parse

name

parse

description

Use when the user invokes LLM Wiki parse workflow — explicitly via "wiki:parse", "parse this", "parse this folder", "wiki: parse <path|url>", drops a YouTube link, or when a new file is dropped into a wiki's raw/_inbox/. Handles four input kinds: single file, folder (batch), generic URL (defuddle clip), or YouTube URL (yt-dlp transcript + optional whisper). Processes each source into the wiki: creates sources/<slug>.md summary, updates 10-15 entities/concepts with citations, appends a log entry. Recognizes the target wiki by a CLAUDE.md with `type: schema, scope: wiki` frontmatter. Reads that CLAUDE.md for the authoritative ingest protocol before acting.

wiki:parse — ingest a source into the wiki

You are the ingest workflow for an LLM Wiki. The authoritative procedure lives in the target wiki's CLAUDE.md — read it before acting.

Step 1 — Locate the wiki

Find CLAUDE.md with frontmatter type: schema and scope: wiki:

1. Project-local: pwd and walk up — look for wiki/CLAUDE.md
2. Vault root (if Obsidian): $VAULT_ROOT/**/wiki/CLAUDE.md
3. Memory: if exactly one wiki is known from prior memory, use it
4. Multiple candidates: ask the user

Step 2 — Classify the input

Argument can be a file path, folder path, URL, YouTube URL, the literal this, or empty. Detect kind:

Input	How to detect	Action
YouTube URL	matches youtube.com/watch, youtu.be/, youtube.com/shorts/	go to Step 2a
Generic URL	starts with http(s)://, not YouTube	call obsidian:defuddle → raw/anthropic-docs/<slug>.md
Folder	[ -d "$path" ]	go to Step 2b (batch)
File	[ -f "$path" ] and lives under raw/	use as-is. If outside raw/ — ask user where it belongs
this / empty	no arg	pick the most recently modified file in raw/_inbox/; if empty, ask user

Step 2a — YouTube transcript

1. Make target dir: mkdir -p wiki/raw/transcripts
2. Try existing captions first (no transcription needed):

   yt-dlp --skip-download --write-auto-sub --write-sub \
     --sub-lang "en,uk,en-orig" --sub-format vtt \
     -o "wiki/raw/transcripts/%(id)s.%(ext)s" "<URL>"
   

3. If no captions produced (.vtt missing), fall back to whisper:

   yt-dlp -x --audio-format mp3 -o "wiki/raw/transcripts/%(id)s.%(ext)s" "<URL>"
   whisper "wiki/raw/transcripts/<id>.mp3" --model small --output_format txt \
     --output_dir wiki/raw/transcripts
   

4. Convert .vtt/.txt into a clean markdown at wiki/raw/transcripts/<slug>.md with metadata header (video title via yt-dlp --get-title, URL, duration, channel, fetched date)
5. Treat that markdown as the raw source going into Step 3

Step 2b — Folder batch

1. List all .md, .txt, and other text files in the folder (skip binaries)
2. Count N files. Pick mode:
   • N ≤ 5: sequential, full protocol per file (Steps 3-4), pause for user confirmation between files
   • 5 < N ≤ 20: sequential, full protocol per file, no inter-file pauses; rollup log entry at end
   • N > 20: parallel-split mode (Step 2c)
3. If the folder is raw/_inbox/: after Step 4 succeeds for each file, move the source out of _inbox/ into its permanent home:
   • .url / .txt containing a URL → already processed via defuddle/yt-dlp, original moves to raw/_inbox/_processed/<filename>
   • YouTube source → raw/transcripts/<id>.md (already there from Step 2a)
   • Generic article (defuddle output) → already in raw/anthropic-docs/ (from Step 2a)
   • Plain markdown/text dropped manually → raw/articles/<filename> (or raw/_inbox/_processed/ if unclear)
   • This guarantees idempotency: cron re-runs see an empty inbox and exit immediately

Step 2c — Parallel-split mode (large batches)

Use this when N > 20. Splits work between subagents to avoid sequential bottleneck and write conflicts.

Phase A — sources/ (parallel, idempotent writes):

1. Group files into chunks of ~10. Dispatch one Agent (subagent_type: general-purpose) per chunk in a single message with multiple Agent tool calls (true parallel)
2. Each agent's prompt: "Read each raw file in this list. For each, write sources/<slug>.md in with frontmatter (type: source, raw: [[...]], url, fetched, tags, confidence) and sections Summary / Key claims / Quotes / Backlinks. Do NOT touch entities/ or concepts/ — that's a later pass. Return list of slugs created."
3. Wait for all agents to finish

Phase B — entities/concepts sweep (single thread, after Phase A):

4. Read all newly created sources/*.md
5. Identify recurring topics → cluster into existing or new entities/concepts
6. For each touched entity: append claim with [[sources/<slug>]] citation, refresh last_updated
7. Create new entities/concepts where needed (10-30 typical for 80+ sources)

Phase C — finalize:

8. Single rollup log entry summarizing the batch
9. qmd update && qmd embed

Step 3 — Read wiki/CLAUDE.md

Read the schema in full. Do not paraphrase or shortcut steps.

Step 4 — Execute the ingest workflow (per source)

Per wiki/CLAUDE.md:

1. Read the raw file completely

2. Discuss takeaways with the user (1-2 sentences each) before writing — in batch mode, summarize per file but skip the back-and-forth

3. Create sources/<slug>.md with frontmatter (type: source, raw: [[...]], url, fetched: YYYY-MM-DD, tags, confidence) and sections: Summary / Key claims / Quotes / Backlinks. For YouTube sources add video_id, channel, duration fields

4. Touch 10-15 entities/ and concepts/: add new claims with citation [[sources/<slug>]], refresh last_updated. Create new entities/concepts if warranted

5. Sync index.md if it contains manual sections (Dataview blocks auto-refresh)

6. Append log.md:

   ## [YYYY-MM-DD] ingest | <Source title>
   
   - bullet 1 — what changed
   - bullet 2
   - bullet 3
   

   For batch ingests: one log entry per source, OR one rollup entry with sub-bullets listing each source — your choice based on scale.

Step 5 — Reindex qmd

After all sources written:

qmd update && qmd embed

Mention this in the final summary so the user knows the search index is fresh.

Hard rules

• Never write into raw/ after Step 2 — read-only from Step 3 onward
• Every claim in entities/concepts must cite a source; otherwise confidence: low + matching questions/<topic>.md
• Ingest must produce a log entry — no exceptions
• For YouTube: prefer existing captions over whisper transcription (faster, accurate, no GPU needed). Only fall back to whisper if captions truly absent
• In folder batch mode: stop at 5 files and check in with the user. Don't run autonomously on 100+ files
• Record the canonical URL in url: frontmatter for any web/YouTube source