-action footage.

When to use / when NOT to use

  • Use when the user wants to collect/extract/organize anime frames or screenshots from a Bilibili video β€” by character, by scenery, or to pull out one specific person.
  • Don't use for live-action video (needs an insightface-class face stack instead), or for generic video editing/trimming/transcoding.

Bundled resources

Resource Read it when
references/pipeline.md Tuning a stage β€” download (--height/--prefer), extract (--scene/--interval/--dedup/--skip), --clean (OCR+LaMa subtitle/watermark removal), classify (--conf/--min-area); feature caching; the CPU/CoreML rule; --redo
references/modes.md Choosing/tuning the two modes β€” mode 1 cluster (--eps/--min-samples) vs mode 2 one-vs-rest (--ref-eps, the distance-band histogram, the compressed-embedding threshold lore); full output layout
scripts/bangumi_frames.py The entry point (all stages + both modes)
scripts/remove_overlay.py Standalone subtitle/watermark removal on a frame dir or single image

Prerequisites

  1. ffmpeg on PATH; yt-dlp on PATH for downloads (a local-file input skips download).
  2. Python 3.9+, pip install dghs-imgutils (first run pulls ~300 MB of models from HuggingFace, then cached locally).
  3. A Bilibili cookie (Netscape cookies.txt). Resolution order: --cookies > $BILIBILI_COOKIES > ~/bb_up/bb_cookies/www.bilibili.com_cookies.txt. 1080p+ / premium episodes need a cookie with membership; a preview-only download means the cookie lacks access to that episode. Local-file input needs no cookie.
  4. Run the CCIP step on CPU β€” do not set ONNX_MODE=CoreML (CCIP crashes; the script pops it before clustering/matching). Person detection is fine on CoreML.
  5. (Only for --clean) pip install rapidocr-onnxruntime simple-lama-inpainting.
  6. (Only for --engine pyscenedetect) pip install scenedetect.

Usage

SKILL=skills/bangumi-frames/scripts/bangumi_frames.py

# Mode 1 β€” cluster everyone into char_NN groups
python3 $SKILL https://www.bilibili.com/video/BV15qVm68E2h --out ~/frames
python3 $SKILL ep1231575 --out ~/frames           # ep / BV id also accepted
python3 $SKILL ~/local.mp4 --out ~/frames          # local file, skips download

# Mode 2 β€” pull out ONE character (ref folder = ~200 crops of that character)
python3 $SKILL BV15qVm68E2h --ref ~/refs/紫灡 --ref-eps 0.04 --out ~/frames

# Optional: strip burned-in subtitles + watermark before analysis
python3 $SKILL ep1231575 --clean --out ~/frames

Stages are idempotent (a stage is skipped when its output already exists; clustering / matching always re-runs since the CCIP features are cached). For every flag, the per-stage trade-offs, and the threshold lore, read the two reference files above.

Agent-native output: stdout is a single JSON envelope ({"ok", "data", "next", "meta"} on success, {"ok": false, "error"} on failure β€” JSON when piped, a human summary on a TTY; force with --format), stderr carries human progress logs, and exit codes are stable (0 ok Β· 1 runtime Β· 2 auth Β· 3 validation). Use --dry-run to preview the plan without downloading, --schema to print the output contract. Details in references/pipeline.md.

Output

<out>/<id>/                     # id = BV id / ep id / local filename
β”œβ”€β”€ frames/  frames.json        # keyframes + timestamps
β”œβ”€β”€ scenery/                    # frames with no detected character
β”œβ”€β”€ crops/  features.npy        # character crops + cached CCIP features
β”œβ”€β”€ detect.json                 # frame -> person boxes / crops
β”œβ”€β”€ characters/                 # MODE 1: char_NN_crop/ + char_NN_full/ (paired), _unsorted/, _montage.png
β”œβ”€β”€ matched/                    # MODE 2: 0.012_<crop>.jpg (distance-prefixed) + index.json
β”œβ”€β”€ matched_montage.png         # MODE 2 sample montage
└── index.json                  # MODE 1: char group -> {crop, frame, time}

After a run, look at characters/_montage.png (mode 1) or matched_montage.png (mode 2) first to judge quality, then read index.json. See references/modes.md for what to adjust when grouping/matching is off.

Limits

  • Anime / 2.5D-render art only; live-action needs a different (face-recognition) stack.
  • CCIP may split one character's different forms (outfit / transform) into separate groups β€” usually fine for "group by visual appearance"; for mode 2, put each form in the ref.
  • 1080p+ on Bilibili needs a membership cookie; download is for personal offline analysis only and uploads nothing.

Source: SKILL.md on GitHub

Third-party checks

No alertstoday3 checks Β· Risk SAFE
  • Gen Agent Trust Hubtoday

    The skill is a specialized media processing tool for Bilibili content that utilizes standard libraries and CLI tools. It includes functionality for downloading videos, extracting frames, and organizing character crops using anime-specific AI models. Security analysis identified typical behaviors for this use case, including local credential file access for authentication and execution of external binaries, handled with safe practices.

  • Sockettoday

    No alerts

  • Snyktoday

    Risk: LOW Β· No issues

Provenance

Signed by skilld at cc9ff4c. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 13 hours ago.

Activeupdated 3 weeks ago

Capability

version
0.5.0
platforms
[
  "macos",
  "linux"
]
Other metadata
homepage
https://github.com/Agents365-ai/365-skills/tree/main/plugins/bangumi-frames/skills/bangumi-frames
compatibility
Requires ffmpeg and (for downloads) yt-dlp on PATH; Python 3.9+ with dghs-imgutils (anime person detection + CCIP). The CCIP step runs on CPU β€” do NOT set ONNX_MODE=CoreML (CCIP crashes there); person detection is fine on CoreML. Anime / 2.5D-render art only β€” not for live-action. Optional --clean (subtitle/watermark removal) needs rapidocr-onnxruntime + simple-lama-inpainting.
metadata
{
  "openclaw": {
    "requires": {
      "anyBins": [
        "ffmpeg"
      ]
    },
    "emoji": "🎬",
    "os": [
      "darwin",
      "linux"
    ],
    "install": [
      {
        "id": "brew-ffmpeg",
        "kind": "brew",
        "formula": "ffmpeg",
        "bins": [
          "ffmpeg"
        ],
        "label": "Install ffmpeg via Homebrew",
        "os": [
          "darwin"
        ]
      },
      {
        "id": "pip-yt-dlp",
        "kind": "pip",
        "package": "yt-dlp",
        "bins": [
          "yt-dlp"
        ],
        "label": "Install yt-dlp (download Bilibili streams)"
      },
      {
        "id": "pip-imgutils",
        "kind": "pip",
        "package": "dghs-imgutils",
        "label": "Install anime person detection + CCIP models"
      }
    ]
  },
  "hermes": {
    "tags": [
      "bilibili",
      "anime",
      "bangumi",
      "frames",
      "character",
      "ccip",
      "screenshot",
      "video"
    ],
    "category": "media",
    "requires_tools": [
      "ffmpeg",
      "yt-dlp"
    ],
    "related_skills": []
  },
  "author": "Agents365-ai",
  "version": "0.5.0"
}

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