---

name: kb description: Manage an Obsidian-based AI knowledge vault with /kb add/find/process-pending/compile/health/tidy/add-video.

/kb - AI Knowledge Vault

Manage the knowledge/ directory in this repository as a reusable AI knowledge vault.

Triggers

• /kb process-pending
• /kb add [content or URL]
• /kb find [query]
• /kb add-video [file-or-directory]
• /kb compile
• /kb health
• /kb tidy

Guardrails

• Do not scan every knowledge/*.md file by default; start from knowledge/_index.md
• Store all knowledge entries under knowledge/
• Keep the original source content in full under ## 原始内容
• Reuse existing tags and concept names when possible
• Prefer local config files for API keys; use environment variables as fallback
• Treat knowledge/concepts/ as the primary navigation layer and knowledge/reports/ as reusable query output
• /kb add and /kb process-pending use the same ingestion action: first produce a pending Markdown source with frontmatter, then create the normalized entry, update the index, and archive the source

Workflows

1) /kb process-pending

Process files in knowledge/inbox/manual/pending/ and turn them into structured entries.

Expected behavior:

1. Read knowledge/_index.md first for existing tags and concepts
2. For each pending Markdown file, extract metadata, summarize 1-3 key points, optionally add 我的思考
3. Write a normalized entry to knowledge/YYYY-MM-DD-title.md
4. Update knowledge/_index.md
5. Move the source file to knowledge/inbox/manual/processed/ or review/ when uncertain

This workflow is handled directly by the agent, not by a Python CLI command.

2) /kb add

Add a single piece of knowledge from text, notes, or a URL.

Expected behavior:

1. Detect source type
2. If the input is a URL, prefer Defuddle CLI to fetch clean Markdown content instead of WebFetch:

defuddle parse "<URL>" --md -o "knowledge/inbox/manual/pending/YYYY-MM-DD-short-title.md"

If title or domain metadata is needed, use:

defuddle parse "<URL>" -p title
defuddle parse "<URL>" -p domain

The pending file must include frontmatter:

• date
• source
• source_type
• source_url
• tags (can start as an empty list and be finalized during ingestion)
• confidence: raw

Keep the full Defuddle Markdown output as the body without truncation. 3. If the input is pasted text or notes, also write it first to knowledge/inbox/manual/pending/YYYY-MM-DD-short-title.md with the same frontmatter and full original body 4. Process the new pending source using the same action as /kb process-pending:

• Read knowledge/_index.md for existing tags, concepts, and recent entries
• Summarize 1-3 key points with the current agent
• Optionally add 我的思考
• Choose tags by reusing existing names when possible
• Choose related concepts from the index concept navigation
• Write a normalized entry to knowledge/YYYY-MM-DD-title.md
• Preserve the pending file body under ## 原始内容
• Update knowledge/_index.md
• Move the pending source to knowledge/inbox/manual/processed/

5. Return the created entry path, tags, related concepts, and suggested links to existing entries

Fallbacks:

• If defuddle is not installed, install it with npm install -g defuddle-cli
• If Defuddle cannot fetch meaningful content, try another reader; if that still fails, keep a URL-only pending source with confidence: raw and move it to knowledge/inbox/manual/review/
• If Defuddle output is too short, mostly navigation, or lacks substantive body content, do not ingest it directly; move it to review/

3) /kb find

Search the knowledge vault and optionally generate a reusable report.

Command:

python3 .claude/skills/kb/scripts/knowledge_ops.py find "context engineering"

Output should prioritize:

• matching concepts
• matching entries
• key takeaways
• suggested follow-up reading

4) /kb add-video

Transcribe local video or audio files, lightly clean the transcript, and create knowledge entries.

Prerequisites:

• pip3 install dashscope
• ffmpeg and ffprobe available on the machine
• Copy .claude/skills/kb/config.example.json to .claude/skills/kb/config.local.json and fill in dashscope_api_key or set DASHSCOPE_API_KEY

Default input directory:

• knowledge/inbox/video/raw/

Command:

python3 .claude/skills/kb/scripts/video_ingest.py [path]

Outputs:

• knowledge/inbox/video/transcripts/*.raw.txt
• knowledge/inbox/video/transcripts/*.clean.txt
• knowledge/inbox/video/transcripts/*.meta.json
• knowledge/inbox/video/logs/ingest.log
• new knowledge entry files under knowledge/

Quality rules:

• Only do light transcript cleanup
• Do not invent facts
• Leave 核心观点 blank for the current agent to fill in later

5) /kb compile

Compile timeline entries into concept pages, related links, index, and Bases view.

Command:

python3 .claude/skills/kb/scripts/knowledge_ops.py compile

6) /kb health

Generate a health report for isolated entries, concept gaps, stale raw files, and report coverage.

Command:

python3 .claude/skills/kb/scripts/knowledge_ops.py health

7) /kb tidy

Normalize tags and rebuild concepts, index, health report, and Bases view.

Command:

python3 .claude/skills/kb/scripts/knowledge_ops.py tidy

Troubleshooting

• If find, compile, or health fail to locate the repo root, set KB_ROOT=/absolute/path/to/repo
• If add-video fails, verify dashscope installation, API key config, and ffmpeg availability