bangumi-frames
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 — 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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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/agents365-ai/365-skills --skill bangumi-frames- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads bangumi-frames automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
bangumi-frames — 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 → extracts scene-change keyframes → splits scenery vs
character frames → organizes the character crops. One pass, two modes:
- no
--ref(cluster mode) — group every character crop by CCIP identity intocharacters/char_NN/. - with
--ref DIR(one-vs-rest mode) — given ONE character's reference folder, pull every crop in the video that matches it intomatched/, 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) — 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 — 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 — 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 — 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
ffmpegon PATH;yt-dlpon PATH for downloads (a local-file input skips download).- Python 3.9+,
pip install dghs-imgutils(first run pulls ~300 MB of models from HuggingFace, then cached locally). - 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. - Run the CCIP step on CPU — do not set
ONNX_MODE=CoreML(CCIP crashes; the script pops it before clustering/matching). Person detection is fine on CoreML. - (Only for
--clean)pip install rapidocr-onnxruntime simple-lama-inpainting. - (Only for
--engine pyscenedetect)pip install scenedetect.
Usage
SKILL=skills/bangumi-frames/scripts/bangumi_frames.py
# Mode 1 — 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 — pull out ONE character (ref folder = ~200 crops of that character)
python3 $SKILL BV15qVm68E2h --ref ~/refs/紫灵 --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 — JSON when piped, a human summary on a TTY;
force with --format), stderr carries human progress logs, and exit codes are stable
(0 ok · 1 runtime · 2 auth · 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
├── frames/ frames.json # keyframes + timestamps
├── scenery/ # frames with no detected character
├── crops/ features.npy # character crops + cached CCIP features
├── detect.json # frame -> person boxes / crops
├── characters/ # MODE 1: char_NN_crop/ + char_NN_full/ (paired), _unsorted/, _montage.png
├── matched/ # MODE 2: 0.012_<crop>.jpg (distance-prefixed) + index.json
├── 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 — 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.
FAQ
What does the bangumi-frames skill do?
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 — 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.
How do I install bangumi-frames?
Run npx skills add https://github.com/agents365-ai/365-skills --skill bangumi-frames in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/bangumi-frames/ (personal) or .claude/skills/bangumi-frames/ (project).
Which AI agents can use bangumi-frames?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is bangumi-frames safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains bangumi-frames?
It is published by agents365-ai in agents365-ai/365-skills. It has 2,287 recorded installs.
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