--- name: bili-mindmap description: Turn a Bilibili video URL or BV number into a human-like XMind mind map. Use when the user wants to collect subtitles, comments, AI summary, and transcript fallback, then generate structured notes or mind maps for a Bilibili video. version: 0.2.1 metadata: clawdbot: emoji: ":brain:" --- # Bili Mindmap Turn a Bilibili video into a mind map that feels closer to something a human actually organized. ## Recommended Flow - Python scripts collect video details, subtitles, AI summary, comments, and ASR fallback when needed. - The host platform's injected model reads the prepared context and writes a high-quality `outline.md`. - Python renders `outline.md` into an `.xmind` file. ## Preconditions - `bili` must be installed and available. - If audio fallback is needed, `bilibili-cli[audio]` should be installed. - If cloud ASR is used on Windows, the Aliyun config file should already exist. - If local ASR is preferred on Linux or macOS, make sure the Parakeet endpoint is running. ## Core Constraints - Prefer subtitles first. Only fall back to ASR when subtitles are unavailable. - Login check is mandatory: run `bili status` before `bili login`. - The main way to produce `outline.md` should be the host model, not the local rule-based script. - The main structure should come from subtitles or ASR. Comments and the site AI summary are supplemental only. - Do not mechanically copy spoken transcript text. Merge themes, compress phrasing, and organize by logic. - If information is weak or incomplete, mark it explicitly instead of inventing facts. ## Main Workflow 1. Accept either a full video URL or a `BV` id. 2. Run `bili status` to check login. 3. If needed, run `bili login` and wait for the user to scan. 4. Run `python scripts/prepare_bili_context.py --source --login-if-needed --transcribe-if-needed`. 5. Read the generated files: `context.md`, `host_outline_prompt.md`, `manifest.json`, `video_details.json`, `subtitles.txt`, `ai_summary.txt`, and `comments.txt`. 6. Feed `host_outline_prompt.md` to the host platform model and let it write `outline.md`. Only use `scripts/generate_outline.py` when the host model path is unavailable. 7. Run `python scripts/render_xmind.py --outline --output `. 8. Tell the user where the `.xmind` file was written and which sources were most important. ## One-Command Workflow `run_bili_mindmap.py` now supports two workflows: - `--workflow host`: recommended quality path. Collects context first, then waits for a host-generated `outline.md`. - `--workflow local`: fallback path. Uses `scripts/generate_outline.py` locally. Recommended command: ```bash python scripts/run_bili_mindmap.py --source "BV1ABcsztEcY" --output-dir output/BV1ABcsztEcY --workflow host --login-if-needed --transcribe-if-needed ``` On the first run, if `outline.md` does not exist yet, the script will stop after context preparation and print: - the `context.md` path - the `host_outline_prompt.md` path - the expected `outline.md` path After the host model writes `outline.md`, run the same command again and it will render the `.xmind` file. ## Fallback Workflow When the host model cannot be used, fall back to the local outline generator: ```bash python scripts/generate_outline.py --context-dir --output ``` This is only a fallback. It is usually lower quality than the host-model result. ## Collection Strategy Collect information in this order: 1. `bili video ` for video details 2. `bili video --subtitle` for subtitles 3. `bili video --ai` for the site AI summary 4. `bili video --comments` for hot comments 5. If subtitles are unavailable: - `bili audio -o ` to extract audio - `auto` mode falls back in `moonshine -> parakeet -> aliyun` order ## Output Requirements - Use the video title as the root topic. - Keep subtitles or ASR as the main evidence. - Prefer abstraction and synthesis over transcript copying. - Mark uncertainty explicitly. - The final artifacts should include both `outline.md` and `.xmind`. ## Important Files - `scripts/prepare_bili_context.py`: login checks, content collection, ASR fallback, and generation of `context.md` plus `host_outline_prompt.md` - `scripts/generate_outline.py`: local fallback outline generator - `scripts/render_xmind.py`: pure Python XMind renderer - `scripts/run_bili_mindmap.py`: one-command entry point with `host` and `local` workflows - `references/mindmap-outline-template.md`: structure template for the final outline - `references/host-llm-outline-spec.md`: quality and behavior rules for the host model path - `vendor/aliyun_asr/`: bundled Aliyun file transcription implementation

bili-mindmap

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